Consumer evaluation content intelligent analysis and reply system based on artificial intelligence technology

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a consumer evaluation content intelligent analysis and reply system based on the artificial intelligence technology, and the system comprises a data collection and preprocessing module which collects consumer evaluation data from a plurality of platforms; the consumer portrait dynamic updating and predicting module constructs and dynamically updates a consumer portrait model, and predicts future behaviors and demands of the consumer portrait model in real time; the evaluation content analysis module performs content analysis on the evaluation data; the semantic understanding and question recognition module recognizes questions and demands concerned by consumers; the intelligent reply generation module automatically generates reply content; and the multi-language support and man-machine collaboration module realizes evaluation content analysis and reply generation of multiple languages. According to the method, the accuracy and the real-time performance of personalized recommendation are improved, and the intelligent level and the user experience of the system are remarkably enhanced.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to an intelligent analysis and response system for consumer reviews based on artificial intelligence technology. Background Technology

[0002] With the rapid development of e-commerce and social media, consumers have left a large amount of review information on various platforms. This review information not only reflects consumers' purchase experience and product satisfaction, but also contains a wealth of valuable data about consumer behavior and needs. How to effectively analyze and utilize this review data to provide enterprises with accurate market feedback and personalized services has become a hot topic in current research and application.

[0003] However, existing technologies have many shortcomings in processing and analyzing consumer review data. First, the efficiency of data collection and preprocessing is low, making it difficult to obtain high-quality review data from multiple platforms. Second, consumer profile models are not updated in a timely manner, failing to accurately reflect consumers' latest behaviors and needs, which affects the effectiveness of personalized recommendations. Third, existing review content analysis methods cannot deeply analyze the sentiment, themes, and trends of reviews, resulting in a lack of targeted response content. In addition, multilingual processing capabilities are insufficient, failing to meet the needs of global users, and there is a lack of effective manual review and intervention mechanisms, affecting the quality of response content and user satisfaction.

[0004] This invention aims to provide an intelligent analysis and response system for consumer reviews based on artificial intelligence technology. It can dynamically update consumer profiles, accurately predict behaviors and needs, automatically generate personalized responses, and support multilingual processing and human intervention, significantly improving user experience and merchant service efficiency. Summary of the Invention

[0005] To achieve the above objectives, this invention provides an intelligent analysis and response system for consumer reviews based on artificial intelligence technology.

[0006] The intelligent analysis and response system for consumer reviews based on artificial intelligence technology includes a data collection and preprocessing module, a dynamic update and prediction module for consumer profiles, a review content analysis module, a semantic understanding and problem identification module, an intelligent response generation module, and a multilingual support and human-computer collaboration module.

[0007] The data acquisition and preprocessing module collects consumer review data from multiple platforms and preprocesses the collected review data, including cleaning, deduplication, word segmentation, and syntactic analysis.

[0008] The consumer profile dynamic update and prediction module constructs and dynamically updates the consumer profile model based on preprocessed evaluation data, and predicts its future behavior and needs in real time, specifically including:

[0009] Model building: Constructing consumer profile models using historical evaluation data;

[0010] Data integration: Receive and integrate pre-processed consumer review data from multiple platforms to form a comprehensive dataset;

[0011] Incremental learning: The incremental learning algorithm is applied to train the comprehensive dataset and dynamically update the consumer profile model;

[0012] Behavioral prediction: Using updated consumer profile models, predict consumers' future behavior and needs in real time;

[0013] Consumer profile storage: Storing dynamically updated consumer profiles in a database;

[0014] The evaluation content analysis module performs content analysis on the predicted future consumer behavior and needs, as well as the preprocessed evaluation data, including sentiment analysis, topic identification, and trend analysis.

[0015] The semantic understanding and problem identification module performs semantic parsing on the results of content analysis, extracts evaluation information, and identifies the problems and needs that consumers care about.

[0016] The intelligent reply generation module automatically generates reply content based on a predefined reply template library and a natural language generation algorithm, utilizing extracted evaluation information and the identification results of consumer concerns and needs.

[0017] The multilingual support and human-computer collaboration module enables the analysis and response generation of evaluation content in multiple languages, and provides an interface for manual review and intervention, allowing users to modify and optimize the automatically generated response content.

