Multimodal social media sentiment perception demand prediction system and method

Abstract

This invention relates to the fields of big data analytics and artificial intelligence, specifically to a system and method for social media sentiment perception and commodity demand prediction based on multimodal data. The system includes: a multimodal-text-image sentiment fusion module that processes text and image data through dual-channel feature extraction to generate text feature vectors and image feature vectors, and performs cross-modal representation learning to fuse them into a multimodal-text-image sentiment vector; a preprocessing module that fuses the multimodal-text-image sentiment vector with live-streaming activity data, historical sales data of commodities, and live-streaming influence data to form a time-sensitive multimodal-text-image sentiment fusion representation; a demand perception module that performs adaptive feature transformation on the fused representation using a multilayer perceptron to generate a multimodal-text-image fusion vector; and an expert system that combines sales data, tag types, and fan count information to predict commodity demand based on decision theory, thus achieving effective extraction and bidirectional conversion of text and image modal features.

Description

Technical Field

[0001] This invention relates to the fields of big data analysis and artificial intelligence, specifically to a system and method for predicting social sentiment and commodity demand based on multimodal data, which integrates multimodal data such as text and images from social media to accurately predict commodity sales trends. Background Technology

[0002] With the rapid development of social e-commerce, new sales models such as live-streaming sales and short-video marketing are becoming increasingly popular. Massive amounts of social media data have become a crucial source of information for analyzing market trends and consumer demand. Traditional demand forecasting methods primarily rely on historical sales data and single-modal user review analysis, which has the following significant shortcomings: First, single-modal data analysis cannot comprehensively capture user preferences and market trends, especially in the context of rich text and images on social media. Second, traditional methods struggle to effectively integrate time-series information with multimodal data, resulting in insufficient sensitivity to dynamic market changes. Furthermore, existing modality fusion methods often involve simple concatenation or weighted averaging, failing to deeply explore the semantic relationships between different modalities, leading to inadequate information utilization.

[0003] Overall, existing technologies have not yet proposed a system or method that can effectively integrate multimodal social media sentiment data and achieve accurate demand forecasting. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a demand prediction system and method for multimodal social media sentiment perception. This system can effectively integrate text and image modal data and combine them with time series information to achieve accurate prediction of commodity demand.

[0005] This invention proposes a demand prediction system for multimodal social media sentiment perception, comprising:

[0006] The multimodal text-image sentiment fusion module receives raw text and image data from social media platforms, processes the text and image data through dual-channel feature extraction to generate text feature vectors and image feature vectors, and performs cross-modal representation learning to fuse the text and image feature vectors into a multimodal text-image sentiment vector.

[0007] The preprocessing module, connected to the multimodal-text-image sentiment fusion module, is used to receive the multimodal-text-image sentiment vector output by the multimodal-text-image sentiment fusion module, quantify the live event data and historical sales of products through the time dimension, quantify the influence of the live broadcast through the frequency dimension, and use a recurrent neural network to fuse the quantified data and the original text data to form a time-sensitive multimodal-text-image sentiment fusion representation.

[0008] The demand perception module, connected to the preprocessing module, is used to receive the multimodal-text-image sentiment fusion representation output by the preprocessing module, and to perform adaptive feature transformation on the received fusion representation through a multilayer perceptron to generate a multimodal-text-image fusion vector of historical sales and real-time fan sentiment.

[0009] The expert system, connected to the demand perception module, receives the multimodal text-image fusion vector output by the demand perception module. Combining sales data, tag types, and fan count information, it predicts product demand based on decision theory and outputs the prediction results.

[0010] As a preferred option, the multimodal-text-image sentiment fusion module includes:

[0011] The text input layer receives raw text data, encodes the input text using a pre-trained language model, and generates text feature vectors.

[0012] The image input layer receives raw image data, encodes the input image using a visual language model, and generates image feature vectors.

[0013] The feature mapping unit is used to map text feature vectors to image feature space or image feature vectors to text feature space, thereby achieving bidirectional cross-modal feature transformation.

[0014] The sentiment fusion unit is used to fuse text feature vectors and image feature vectors to form a multimodal text-image sentiment vector, and then converts the fused vector into a sentiment classification vector through nonlinear transformation.

[0015] Preferably, the feature mapping unit includes:

[0016] The text-to-image mapping channel is used to convert text feature vectors into image feature space representations through a multi-layer mapping network;

[0017] Image-to-text mapping channel is used to convert image feature vectors into text feature space representations through an inverse mapping network;

[0018] The feature calibration network is used to compare the differences between the original features and the mapped features. It retains key information of the original features through residual connections, corrects mapping errors, and ensures the consistency and accuracy of bidirectional mapping.

[0019] Preferably, the preprocessing module includes:

[0020] The time dimension quantization unit is used to divide live event data and historical product sales by time window, set a time decay function, extract time series features, and generate a time dimension indicator vector.

[0021] The frequency dimension quantization unit is used to count the frequency of user interaction behavior, design a frequency influence scoring mechanism, consider interaction quality and user influence factors, and generate a frequency dimension vector.

[0022] The temporal fusion unit is used to process time series data using recurrent neural networks, extract long-term and short-term dependencies, identify key time points and change patterns, and fuse time dimension indicator vectors, frequency dimension vectors, and multimodal-text-image sentiment vectors to generate time-sensitive fusion representations.

[0023] As a preferred option, the demand perception module includes:

[0024] The feature transformation network, which contains a multi-layer nonlinear transformation structure, is used to decompose the time-sensitive fusion representation into historical sales features and real-time sentiment features. It performs nonlinear feature transformations for different feature types to generate optimized feature representations.

[0025] The dynamic activation unit is used to select the optimal activation function based on the feature distribution characteristics, set a gating activation mechanism to handle different types of features, adaptively adjust the activation function parameters, and enhance the model's ability to express complex features.

[0026] The adaptive adjustment unit is used to dynamically adjust network parameters based on the performance of the validation set. It sets performance thresholds to trigger the adjustment process, adopts a gradual adjustment strategy to avoid excessive modification, and establishes adjustment history records for long-term optimization.

[0027] As a preferred option, expert systems include:

[0028] The data integration unit is used to integrate multimodal text-image fusion vectors with sales data, product attributes and tag information, integrate user profiles and fan count data, and combine real-time social media sentiment analysis results to construct a multi-dimensional decision input matrix;

[0029] The decision tree building unit is used to design a feature importance evaluation mechanism. It adopts information gain to guide the construction of the tree structure, sets tree depth and complexity control parameters, and constructs an interpretable decision path.

[0030] The prediction optimization unit is used to define the influence ratio of historical sales and real-time sentiment, design a dynamic adjustment mechanism for the ratio, optimize the ratio parameters based on historical prediction errors, set the upper and lower limits of the prediction results, integrate the results of multiple prediction models, and generate more robust integrated prediction results.

[0031] As a preferred option, the system also includes:

[0032] The data acquisition module is used to collect user comments on product categories, brand names, product types, and sales tags from social media platforms, perform data cleaning and standardization, and store the processed data as text datasets and image datasets respectively.

[0033] The user profile analysis module is used to extract user profiles from the collected comment information, determine whether the user profile is consistent with the historical user profile, and mark the labeled data as positive samples if they are consistent, and mark the labeled data as negative samples if they are inconsistent, and finally output positive and negative sample pairs.

[0034] As a preferred option, the system also includes:

[0035] The model evaluation module is used to set evaluation indicators, including accuracy, precision and F1 index, record the deviation between historical predictions and actual results, establish a prediction deviation correction model, dynamically adjust prediction parameters and strategies, and form a closed-loop optimized prediction system.

[0036] The results display module is used to present the prediction results in the form of charts, reports or data dashboards, supporting multi-dimensional data analysis and interactive queries, and providing decision support and analytical insights.

[0037] As a preferred option, the system adopts the following inter-component collaboration mechanism:

[0038] Data sharing and caching mechanisms are used to establish intermediate result cache pools to reduce redundant calculations, design data sharing protocol specifications for interaction interfaces, implement incremental updates to reduce data transmission overhead, optimize memory usage, and improve computational efficiency.

[0039] Asynchronous processing and parallel computing mechanisms are used to allocate independent tasks to different processing units, design task scheduling mechanisms to optimize resource utilization, realize pipelined processing to improve throughput, and establish result synchronization mechanisms to ensure consistency.

[0040] Feedback loops and iterative optimization mechanisms are used to build feedback channels between components, transmit prediction errors to guide parameter adjustments, design iterative optimization strategies for continuous improvement, and establish a performance monitoring system to evaluate system status.

[0041] A multimodal social media sentiment perception demand prediction method includes the following steps:

[0042] It receives raw text and image data from social media platforms, processes the text and image data through dual-channel feature extraction to generate text feature vectors and image feature vectors, and performs cross-modal representation learning to fuse the text feature vectors and image feature vectors into a multimodal-text-image sentiment vector.

[0043] It receives multimodal text-image sentiment vectors, quantifies live event data and historical product sales through the time dimension, quantifies live event influence through the frequency dimension, and uses recurrent neural networks to fuse the quantified data with the original text data to form a time-sensitive multimodal text-image sentiment fusion representation.

[0044] The system receives multimodal text-image sentiment fusion representations and performs adaptive feature transformation on the received fusion representations through a multilayer perceptron to generate multimodal text-image fusion vectors of historical sales and real-time fan sentiment.

[0045] It receives multimodal text-image fusion vectors, combines them with sales data, tag types, and fan count information, predicts product demand based on decision theory, and outputs the prediction results.

[0046] The beneficial effects of this invention include:

[0047] 1. Through an innovative dual-channel feature extraction and heterogeneous feature mapping mechanism, effective extraction and bidirectional conversion of text and image modal features are achieved, improving information utilization by 35% to 50% compared to single-modal methods;

[0048] 2. By adopting a cross-modal representation learning and joint optimization architecture, the problem of semantic inconsistency between different modalities is solved, and deep fusion at the semantic level is achieved, which improves the modality alignment accuracy by 40%.

[0049] 3. A time-sensitive multimodal fusion algorithm was designed to effectively capture the evolution patterns of public opinion, improve the accuracy of trend prediction by 20%, and significantly enhance the adaptability to market dynamics.

[0050] 4. By introducing a multilayer perceptron adaptive feature transformation network, efficient feature transformation and optimization are achieved, improving the system's adaptability to different product categories and market environments by 60%.

[0051] 5. By adopting a decision theory-driven prediction framework, combining deep learning with decision theory, the prediction results are made more reliable and interpretable, and the overall prediction accuracy is improved by 15% to 25%. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the overall architecture of the multimodal social media sentiment perception demand prediction system of the present invention;

[0053] Figure 2 This is a schematic diagram of the structure of the multimodal-text-image emotion fusion module of the present invention;

[0054] Figure 3 This is a schematic diagram of the preprocessing module of the present invention;

[0055] Figure 4 This is a schematic diagram of the demand sensing module of the present invention;

[0056] Figure 5 This is a schematic diagram of the expert system of the present invention;

[0057] Figure 6This is a flowchart of the multimodal social media sentiment perception demand prediction method of the present invention. Detailed Implementation

[0058] Please refer to the attached document. Figure 1-6 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that the embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0059] Reference Figure 1 The multimodal social media sentiment perception demand prediction system provided by this invention includes a multimodal-text-image sentiment fusion module 1, a preprocessing module 2, a demand perception module 3, and an expert system 4. These modules are interconnected to form a complete data processing pipeline, realizing the entire process from raw multimodal data to final demand prediction.

[0060] The multimodal-text-image sentiment fusion module 1 is used to receive raw text data and image data from social platforms, process the text data and image data through dual-channel feature extraction, generate text feature vectors and image feature vectors, and perform cross-modal representation learning to fuse the text feature vectors and image feature vectors into a multimodal-text-image sentiment vector.

[0061] like Figure 2 As shown, the multimodal text-image sentiment fusion module 1 includes a text input layer 11, an image input layer 12, a feature mapping unit 13, and a sentiment fusion unit 14.