[0018] Furthermore, the data acquisition and preprocessing module includes:

[0019] Data collection: Real-time collection of consumer review data from multiple platforms (such as e-commerce websites, social media platforms, forums, etc.);

[0020] Data cleaning: Cleaning the collected evaluation data to remove noise and irrelevant information;

[0021] Data deduplication: Cosine similarity is used to detect and delete duplicate data. The calculation formula is as follows:

[0022]

[0023] Where A and B represent two text vectors;

[0024] Word segmentation: The evaluation data is segmented using a word segmentation algorithm based on the bidirectional maximum matching (BMM) method. The calculation formula is as follows:

[0025] BMM(S) = max BMM (S1,S2,...,S n );

[0026] Where S represents the sentence to be segmented, S1, S2, ..., S n Indicates the result of word segmentation;

[0027] Syntax analysis: Dependency parsing algorithm is used for syntactic structure analysis. The calculation formula is as follows:

[0028] Score(T) = ∑ (h,m)∈T weight(h,m);

[0029] Where T represents the dependency tree, h and m represent the head node and modifier of the word, respectively, and weight(h,m) represents the dependency weight between them.

[0030] Furthermore, the consumer profile model employs a graph neural network (GNN), which includes:

[0031] Graph Construction and Initialization: The consumer review data is constructed into a graph structure, where nodes represent consumers, edges represent relationships between consumers (such as joint purchases, similar reviews, etc.), the node feature matrix X represents the feature vector of each consumer, and the adjacency matrix A represents the relationships between consumers. The initial node feature matrix is ​​expressed as:

[0032] H (0) =X;

[0033] Graph Convolutional Layer: Introduces an interaction weight matrix W between consumers. inter and feature weight matrix W feat The calculation formula is:

[0034] H (l+1) =σ(D -1 / 2 AD -1 / 2 H (l) W inter +H (l) W feat );

[0035] Among them, H (l) σ is the feature matrix of the nodes in the l-th layer, σ is the activation function (such as ReLU), and D is the degree matrix. ii =∑ j Aij W inter It is the interaction weight matrix between consumers, W feat It is the feature weight matrix;

[0036] Self-attention mechanism: Introducing a self-attention mechanism for feature weighting, specifically including:

[0037] Self-attention mechanism: e ij =LeakyReLU(a T [WH i WH j ];

[0038] Normalization of attention coefficient:

[0039] Update node characteristics:

[0040] Among them, e ij Here, is the attention coefficient between consumers i and j, is the attention vector, ‖ denotes the vector concatenation operation, W is the feature transformation matrix, and H... i and H j These are the feature vectors of consumers i and j, respectively. It is the set of neighboring nodes of consumer i;

[0041] Multi-layer stacking and non-linear activation: Multiple graph convolutional layers are stacked and subjected to multiple non-linear activations to extract features. The calculation formula is as follows:

[0042]

[0043] Feature aggregation and output: Aggregate the features of each node to generate a consumer profile. The calculation formula is as follows:

[0044] Z = Aggregate(H) (L) );

[0045] Aggregate is used for aggregation of mean, sum, or maximum value.

[0046] Behavior prediction: Using the generated consumer profiles, behavior prediction is performed to forecast future behaviors and needs. The calculation formula is as follows:

[0047]

[0048] In this context, MLP stands for Multilayer Perceptron, and Z represents the aggregated node features.

[0049] Furthermore, the data integration includes:

[0050] Data reception: Receives pre-processed consumer review data from multiple platforms;

[0051] Data mapping and standardization: Mapping evaluation data from different platforms to a unified data structure and format to eliminate data differences between platforms;

[0052] Data merging: This involves merging evaluation data from multiple sources to form a unified, comprehensive dataset.

[0053] Furthermore, the incremental learning includes:

[0054] Incremental data reception: Receive newly added consumer review data and add it to the comprehensive dataset;

[0055] Feature extraction: Consumer features are extracted from new data using the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm, and the feature matrix is ​​updated.

[0056] Model Update: The consumer profile model is dynamically updated using incremental learning algorithms, specifically including:

[0057] Weight initialization: Initialize the weights of the newly added data feature vectors;

[0058] Incremental training: Incremental training is performed using gradient descent to update the model weights. The calculation formula is as follows:

[0059]

[0060] Among them, w new It is the updated weight, w old These are the original weights, and η is the learning rate. It is the gradient of the loss function;

[0061] Model fusion: The updated model weights are fused to form a new consumer profile model. The calculation formula is as follows:

[0062] w final =αw old +(1-α)w new ;

[0063] Among them, w final α is the weight after fusion, and α is the weight fusion coefficient.