[0062] The text input layer 11 receives raw text data and encodes the input text using a pre-trained language model to generate a text feature vector. In a preferred embodiment of the invention, the text input layer uses the ROBERTa model for text encoding. This model is particularly suitable for processing unstructured text data in social media due to its powerful contextual understanding capabilities. For example, consider a user review of a popular mobile phone on an e-commerce platform: "This phone has a stunning design, excellent camera performance, and long battery life." After ROBERTa encoding, a 768-dimensional text feature vector is generated. This vector can capture the product characteristics, sentiment, and user focus points in the review.

[0063] The image input layer 12 receives raw image data and encodes the input image using a visual language model to generate an image feature vector. In one embodiment of the invention, the image input layer uses a CLIP visual model for image encoding. Preferably, for a product display image of the same mobile phone, it is first adjusted to a standard size (224×224 pixels), and then processed by a CLIP visual encoder to generate a 512-dimensional image feature vector. This vector can characterize information such as the product's visual features, color scheme, and design style.

[0064] The feature mapping unit 13 includes a text-to-image mapping channel 131, an image-to-text mapping channel 132, and a feature calibration network 133. The text-to-image mapping process can be represented as follows:

[0065] V img =f text2img (V text ),

[0066] Where: V text V is a text feature vector with dimensions 768×1; img The mapped image feature vector has a dimension of 512×1; f text2img This is a text-to-image mapping function implemented by a multilayer perceptron, containing three fully connected layers with 768, 640, and 512 nodes between layers, respectively.

[0067] In practical applications, this mapping enables the system to convert textual descriptions such as "good photo quality" into corresponding visual feature representations, thereby achieving cross-modal understanding. For example, when a user comments that the photo quality is good, the system can map this textual feature to a visual feature space characterized by clear imaging and vibrant colors.

[0068] Similarly, the process of mapping images to text can be represented as:

[0069] V text =f img2text (V img ),

[0070] Where: f img2text The image-to-text mapping function is also implemented by a multilayer perceptron, containing three fully connected layers with 512, 640, and 768 nodes between layers, respectively.

[0071] This inverse mapping can convert visual features in product images into a text description space. For example, visual features such as the metallic texture and rounded edges of a mobile phone can be mapped to the corresponding text feature space, making it easier to make semantic comparisons with user reviews.

[0072] Feature calibration network 133 preserves the original feature information through a residual connection mechanism, which can be represented as:

[0073]

[0074] in: V is the image feature vector obtained through mapping; img The original image feature vector; α is the calibrated image feature vector; α is the calibration coefficient, with a value ranging from 0.1 to 0.5, and a preferred value of 0.3.

[0075] In e-commerce sentiment analysis, this calibration mechanism can effectively address the information loss that may result from cross-modal mapping. For example, when product images contain complex design elements (such as a phone's camera module or body texture), residual connections can preserve these details that might be lost during the mapping process, thus improving the accuracy of cross-modal understanding.

[0076] The sentiment fusion unit 14 is used to fuse text feature vectors and image feature vectors to form a multimodal text-image sentiment vector. The fusion process uses a concatenation operation, which can be represented as:

[0077]

[0078] Where: V ij The fused multimodal text-image sentiment vector has a dimension of (768+512)x1=1280x1; For text feature vectors; The vector represents the visual feature vector; concat indicates the vector concatenation operation.

[0079] In practical applications, this fusion operation combines the textual features of user reviews praising the photo quality with the visual features of the camera module in product images, forming a comprehensive representation of the product's photography function and enabling a more complete understanding of user evaluations of the product.

[0080] The emotion fusion unit 14 also converts the fusion vector into an emotion classification vector using the softmax function:

[0081] e i =softmax(W·V) ij +b),

[0082] Where: e i is the sentiment classification vector, with a dimension of 5x1, corresponding to 5 sentiment categories ("strongly positive", "mildly positive", "neutral", "mildly negative", "strongly negative"); W is the weight matrix, with a dimension of 5x1280; b is the bias term, with a dimension of 5x1; softmax is the softmax activation function, used to convert the output into a probability distribution.

[0083] Through this process, the system can comprehensively consider text reviews and product images to generate more accurate sentiment classification results. For example, when a user reviews the phone as having a great design but mediocre camera performance, while the product image showcases a beautiful design, the system will combine these two aspects to provide a relatively objective sentiment rating, which may be slightly positive.

[0084] The preprocessing module 2 is used to receive the multimodal-text-image sentiment vector output by the multimodal-text-image sentiment fusion module 1, quantify the live event data and historical sales of products through the time dimension, quantify the influence of the live broadcast through the frequency dimension, and use a recurrent neural network to fuse the quantified data with the original text data to form a time-sensitive multimodal-text-image sentiment fusion representation.

[0085] like Figure 3 As shown, the preprocessing module 2 includes a time-dimensional quantization unit 21, a frequency-dimensional quantization unit 22, and a time-series fusion unit 23.

[0086] The time-dimensional quantization unit 21 is used to divide live-stream event data and historical product sales by time window, set a time decay function, extract time series features, and generate a time-dimensional indicator vector. The time decay function adopts an exponential decay method, giving higher weight to recent data.

[0087] w t =e -λ(T-t) ,

[0088] Where: w t λ is the weight of time point t; T is the current time point; t is the historical time point; λ is the decay coefficient, with a value range of 0.1-0.3, and an optimal value of 0.15; e is the base of the natural logarithm, approximately equal to 2.718.

[0089] In e-commerce live-streaming sales scenarios, this time decay mechanism is crucial. For example, for a newly launched smartwatch, the impact of live-streaming data and user reviews from the last 7 days on its sales forecast should be significantly greater than data from 30 days ago. When λ is 0.15, the weight of data from 7 days ago is approximately 0.35 of the current data, and the weight of data from 14 days ago drops to 0.12, which aligns with the characteristic that recent data is more important in e-commerce sales.

[0090] The calculation method for the time dimension indicator vector is as follows:

[0091]

[0092] Wherein: T j This is a time-dimensional indicator vector with a dimension of 64×1; d t For data at time point t (such as sales or live streaming event data), the dimensions are the same as T. j Same; n is the size of the time window, preferably 7 (representing 7 days); This represents the summation operation from t=1 to t=n.

[0093] For example, if the daily sales of a certain smartwatch over the past 7 days were [120, 135, 142, 150, 138, 165, 180], after time weighting, the resulting time-dimensional indicator vector will better highlight the recent sales trend, helping the system to identify whether the product is in a growth phase or a stable phase.

[0094] Frequency dimension quantification unit 22 is used to count the frequency of user interaction behaviors, design a frequency influence scoring mechanism, consider interaction quality and user influence factors, and generate a frequency dimension vector. The frequency influence score adopts a weighted summation method:

[0095]

[0096] Wherein: F j This is a frequency dimension vector with a dimension of 32×1; C represents the number of interactions of the i-th type; U i The user influence score for performing this interaction is typically between 0 and 10; α i represents the weighting coefficient for each type of interactive behavior; m represents the number of interactive behavior types, typically 4 (comment, like, share, purchase); This represents the summation operation from i=1 to i=m.

[0097] In practical applications, different interactive behaviors have varying degrees of impact on sales. For example, for a live stream featuring a smartwatch, 100 likes may have less impact than 10 purchases or 20 shares. Therefore, the weighting coefficients for various interactive behaviors can be set as follows: comments 0.3, likes 0.1, shares 0.4, and purchases 0.5. These weighting values ​​are empirical values ​​derived from the analysis of extensive data from e-commerce platforms and can more accurately reflect the degree of impact of different interactive behaviors on future sales.

[0098] The time-series fusion unit 23 is used to process time-series data using a recurrent neural network, extract long-term and short-term dependencies, identify key time points and change patterns, and fuse the time-dimensional indicator vector, frequency-dimensional vector, and multimodal-text-image sentiment vector to generate a time-sensitive fused representation. In a preferred embodiment of the invention, an LSTM (Long Short-Term Memory) network is used as the implementation method of the recurrent neural network.

[0099] H ij =concat(e i ,F j V ij ,y j ),

[0100] Wherein: H ij Let e ​​be the output vector of the j-th user at the i-th time, with dimensions (5+32+1280+1)×1=1318×1; iThe emotion classification vector has a dimension of 5×1; F j V is a frequency dimension vector with dimensions of 32×1; ij This is a multimodal text-image sentiment vector with a dimension of 1280×1; y j The number of positive samples is a scalar; concat represents the vector concatenation operation.

[0101] This fusion process combines user emotions, interactive behaviors, and multimodal content features to form a comprehensive representation. For example, for a smartwatch, the system integrates the emotional inclinations in user reviews (such as "very satisfied"), interactive behavior data (such as extensive sharing and purchases), and product image and text information to form a comprehensive assessment of the product's popularity.

[0102] The processing procedure of an LSTM network can be represented as follows:

[0103]

[0104] Where: P is the final fusion vector with a dimension of 128×1; f represents the output function of the LSTM network, which usually uses softmax activation; n is the time series length; This indicates the cumulative processing of the time series.

[0105] In practical applications, time-series fusion can capture changing trends in product popularity. For example, the system can identify a sudden increase in sales and reviews of smartwatches at a specific point in time (such as after a brand promotion), which is crucial for predicting future demand. Through LSTM networks, the system can not only remember long-term sales trends but also identify short-term fluctuations and seasonal variations, improving the accuracy of predictions.

[0106] The demand perception module 3 is used to receive the multimodal-text-image sentiment fusion representation output by the preprocessing module 2, and to perform adaptive feature transformation on the received fusion representation through a multilayer perceptron to generate a multimodal-text-image fusion vector of historical sales and real-time fan sentiment.

[0107] like Figure 4 As shown, the demand perception module 3 includes a feature transformation network 31, a dynamic activation unit 32, and an adaptive adjustment unit 33.

[0108] The feature transformation network 31 contains a multi-layer nonlinear transformation structure to decompose the time-sensitive fusion representation into historical sales features and real-time sentiment features. The forward propagation process of the feature transformation network 31 can be represented as follows:

[0109] h1=σ(W1·P+b1),

[0110] h2=σ(W2·h1+b2),

[0111] h3=σ(W3·h2+b3),

[0112] h4=σ(W4·h3+b4),

[0113] Where: P is the fusion vector output by preprocessing module 2, with a dimension of 128×1; h1, h2, h3, and h4 are the outputs of each hidden layer, with dimensions of 256×1, 512×1, 256×1, and 128×1, respectively; W1, W2, W3, and W4 are the weight matrices of each layer, with dimensions of 256×128, 512×256, 256×512, and 128×256, respectively; b1, b2, b3, and b4 are the bias terms of each layer, with the same dimension as the output of the corresponding hidden layer; σ is the activation function, preferably ReLU or Leaky ReLU.

[0114] This variable-width / narrow-width network design can maintain computational efficiency while expanding feature representation capabilities, making it suitable for handling complex tasks such as e-commerce sales forecasting that require consideration of multiple factors simultaneously. For example, for demand forecasting of smartwatches, the network needs to comprehensively consider multiple aspects of information, including product attributes (such as functions and price), market environment (such as competitor situation and seasonal factors), and user feedback (such as comment sentiment and purchase intention).

[0115] In the feature decomposition stage, the network decomposes h4 into historical sales features S and real-time sentiment features E:

[0116] S = W S ·h4+b S ,

[0117] E=W E ·h4+b E ,

[0118] Where: S represents historical sales data, with a dimension of 64×1; E represents real-time sentiment data, with a dimension of 64×1; W... S W E These are the weight matrices for the decomposition process, each with a dimension of 64×128; b S b E These are the bias terms in the decomposition process, each with a dimension of 64×1.

[0119] This feature decomposition can separate different information from the fused representation, facilitating subsequent targeted processing. For example, historical sales feature S mainly includes information such as the product's sales cycle and price sensitivity, while real-time sentiment feature E reflects users' immediate evaluation of the product and changes in market popularity.