[0064] Furthermore, the behavior prediction includes:

[0065] Input features: Extract the consumer's feature vector from the feature matrix;

[0066] Forward propagation: Calculates predictions of future consumer behavior using a new consumer profiling model. The formula is as follows:

[0067] H (l+1) =σ(D -1 / 2AD -1 / 2 H (l) W inter +H (l) W feat );

[0068] Among them, H (l) Here, D is the node feature matrix of layer l, D is the degree matrix, A is the adjacency matrix, and W is the node feature matrix of layer l. inter It is the interaction weight matrix, W feat It is the feature weight matrix, and σ is the activation function;

[0069]

[0070] Among them, H (L) It is the node feature matrix of the final layer; MLP is a multilayer perceptron.

[0071] Loss Calculation: Calculate the loss between the predicted value and the actual value. The calculation formula is as follows:

[0072]

[0073] Where y is the actual value, is the predicted value, and n is the number of samples.

[0074] Furthermore, the evaluation content analysis module includes:

[0075] Sentiment Analysis: The preprocessed evaluation data is classified according to sentiment. Sentiment analysis algorithms are used to identify positive, negative, and neutral ratings. The calculation formula is as follows:

[0076]

[0077] Where Sentiment(d) represents the sentiment score of document d, and vec(w i ) indicates the word w i The word vectors, sentiment(w i ) indicates the word w i Emotional weighting;

[0078] Topic Identification: The main topics in the evaluation data are identified using the Latent Dirichlet Allocation (LDA) model, extracting the hot topics of consumer interest. The calculation formula is as follows:

[0079]

[0080] Where θ represents the document-topic distribution, z represents the topic distribution, w represents the words, and α and β are the hyperparameters of LDA;

[0081] Trend Analysis: An Autoregressive Integrated Moving Average (ARIMA) model was used to perform time-series analysis on the evaluation data to identify trends in consumer behavior and demand. The calculation formula is as follows:

[0082]

[0083] Among them, y t Here is the predicted value for time t, and c is a constant term. It is the autoregressive coefficient, θ i It is the moving average coefficient, ∈ t This is the error term.

[0084] Furthermore, the semantic understanding and question identification module includes:

[0085] Semantic parsing: The results of content analysis are subjected to semantic parsing using dependency parsing algorithms to identify grammatical relations and dependency structures in sentences;

[0086] Problem identification: Named entity recognition and keyword extraction algorithms are used to identify the problems and needs that consumers care about.

[0087] The named entity recognition algorithm uses a conditional random field (CRF) model to identify named entities and information in the evaluation.

[0088] The keyword extraction algorithm uses TF-IDF and TextRank algorithms to extract keywords and identify the core issues and needs in the evaluation.

[0089] Furthermore, the intelligent reply generation module includes:

[0090] Template matching: Selects a template from a predefined response template library using similarity calculation. The calculation formula is as follows:

[0091]

[0092] Where T is the template vector, Q is the question vector, and t i and q i These are the template and the feature values ​​of the problem, respectively.

[0093] Natural Language Generation: A sequence-to-sequence (Seq2Seq) model based on an attention mechanism is used to generate a natural language response based on the extracted evaluation information and recognition results. The calculation formula is as follows:

[0094]

[0095] e ij =a(s i-1 ,h j );

[0096] Where x is the input sequence (evaluation information and recognition result), y is the output sequence (response content), and P(y t |y 1:t-1 (x) is the word y generated at time step t. t The probability, α ij It is attention weight, e ij It is the attention scoring function, s i-1 It's the decoder state, h j It is the encoder output.

[0097] Furthermore, the multilingual support and human-computer collaboration module includes:

[0098] Multilingual translation: Employing an attention-based sequence-to-sequence model to achieve translation between multiple languages;

[0099] Multilingual sentiment analysis and response generation: performing sentiment analysis and response generation on translated evaluation content;

[0100] Manual review and intervention interface: Provides an interface to support users in reviewing, editing, and confirming the generated response content.

[0101] The beneficial effects of this invention are:

[0102] This invention collects and processes consumer review data from multiple platforms through a data acquisition and preprocessing module, ensuring the integrity and accuracy of the data and providing a high-quality data foundation for subsequent analysis and processing. The review content analysis module, through sentiment analysis, topic identification, and trend analysis, deeply analyzes the emotional tendencies, hot topics of interest, and behavioral trends in consumer reviews, providing strong support for intelligent response generation and personalized recommendations. The semantic understanding and problem identification module, through semantic parsing and problem identification, extracts review information and identifies the issues and needs that consumers care about, ensuring that the system can fully understand consumer behavior and needs, further improving the system's intelligence level.