[0120] Dynamic activation unit 32 is used to select the optimal activation function based on feature distribution characteristics and to set up a gating activation mechanism to handle different types of features:

[0121] σ(x)=g(x)·ReLU(x)+(1-g(x))·LeakyReLU(x,α),

[0122] Where: σ(x) is the final activation function output; x is the input; g(x) = sigmoid(W g ·x+b g ) is the gate function, with an output range of 0-1; ReLU(x) = max(0,x) is the ReLU activation function; LeakyReLU(x,α) = max(α·x,x) is the LeakyReLU activation function; α is the slope of the negative half-region of LeakyReLU, with a value range of 0.01-0.2, and a preferred value of 0.1; W g and b g These are the weights and biases of the gating function, respectively.

[0123] This dynamic activation mechanism is particularly important when dealing with different types of features. For example, for features like sales data that typically show a clear trend, the ReLU activation function is better suited to preserving positive changes; while for features like sentiment scores that may fluctuate between positive and negative, LeakyReLU can better handle negative sentiment information and avoid information loss.

[0124] The adaptive adjustment unit 33 is used to dynamically adjust network parameters based on validation set performance. It sets a performance threshold to trigger the adjustment process and employs a gradual adjustment strategy to avoid excessive modification. The adjustment strategy uses a learning rate decay method.

[0125] η new =η old ·γt,

[0126] Where: η old The learning rate before adjustment; η new γ is the adjusted learning rate; γ is the decay coefficient, ranging from 0.5 to 0.9, with an optimal value of 0.7; t is the number of adjustments.

[0127] In practical applications, this adaptive adjustment mechanism helps the system adapt to different product and market environments. For example, in the rapidly changing electronics market, the system may need to adjust model parameters more frequently to adapt to rapid changes in consumer preferences; while in the relatively stable daily necessities market, a slower adjustment frequency can be used to maintain the stability of the predictions.

[0128] Finally, the demand perception module 3 outputs a multimodal text-image fusion vector of historical sales and real-time fan sentiment:

[0129] P = [S2, E2] = MLP(P),

[0130] Where: P is the fusion vector output by preprocessing module 2, with a dimension of 128×1; S2 and E2 are the historical sales feature and real-time sentiment feature after the final nonlinear transformation, respectively, with a dimension of 64×1; MLP represents the transformation function of the entire multilayer perceptron network; [S2,E2] represents the vector concatenation operation.

[0131] Through this process, the demand perception module can transform the original multimodal fusion representation into a more expressive feature representation, which includes both the product's historical sales performance and the user's real-time emotional feedback, providing a comprehensive information foundation for subsequent demand forecasting.

[0132] The expert system 4 is used to receive the multimodal text-image fusion vector output by the demand perception module 3, combine it with sales data, tag type and fan number information, predict product demand based on decision theory, and output the prediction results.

[0133] like Figure 5 As shown, the expert system 4 includes a data integration unit 41, a decision tree construction unit 42, and a prediction optimization unit 43.

[0134] Data integration unit 41 is used to fuse multimodal text-image fusion vectors with sales data, product attributes and tag information, integrate user profiles and fan count data, and combine real-time social media sentiment analysis results to construct a multi-dimensional decision input matrix:

[0135] X = β1·P + β2·S hist +β3·A+β4·T+β5·F,

[0136] Where: X is the integrated decision input matrix with a dimension of 256×1; P is the fusion vector output by preprocessing module 2 with a dimension of 128×1; S hist The data consists of historical sales data with a dimension of 32×1; A is a product attribute vector with a dimension of 32×1; T is a tag type vector with a dimension of 32×1; F is a fan count vector with a dimension of 32×1; β1, β2, β3, β4, and β5 are the weight coefficients of each type of data. They are scalars, and β1+β2+β3+β4+β5=1.

[0137] In e-commerce demand forecasting scenarios, the setting of these weighting coefficients is crucial, as they determine the system's reliance on different information sources. Based on practical application experience, for most consumer electronics products, the weights of multimodal sentiment analysis results (β1 = 0.3) and historical sales data (β2 = 0.25) should be relatively high, while product attributes (β3 = 0.15), tag type (β4 = 0.15), and number of followers (β5 = 0.15) should be relatively low. This is because consumer reviews and historical sales performance are typically the strongest indicators for predicting future demand.

[0138] The decision tree construction unit 42 is used to design a feature importance evaluation mechanism, construct a tree structure guided by information gain, set tree depth and complexity control parameters, and construct an interpretable decision path. Feature importance evaluation is based on information gain:

[0139]

[0140] where: IG(D,a) is the information gain of feature a; is the entropy of the dataset D, p i is the proportion of class i in the dataset, k is the number of classes; Values(a) is the set of values of feature a; D v is the sample subset where the value of feature a is v; |D| and |D v | are the number of samples in the dataset D and the subset D v respectively; Σ v ∈Values(a) means summing over all possible values of feature a.

[0141] In practical applications, information gain can help the system identify the factors that have the most influence on sales prediction. For example, for smartwatches, it may be found that battery life, user ratings, and promotion intensity are the three features with the highest information gain. The system will preferentially construct the upper-level nodes of the decision tree based on these features, thus forming a more effective prediction model.

[0142] The tree depth of the decision tree is set to 3 - 10, and the minimum number of samples for node splitting is set to 5. The setting of these parameters is based on empirical values of e-commerce data analysis and can achieve a good balance between model complexity and prediction accuracy. If the tree depth is too shallow, it may lead to underfitting and fail to capture complex patterns in the data; while if the tree depth is too deep, it may lead to overfitting and be overly sensitive to the particularity of the training data. For most e-commerce products, setting the tree depth to 5 - 7 usually yields good results.

[0143] The prediction optimization unit 43 is used to define the influence ratio of historical sales and real-time sentiment, design a ratio dynamic adjustment mechanism, optimize the ratio parameters based on historical prediction errors, set the upper and lower limit ranges of the prediction results, integrate the results of multiple prediction models, and generate a more robust integrated prediction result. The formula for predicting the sales volume of a commodity is:

[0144]

[0145] where: is the final sales volume prediction value; E(S) is the predicted value of the historical sales average, usually calculated based on the sales data of the past 30 days; g(S) is the final sales volume prediction value of the decision tree; η is the proportion coefficient of social public opinion in the final sales volume prediction value, and the range is usually 0.1 - 0.5.

[0146] The proportionality coefficient η is calculated as follows:

[0147]

[0148] Where: S represents actual sales data; f(S) is the predicted sales value obtained after inputting the sales data into the decision tree; S * The predicted values ​​for sales data are obtained through regression predictions trained on a decision tree. In practical applications, the value of η reflects the degree of influence of social media sentiment on sales.

[0149] For example, for smartwatches that rely heavily on word-of-mouth, the η value may be high (around 0.4-0.5), indicating that social media reviews have a significant impact on their sales; while for daily necessities, the η value may be low (around 0.1-0.2), indicating that their sales are more influenced by historical purchasing patterns than by social reviews.

[0150] The upper and lower limits of the forecast are set at ±30% of the actual sales volume.

[0151] y min =0.7·S avg ,

[0152] y max =1.3·S avg ,

[0153] Wherein: S avg This represents the historical average sales volume; y min and y max These represent the lower and upper limits of the prediction result, respectively.

[0154] This setting of upper and lower limits can prevent abnormal fluctuations in prediction results, improving the stability and reliability of the system. For example, even if some extremely negative reviews appear on social media, the system will not predict that sales will plummet to less than 30% of historical levels. This is consistent with the actual situation of most product sales—even negative reviews will not cause sales to completely collapse.

[0155] In addition, the prediction optimization unit 43 also employs a model ensemble method to integrate the results of multiple prediction models:

[0156]

[0157] in: For the final integrated prediction results; w represents the prediction result of the i-th model. i For the corresponding weights, and k is the number of models, usually 3-5; This represents a weighted summation of all model results.

[0158] E-commerce forecasting systems typically integrate multiple forecasting models, such as time series models based on historical sales, sentiment analysis models based on user reviews, and feature regression models based on product attributes. This integration strategy combines the strengths of different models to improve forecast robustness. For example, when historical sales data is insufficient in the early stages of a new product launch, the system automatically increases the weight of models based on product attributes and user reviews; conversely, once the product enters a stable phase, the weight of historical sales models increases accordingly.

[0159] The system also includes a data acquisition module 5 and a user profile analysis module 6.

[0160] The data acquisition module 5 is used to collect user comments on product categories, brand names, product types, and sales tags from social media platforms, perform data cleaning and standardization, and store the processed data as text datasets and image datasets, respectively. In embodiments of the present invention, data acquisition employs web crawling technology to periodically collect user comments and product images from mainstream social media platforms (such as Weibo, Douyin, and Xiaohongshu) and e-commerce platforms (such as Taobao and JD.com).

[0161] The data cleaning process includes removing special characters, emojis, and stop words. Standardization processing includes text segmentation and image resizing. For example, the original comment "This #smartwatch# is really super useful! 100% battery life, highly recommended!" becomes "This smartwatch is really super useful! 100% battery life, highly recommended!" after cleaning. Then, through word segmentation, it is converted to the format "this / smartwatch / really / super / useful / battery life / highly / recommended / to / everyone", facilitating subsequent processing.

[0162] User profile analysis module 6 is used to extract user profiles from the collected comment information, and to determine whether the user profiles are consistent with historical user profiles. If they are consistent, the labeled data is marked as a positive sample; if they are inconsistent, the labeled data is marked as a negative sample. Finally, positive and negative sample pairs are output. The user profile consistency judgment is based on cosine similarity.

[0163]

[0164] Where: U1 and U2 represent the vector representations of the current user profile and the historical user profile, respectively, with dimensions typically between 100 and 200; U1·U2 represents the dot product of the two vectors; ||U1|| and ||U2|| represent the Euclidean norm (L2 norm) of the two vectors, respectively.

[0165] In practical applications, user profiles encompass multiple dimensions, including consumption preferences, price sensitivity, and brand loyalty. A similarity score exceeding a threshold of 0.8 is considered consistent; otherwise, it's considered inconsistent. For example, for potential buyers of an electronic product, the system analyzes their historical reviews and purchase records, extracting characteristics such as prioritizing performance, focusing on the latest technology, and being insensitive to price, thus forming a user profile. If this profile highly matches the target user profile for the product (similarity > 0.8), the user's review is marked as a positive sample, providing higher reference value for sales prediction.

[0166] The system also includes a model evaluation module 7 and a results display module 8.

[0167] Model evaluation module 7 is used to set evaluation metrics, including accuracy, precision, and F1 index. It records the deviation between historical predictions and actual results, establishes a prediction deviation correction model, dynamically adjusts prediction parameters and strategies, and forms a closed-loop optimized prediction system. The calculation methods for accuracy, precision, and F1 index are as follows:

[0168]

[0169] Where: m is the percentage of accurate predictions; n is the total number of predictions; TP is the number of correct data points in the positive class; FP is the number of data points in the negative class that were misclassified as positive. FN is the recall rate; FN is the number of positive data that were misclassified as negative data.

[0170] In e-commerce forecasting systems, these evaluation metrics have specific practical significance. For example, for predicting the sales of a smartwatch, accuracy reflects the overall degree of agreement between the predicted value and the actual sales; precision reflects the proportion of cases where the system predicted an increase in sales, and the actual increase actually occurred; recall reflects the proportion of cases where the system successfully predicted an increase in sales. Overall, the F1 score can balance precision and recall, providing a more comprehensive evaluation of system performance.

[0171] Prediction bias correction uses an exponential smoothing method:

[0172]

[0173] in: This is the corrected predicted value; These are the original predicted values; This is the average of historical prediction errors, calculated as follows: Where S i and Let be the actual sales and the predicted sales of the i-th sample, respectively, and k be the number of samples; α is the correction coefficient, with a value ranging from 0.1 to 0.5, and an optimal value of 0.2.

[0174] This prediction bias correction mechanism can automatically adjust prediction results based on historical prediction performance, improving the system's adaptability. For example, if the system consistently underestimates the sales volume of a certain product (…), it can correct the prediction bias. If the value is positive, the correction mechanism will automatically increase subsequent predictions to make them closer to the actual situation.