[0103] This invention achieves dynamic updates to consumer profiles through incremental learning and real-time prediction. It can efficiently capture the complex relationships and feature importance among consumers, reflect the latest consumer behaviors and needs in real time, and improve the accuracy and real-time performance of personalized recommendations by dynamically updating consumer profiles and accurately predicting future behaviors. This significantly enhances the system's intelligence level and user experience.

[0104] This invention utilizes machine translation technology and a user interface to analyze and generate feedback in multiple languages, and provides interfaces for manual review and intervention to ensure the quality and personalization of the responses. This significantly improves the efficiency and quality of customer service, enhances user satisfaction and system flexibility, and adapts to the needs of users worldwide. Attached Figure Description

[0105] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0106] Figure 1 This is a schematic diagram of the system functional modules according to an embodiment of the present invention;

[0107] Figure 2 This is a schematic diagram of the consumer profile dynamic update and prediction module in an embodiment of the present invention. Detailed Implementation

[0108] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0109] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0110] like Figures 1-2 As shown, the intelligent analysis and response system for consumer reviews based on artificial intelligence technology includes a data collection and preprocessing module, a consumer profile dynamic update and prediction module, a review content analysis module, a semantic understanding and problem identification module, an intelligent response generation module, and a multilingual support and human-computer collaboration module.

[0111] The data acquisition and preprocessing module collects consumer review data from multiple platforms and preprocesses the collected review data, including cleaning, deduplication, word segmentation, and syntactic analysis.

[0112] The consumer profile dynamic update and prediction module constructs and dynamically updates the consumer profile model based on preprocessed evaluation data, and predicts future behaviors and needs in real time, improving the accuracy and real-time performance of personalized recommendations. Specifically, this includes:

[0113] Model building: Constructing consumer profile models using historical evaluation data;

[0114] Data integration: Receive and integrate pre-processed consumer review data from multiple platforms to form a comprehensive dataset;

[0115] Incremental learning: The incremental learning algorithm is applied to train the comprehensive dataset and dynamically update the consumer profile model;

[0116] Behavioral prediction: Using updated consumer profile models, predict consumers' future behavior and needs in real time;

[0117] Consumer profile storage: Store dynamically updated consumer profiles in a database for access and use by other modules;

[0118] The evaluation content analysis module performs content analysis on predicted future consumer behavior and needs, as well as preprocessed evaluation data, including sentiment analysis, topic identification, and trend analysis.

[0119] The semantic understanding and problem identification module performs semantic parsing on the results of content analysis, extracts evaluation information, and identifies the issues and needs that consumers care about.

[0120] The intelligent reply generation module is based on a predefined reply template library and a natural language generation algorithm. It uses extracted evaluation information and the identification results of consumer concerns and needs to automatically generate reply content.

[0121] The multilingual support and human-computer collaboration module enables the analysis and response generation of evaluation content in multiple languages, and provides a human review and intervention interface, allowing users to modify and optimize the automatically generated response content;

[0122] Through the above, comprehensive analysis and intelligent processing of consumer review data are achieved, enabling dynamic updates to consumer profiles, accurate prediction of behavior and needs, automatic generation of personalized responses, and support for multilingual processing and human intervention, significantly improving user experience and merchant service efficiency.

[0123] The data acquisition and preprocessing module includes:

[0124] Data collection: Real-time collection of consumer review data from multiple platforms (such as e-commerce websites, social media platforms, forums, etc.);

[0125] Data cleaning: Cleaning the collected evaluation data to remove noise and irrelevant information;

[0126] Data deduplication: Cosine similarity is used to detect and delete duplicate data. The calculation formula is as follows:

[0127]

[0128] Here, A and B represent two text vectors. By comparing text pairs with a similarity greater than a certain threshold, duplicate evaluation data is removed.

[0129] Word segmentation: The evaluation data is segmented using a word segmentation algorithm based on the bidirectional maximum matching (BMM) method. The calculation formula is as follows:

[0130] BMM(S) = max BMM (S1,S2,...,S n );

[0131] Where S represents the sentence to be segmented, S1, S2, ..., S n This indicates the result of word segmentation, and the optimal segmentation result is selected through bidirectional matching.