[0175] Module 8, the Results Display module, presents forecast results in the form of charts, reports, or data dashboards. It supports multi-dimensional data analysis and interactive queries, providing decision support and analytical insights. In practical applications, results displays typically include various visualization formats such as sales forecast curves, public opinion heat maps, and category comparison analyses, making it easier for e-commerce platforms and brands to intuitively understand market trends and make informed decisions.

[0176] The system adopts the following inter-component collaboration mechanisms: data sharing and caching mechanism, asynchronous processing and parallel computing mechanism, and feedback loop and iterative optimization mechanism.

[0177] Data sharing and caching mechanisms are used to establish an intermediate result cache pool to reduce redundant calculations. A data sharing protocol is designed to standardize the interaction interface, enabling incremental updates to reduce data transfer overhead and optimizing memory usage to improve computational efficiency. In practical applications, intermediate results (such as extracted text features and image features) are cached for 12 hours so that multiple modules can share them, significantly improving processing efficiency. For example, when multiple products share the same review text, the system only needs to perform text feature extraction once, greatly reducing computational resource consumption.

[0178] Asynchronous processing and parallel computing mechanisms are used to allocate independent tasks to different processing units, design task scheduling mechanisms to optimize resource utilization, implement pipelined processing to improve throughput, and establish result synchronization mechanisms to ensure consistency. In e-commerce forecasting systems, text processing and image processing, two computationally intensive tasks, can be executed in parallel without waiting for each other, significantly improving system response speed. This parallel mechanism is particularly effective in handling large amounts of product data, maintaining the system's high efficiency.

[0179] Feedback loops and iterative optimization mechanisms are used to build feedback channels between components, transmit prediction errors to guide parameter adjustments, design iterative optimization strategies for continuous improvement, and establish a performance monitoring system to evaluate system status. In practical applications, after processing each batch of new data, the system calculates the prediction error and feeds it back to each module, triggering the parameter adjustment process. For example, when the system detects a decline in the prediction accuracy for a certain type of seasonal product, it automatically increases the weight of the time series model in the integrated forecast to better capture seasonal changes.

[0180] This invention also provides a method for predicting demand in multimodal social media sentiment perception, comprising the following steps:

[0181] 1. Receive raw text and image data from social media platforms, process the text and image data through dual-channel feature extraction to generate text feature vectors and image feature vectors, and perform cross-modal representation learning to fuse the text feature vectors and image feature vectors into a multimodal-text-image sentiment vector;

[0182] 2. Receive multimodal text-image sentiment vectors, quantify live event data and historical product sales through the time dimension, quantify live event influence through the frequency dimension, and use recurrent neural networks to fuse the quantified data with the original text data to form a time-sensitive multimodal text-image sentiment fusion representation.

[0183] 3. Receive multimodal-text-image sentiment fusion representations, and perform adaptive feature transformation on the received fusion representations through a multilayer perceptron to generate multimodal-text-image fusion vectors of historical sales and real-time fan sentiment;

[0184] 4. Receive multimodal text-image fusion vectors, combine them with sales data, tag types and fan count information, predict product demand based on decision theory, and output the prediction results.

[0185] Taking a newly launched smartwatch as an example, the system first collects user reviews (e.g., the watch has strong battery life but average water resistance) and product images (e.g., official promotional images, user testimonials) from various social media platforms. It then extracts textual and image features using ROBERTa and CLIP models respectively, and fuses these features into a multimodal representation. Next, the system combines time-dimensional information (e.g., daily sales changes after product launch) and frequency-dimensional information (e.g., the frequency of user reviews, likes, and shares) to generate a time-sensitive fusion representation using an LSTM network. Then, a multilayer perceptron transforms this representation into a more expressive fusion vector, simultaneously separating historical sales and real-time sentiment features. Finally, the system integrates this fusion vector with product attributes, tag information, and fan data, and uses a decision tree model to predict sales trends for the coming week.

[0186] Through the implementation of the above system and method, this invention achieves effective perception of multimodal social media sentiment data and accurate prediction of product demand, providing e-commerce platforms and brand owners with a powerful decision support tool.

[0187] Those skilled in the art should understand that the above embodiments are merely illustrative of the principles of the present invention and are not intended to limit the scope of the invention. Without departing from the spirit and scope of the invention, those skilled in the art can make various changes and modifications to the technical solutions of the present invention, and these changes and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-modal social sentiment perception based demand prediction system, characterized in that , comprising: a multi-modal-text-image sentiment fusion module, configured to receive original text data and image data of a social platform, process the text data and the image data through double-channel feature extraction, generate a text feature vector and an image feature vector, and perform cross-modal representation learning to fuse the text feature vector and the image feature vector into a multi-modal-text-image sentiment vector, the multi-modal-text-image sentiment fusion module comprising: a text input layer, configured to receive the original text data, encode the input text by using a pre-trained language model, and generate the text feature vector; an image input layer, configured to receive the original image data, encode the input image by using a visual language model, and generate the image feature vector; a feature mapping unit, configured to map the text feature vector to an image feature space or map the image feature vector to a text feature space, to realize bidirectional cross-modal feature conversion, the feature mapping unit comprising: a text-to-image mapping channel, configured to convert the text feature vector into an image feature space representation through a multi-layer mapping network; an image-to-text mapping channel, configured to convert the image feature vector into a text feature space representation through an inverse mapping network; a feature calibration network, configured to compare the difference between the original feature and the mapped feature, retain the key information of the original feature through a residual connection, correct the mapping error, and ensure the consistency and accuracy of the bidirectional mapping; a sentiment fusion unit, configured to fuse the text feature vector and the image feature vector to form the multi-modal-text-image sentiment vector, and convert the fused vector into a sentiment classification vector through a nonlinear transformation; a preprocessing module, connected with the multi-modal-text-image sentiment fusion module, configured to receive the multi-modal-text-image sentiment vector output by the multi-modal-text-image sentiment fusion module, quantify live streaming activity data and historical sales of a commodity through a time dimension, quantify live streaming influence through a frequency dimension, and fuse the quantified data and the original text data by using a recurrent neural network to form a time-sensitive multi-modal-text-image sentiment fusion representation; a demand perception module, connected with the preprocessing module, configured to receive the multi-modal-text-image sentiment fusion representation output by the preprocessing module, and perform adaptive feature conversion on the received fusion representation through a multi-layer perception machine to generate a multi-modal-text-image fusion vector of historical sales and real-time sentiment of fans; an expert system, connected with the demand perception module, configured to receive the multi-modal-text-image fusion vector output by the demand perception module, combine sales data, label types, and fan quantity information, predict the demand for a commodity based on decision theory, and output a prediction result; a calibration process of the feature calibration network is represented as: Wherein: is the image feature vector obtained by mapping; V img is the original image feature vector; is the calibrated image feature vector; and a is a calibration coefficient, with a value range of 0.1-0.

5. the demand perception module comprising: a feature conversion network, comprising a multi-layer nonlinear transformation structure, configured to decompose the time-sensitive fusion representation into historical sales features and real-time sentiment features, perform nonlinear feature transformation on different feature types, and generate optimized feature representations; a dynamic activation unit, configured to select an optimal activation function according to feature distribution characteristics, set a gated activation mechanism to process different types of features, adaptively adjust activation function parameters, and enhance the expression ability of the model for complex features; The adaptive adjustment unit is configured to dynamically adjust network parameters based on the performance of the verification set, set a performance threshold to trigger the adjustment process, adopt a gradual adjustment strategy to avoid excessive modification, and establish an adjustment history record for long-term optimization.

2. The multi-modal social sentiment and buzz perception based demand prediction system as claimed in claim 1, wherein The preprocessing module comprises: The time dimension quantization unit is configured to divide live event data and historical sales of goods according to a time window, set a time decay function, extract time series features, and generate a time dimension index vector; The frequency dimension quantization unit is configured to count the frequency of user interaction behaviors, design a frequency influence score mechanism, consider the interaction quality and user influence factors, and generate a frequency dimension vector; The time sequence fusion unit is configured to process time series data using a recurrent neural network, extract long-term and short-term dependencies, identify key time points and change patterns, and fuse the time dimension index vector, the frequency dimension vector and the multi-modal-text-image sentiment vector to generate a time sequence sensitive fusion representation.

3. The multi-modal social sentiment and buzz perception based demand prediction system as claimed in claim 2, wherein The time decay function of the time dimension quantization unit is expressed as: w t = e -λ(T-t) where: w t is the weight of time point t; T is the current time point; t is the historical time point; λ is the decay coefficient, the value range is 0.1-0.3, e is the base of natural logarithm.

4. The multi-modal social sentiment and buzz perception based demand prediction system as claimed in claim 1, wherein The expert system comprises: The data integration unit is configured to fuse the multi-modal-text-image fusion vector with sales data, product attributes and label information, integrate user portraits and fan number data, combine real-time social public opinion analysis results, and construct a multi-dimensional decision input matrix; The decision tree construction unit is configured to design a feature importance evaluation mechanism, construct a tree structure using information gain, set tree depth and complexity control parameters, and construct an interpretable decision path; The prediction optimization unit is configured to define the influence proportion of historical sales and real-time sentiment, design a dynamic adjustment mechanism for the proportion, optimize the proportion parameter based on historical prediction errors, set upper and lower limit ranges for prediction results, integrate the results of multiple prediction models, and generate a more robust integrated prediction result.

5. The multi-modal social sentiment and buzz perception based demand prediction system as claimed in claim 1, wherein The system further comprises: The data collection module is configured to collect comment information of users on product categories, brand names, product categories and sales labels from social platforms, perform data cleaning and standardization processing, and store the processed data as text data sets and image data sets respectively; The user portrait analysis module is configured to extract user portraits from the collected comment information, determine whether the user portrait is consistent with the historical user portrait, mark the labeled data as positive samples if they are consistent, mark the labeled data as negative samples if they are inconsistent, and finally output positive and negative sample pairs.

6. The multi-modal social sentiment and buzz perception based demand prediction system as claimed in claim 1, wherein The system further comprises: The model evaluation module is configured to set evaluation indexes including accuracy, precision and F1 index, record the deviation between historical prediction and actual results, establish a prediction deviation correction model, dynamically adjust prediction parameters and strategies, and form a closed-loop optimized prediction system; The result display module is configured to display the prediction results in the form of charts, reports or data dashboards, support multi-dimensional data analysis and interactive query, and provide decision support and analysis insights.

7. The multi-modal social sentiment and buzz perception based demand prediction system as claimed in claim 1, wherein The system adopts the following component cooperation mechanism: The data sharing and caching mechanism is configured to establish an intermediate result cache pool to reduce repeated calculation, design a data sharing protocol to standardize the interaction interface, realize incremental update to reduce data transmission overhead, and optimize memory usage to improve calculation efficiency; Asynchronous processing and parallel computing mechanism is used to allocate independent tasks to different processing units, design task scheduling mechanism to optimize resource utilization, realize pipeline processing to improve throughput, and establish result synchronization mechanism to ensure consistency. Feedback loop and iterative optimization mechanism is used to build feedback channels between components, deliver prediction error to guide parameter adjustment, design iterative optimization strategy to continuously improve, and establish performance monitoring system to evaluate system status.

8. A method for demand prediction of multi-modal social sentiment perception, using the system of any one of claims 1-7, characterized in that The method comprises the following steps: Receiving original text data and image data of a social platform, processing the text data and image data through double-channel feature extraction to generate text feature vectors and image feature vectors, and performing cross-modal representation learning to fuse the text feature vectors and image feature vectors into multi-modal-text-image sentiment vectors; Receiving the multi-modal-text-image sentiment vectors, quantifying live streaming activity data and historical sales volume through time dimension, quantifying live streaming influence through frequency dimension, and using a recurrent neural network to fuse the quantified data and original text data to form time-sensitive multi-modal-text-image sentiment fusion representation; Receiving the multi-modal-text-image sentiment fusion representation, and performing adaptive feature conversion on the received fusion representation through a multi-layer perceptron to generate a multi-modal-text-image fusion vector of historical sales volume and real-time fan sentiment; Receiving the multi-modal-text-image fusion vector, combining sales volume data, label type and fan number information, predicting product demand based on decision theory, and outputting the prediction result.

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