[0132] Syntax analysis: Dependency parsing algorithm is used for syntactic structure analysis. The calculation formula is as follows:

[0133] Score(T) = ∑ (h,m)∈T weight(h,m);

[0134] Where T represents the dependency tree, h and m represent the head node and modifier of the vocabulary, respectively, and weight(h,m) represents the dependency relationship weight between them. The grammatical structure of the sentence is analyzed by calculating the score of the dependency tree.

[0135] The above measures improve the efficiency and accuracy of data preprocessing, providing a high-quality data foundation for subsequent analysis and processing. This enhances the overall intelligence level and user experience of the system, significantly improving the effectiveness of personalized recommendations and intelligent responses.

[0136] The consumer profiling model uses a graph neural network (GNN), which includes:

[0137] Graph Construction and Initialization: The consumer review data is constructed into a graph structure, where nodes represent consumers, edges represent relationships between consumers (such as joint purchases, similar reviews, etc.), the node feature matrix X represents the feature vector of each consumer, and the adjacency matrix A represents the relationships between consumers. The initial node feature matrix is ​​expressed as:

[0138] H (0) =X;

[0139] Graph Convolutional Layer: To capture the complex relationships between consumers, an interaction weight matrix W is introduced. inter and feature weight matrix W feat The calculation formula is:

[0140] H (l+1) =σ(D -1 / 2 AD -1 / 2 H (l) W inter +H (l) W feat );

[0141] Among them, H (l) σ is the feature matrix of the nodes in the l-th layer, σ is the activation function (such as ReLU), and D is the degree matrix. ii =∑ j A ij W inter It is the interaction weight matrix between consumers, W feat It is the feature weight matrix;

[0142] Self-attention mechanism: To enhance the importance of different consumer characteristics, a self-attention mechanism is introduced for feature weighting, specifically including:

[0143] Self-attention mechanism: e ij =LeakyReLU(a T [WH i WH j ]);

[0144] Normalization of attention coefficient:

[0145] Update node characteristics:

[0146] Among them, e ij Here, is the attention coefficient between consumers i and j, is the attention vector, ‖ denotes the vector concatenation operation, W is the feature transformation matrix, and H... i and H j These are the feature vectors of consumers i and j, respectively. It is the set of neighboring nodes of consumer i;

[0147] Multi-layer stacking and non-linear activation: Multiple graph convolutional layers are stacked and subjected to multiple non-linear activations to extract higher-level features. The calculation formula is as follows:

[0148]

[0149] Feature aggregation and output: Aggregate the features of each node to generate a consumer profile. The calculation formula is as follows:

[0150] Z = Aggregate(H) (L) );

[0151] Aggregate is used for aggregation of mean, sum, or maximum value.

[0152] Behavior prediction: Using the generated consumer profiles, behavior prediction is performed to forecast future behaviors and needs. The calculation formula is as follows:

[0153]

[0154] Where MLP stands for Multilayer Perceptron, and Z represents the aggregated node features;

[0155] Through the above, dynamic updates and real-time predictions of consumer profiles were achieved, significantly improving the accuracy and real-time nature of personalized recommendations, and enhancing the system's intelligence and user experience.

[0156] Data integration includes:

[0157] Data reception: Receives pre-processed consumer review data from multiple platforms;

[0158] Data mapping and standardization: Mapping evaluation data from different platforms to a unified data structure and format to eliminate data differences between platforms;

[0159] Data merging: Merging evaluation data from multiple sources to form a unified, comprehensive dataset;

[0160] The above steps ensured the effectiveness and accuracy of data integration, laying a solid foundation for subsequent intelligent analysis and processing.

[0161] Incremental learning includes:

[0162] Incremental data reception: Receive newly added consumer review data and add it to the comprehensive dataset;

[0163] Feature extraction: Consumer features are extracted from new data using the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm, and the feature matrix is ​​updated. The calculation formula is as follows:

[0164] TF-IDF(t,d,D)=TF(t,d)×IDF(t,D);

[0165]

[0166] Where TF(t,d) is the term frequency of word t in document d, and IDF(t,D) is the inverse document frequency of word t in document set D;

[0167] Model Update: The consumer profile model is dynamically updated using incremental learning algorithms, specifically including:

[0168] Weight initialization: Initialize the weights of the newly added data feature vectors;

[0169] Incremental training: Incremental training is performed using gradient descent to update the model weights. The calculation formula is as follows:

[0170]

[0171] Among them, w new It is the updated weight, w old These are the original weights, and η is the learning rate. It is the gradient of the loss function;

[0172] Model fusion: The updated model weights are fused to form a new consumer profile model. The calculation formula is as follows:

[0173] w final =αw old +(1-α)w new ;

[0174] Among them, w final These are the weights after fusion, and α is the weight fusion coefficient;

[0175] The above ensures that the consumer profile model can be dynamically updated and reflect the latest consumer behavior and needs in real time, thereby improving the accuracy and timeliness of personalized recommendations.

[0176] Behavioral prediction includes:

[0177] Input features: Extract the consumer's feature vector from the feature matrix;

[0178] Forward propagation: Calculates predictions of future consumer behavior using a new consumer profiling model. The formula is as follows:

[0179] H (l+1) =σ(D -1 / 2 AD -1 / 2 H (l) W inter +H (l) W feat );

[0180] Among them, H (l) Here, D is the node feature matrix of layer l, D is the degree matrix, A is the adjacency matrix, and W is the node feature matrix of layer l. inter It is the interaction weight matrix, W feat It is the feature weight matrix, and σ is the activation function;

[0181]

[0182] Among them, H (L) It is the node feature matrix of the final layer; MLP is a multilayer perceptron.

[0183] Loss Calculation: Calculate the loss between the predicted value and the actual value. The calculation formula is as follows:

[0184]

[0185] Where y is the actual value, This is the predicted value, and n is the number of samples;

[0186] By following the steps above, the accuracy and reliability of predictions are improved, the effectiveness of personalized recommendations and marketing strategies is enhanced, and the user experience and system intelligence are increased.

[0187] The evaluation content analysis module includes:

[0188] Sentiment Analysis: The preprocessed evaluation data is classified according to sentiment. Sentiment analysis algorithms are used to identify positive, negative, and neutral ratings. The calculation formula is as follows:

[0189]

[0190] Where Sentiment(d) represents the sentiment score of document d, and vec(w i ) indicates the word w i The word vectors, sentiment(w i ) indicates the word w i Emotional weighting;

[0191] Topic Identification: The main topics in the evaluation data are identified using the Latent Dirichlet Allocation (LDA) model, extracting the hot topics of consumer interest. The calculation formula is as follows:

[0192]

[0193] Where θ represents the document-topic distribution, z represents the topic distribution, w represents the words, and α and β are the hyperparameters of LDA;

[0194] Trend Analysis: An Autoregressive Integrated Moving Average (ARIMA) model was used to perform time-series analysis on the evaluation data to identify trends in consumer behavior and demand. The calculation formula is as follows:

[0195]

[0196] Among them, y t Here is the predicted value for time t, and c is a constant term. It is the autoregressive coefficient, θ iIt is the moving average coefficient, ∈ t It is an error term;

[0197] The above content provides in-depth and accurate consumer insights, which strongly supports intelligent response generation and personalized recommendations, significantly improving the system's intelligence level and user experience.

[0198] The semantic understanding and question identification module includes:

[0199] Semantic parsing: The results of content analysis are semantically parsed using a dependency parsing algorithm to identify grammatical relations and dependency structures in the sentence. The calculation formula is as follows:

[0200] Score(T) = ∑ (h,m)∈T weight(h,m);

[0201] Where T represents the dependency tree, h and m represent the head node and modifier of the word respectively, and weight(h,m) represents the dependency weight between them;

[0202] Problem identification: Named entity recognition and keyword extraction algorithms are used to identify the problems and needs that consumers care about.

[0203] Named entity recognition algorithms use Conditional Random Field (CRF) models to identify entities and information in the evaluation. The calculation formula is as follows:

[0204]

[0205] Where y is the label sequence, x is the observation sequence, and λ is the number of labels. k These are model parameters, f k Z(x) is the characteristic function, and Z(x) is the normalization factor.

[0206] The keyword extraction algorithm uses TF-IDF and TextRank algorithms to extract keywords, identifying the core issues and needs in the evaluation. The calculation formula is as follows:

[0207] TF-IDF(t,d,D)=TF(t,d)×IDF(t,D);

[0208] Where TF(t,d) is the term frequency of word t in document d, and IDF(t,D) is the inverse document frequency of word t in document set D;

[0209]

[0210] Among them, S(V i ) is node V i The fraction, In(V) i ) is a pointer to node Vi The set of nodes, Out(V) j ) is from node V j The set of nodes is specified, and d is the damping coefficient;

[0211] The above ensures that the system can deeply analyze the semantic structure of consumer reviews and identify the issues and needs that consumers care about, providing an important basis for subsequent intelligent response generation. Through semantic parsing and issue identification, the semantic understanding and issue identification module can fully understand consumer behavior and needs, improving the system's intelligence level and user experience.

[0212] The intelligent reply generation module includes:

[0213] Template matching: Selects a template from a predefined response template library using similarity calculation. The calculation formula is as follows:

[0214]

[0215] Where T is the template vector, Q is the question vector, and t i and q i These are the template and the feature values ​​of the problem, respectively.

[0216] Natural Language Generation: A sequence-to-sequence (Seq2Seq) model based on an attention mechanism is used to generate a natural language response based on the extracted evaluation information and recognition results. The calculation formula is as follows:

[0217]

[0218] e ij =a(s i-1 ,h j );

[0219] Where x is the input sequence (evaluation information and recognition result), y is the output sequence (response content), and P(y t |y 1:t-1 (x) is the word y generated at time step t. t The probability, α ij It is attention weight, e ij It is the attention scoring function, s i-1 It's the decoder state, h j It is the encoder output;

[0220] The above ensures that the system can automatically generate natural and targeted responses based on consumer reviews and identified needs. Through template matching and natural language generation, the intelligent response generation module can efficiently provide personalized customer service responses, improving user experience and the system's intelligence level.

[0221] The multilingual support and human-computer collaboration module includes:

[0222] Multilingual translation: Employing an attention-based sequence-to-sequence model to achieve translation between multiple languages;

[0223] Multilingual sentiment analysis and response generation: performing sentiment analysis and response generation on translated evaluation content;

[0224] Manual review and intervention interface: Provides an interface to support users in reviewing, editing, and confirming the generated response content;

[0225] Through the above features, users can modify and optimize automatically generated responses, improving response quality and user satisfaction. Multilingual support and human-computer collaboration modules enhance the system's flexibility and intelligence, adapting to the needs of users worldwide.

[0226] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

Claims

1. A consumer review content intelligent analysis and response system based on artificial intelligence technology, characterized in that: It includes modules for data acquisition and preprocessing, dynamic updating and prediction of consumer profiles, evaluation content analysis, semantic understanding and problem identification, intelligent response generation, and multilingual support and human-computer collaboration. The data acquisition and preprocessing module collects consumer review data from multiple platforms and preprocesses the collected review data, including cleaning, deduplication, word segmentation, and syntactic analysis. The consumer profile dynamic update and prediction module constructs and dynamically updates the consumer profile model based on preprocessed evaluation data, and predicts its future behavior and needs in real time, specifically including: Model building: Constructing consumer profile models using historical evaluation data; Data integration: Receive and integrate pre-processed consumer review data from multiple platforms to form a comprehensive dataset; Incremental learning: The incremental learning algorithm is applied to train the comprehensive dataset and dynamically update the consumer profile model; Behavioral prediction: Using updated consumer profile models, predict consumers' future behavior and needs in real time; Consumer profile storage: Storing dynamically updated consumer profiles in a database; The evaluation content analysis module performs content analysis on the predicted future consumer behavior and needs, as well as the preprocessed evaluation data, including sentiment analysis, topic identification, and trend analysis. The semantic understanding and problem identification module performs semantic parsing on the results of content analysis, extracts evaluation information, and identifies the problems and needs that consumers care about. The intelligent reply generation module automatically generates reply content based on a predefined reply template library and a natural language generation algorithm, utilizing extracted evaluation information and the identification results of consumer concerns and needs. The multilingual support and human-computer collaboration module enables the analysis and response generation of evaluation content in multiple languages, and provides an interface for manual review and intervention, allowing users to modify and optimize the automatically generated response content.

2. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 1, characterized in that, The data acquisition and preprocessing module includes: Data collection: Real-time collection of consumer review data from multiple platforms; Data cleaning: Cleaning the collected evaluation data to remove noise and irrelevant information; Data deduplication: Cosine similarity is used to detect and delete duplicate data. The calculation formula is as follows: Where A and B represent two text vectors; Word segmentation: A word segmentation algorithm based on bidirectional maximum matching is used to segment the evaluation data. The calculation formula is as follows: BMM(S)=max BMM (S1,S2,...,S n ); Where S represents the sentence to be segmented, S1, S2, ..., S n Indicates the result of word segmentation; Syntax analysis: Dependency parsing algorithm is used for syntactic structure analysis. The calculation formula is as follows: Score(T)=∑ (h,m)∈T weight(h,m); Where T represents the dependency tree, h and m represent the head node and modifier of the word, respectively, and weight(h,m) represents the dependency weight between them.

3. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 1, characterized in that, The consumer profiling model employs a graph neural network, which includes: Graph Construction and Initialization: The consumer review data is constructed into a graph structure, where nodes represent consumers, edges represent relationships between consumers, the node feature matrix X represents the feature vector of each consumer, and the adjacency matrix A represents the relationships between consumers. The initial node feature matrix is ​​as follows: H (0) =X; Graph Convolutional Layer: Introduces an interaction weight matrix W between consumers. inter and feature weight matrix W feat The calculation formula is: H (l+1) =σ(D -1 / 2 AD -1 / 2 H (l) W inter +H (l) W feat ); Among them, H (l) σ is the feature matrix of the nodes in the l-th layer, σ is the activation function, and D is the degree matrix. ii =∑ j A ij W inter It is the interaction weight matrix between consumers, W feat It is the feature weight matrix; Self-attention mechanism: Introducing a self-attention mechanism for feature weighting, specifically including: Self-attention mechanism: e ij =LeakyReLU(a T [WH i WH j ]); Normalization of attention coefficient: Update node characteristics: Among them, e ij Here, is the attention coefficient between consumers i and j, is the attention vector, ‖ denotes the vector concatenation operation, W is the feature transformation matrix, and H... i and H j These are the feature vectors of consumers i and j, respectively. It is the set of neighboring nodes of consumer i; Multi-layer stacking and non-linear activation: Multiple graph convolutional layers are stacked and subjected to multiple non-linear activations to extract features. The calculation formula is as follows: Feature aggregation and output: Aggregate the features of each node to generate a consumer profile. The calculation formula is as follows: Z=Aggregate(H (L) ); Aggregate is used for aggregation of mean, sum, or maximum value. Behavior prediction: Using the generated consumer profiles, behavior prediction is performed to forecast future behaviors and needs. The calculation formula is as follows: In this context, MLP stands for Multilayer Perceptron, and Z represents the aggregated node features.

4. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 3, characterized in that, The data integration includes: Data reception: Receives pre-processed consumer review data from multiple platforms; Data mapping and standardization: Mapping evaluation data from different platforms to a unified data structure and format to eliminate data differences between platforms; Data merging: This involves merging evaluation data from multiple sources to form a unified, comprehensive dataset.

5. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 4, characterized in that, The incremental learning includes: Incremental data reception: Receive newly added consumer review data and add it to the comprehensive dataset; Feature extraction: Consumer features are extracted from new data using the TF-IDF algorithm, and the feature matrix is ​​updated. Model Update: The consumer profile model is dynamically updated using incremental learning algorithms, specifically including: Weight initialization: Initialize the weights of the newly added data feature vectors; Incremental training: Incremental training is performed using gradient descent to update the model weights. The calculation formula is as follows: Among them, w new It is the updated weight, w old These are the original weights, and η is the learning rate. It is the gradient of the loss function; Model fusion: The updated model weights are fused to form a new consumer profile model. The calculation formula is as follows: w final =αw old +(1-a)w new ; Among them, w final α is the weight after fusion, and α is the weight fusion coefficient.

6. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 5, characterized in that, The behavior prediction includes: Input features: Extract the consumer's feature vector from the feature matrix; Forward propagation: Calculates predictions of future consumer behavior using a new consumer profiling model. The formula is as follows: H (l+1) =σ(D -1 / 2 AD -1 / 2 H (l) W inter +H (l) W feat ); Among them, H (l) Here, D is the node feature matrix of layer l, D is the degree matrix, A is the adjacency matrix, and W is the node feature matrix of layer l. inter It is the interaction weight matrix, W feat It is the feature weight matrix, and σ is the activation function; Among them, H (L) It is the node feature matrix of the final layer; MLP is a multilayer perceptron. Loss Calculation: Calculate the loss between the predicted value and the actual value. The calculation formula is as follows: Where y is the actual value, is the predicted value, and n is the number of samples.

7. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 1, characterized in that, The evaluation content analysis module includes: Sentiment Analysis: The preprocessed evaluation data is classified according to sentiment. Sentiment analysis algorithms are used to identify positive, negative, and neutral ratings. The calculation formula is as follows: Where Sentiment(d) represents the sentiment score of document d, and vec(w i ) indicates the word w i The word vectors, sentiment(w i ) indicates the word w i Emotional weighting; Topic Identification: The main topics in the evaluation data are identified using a latent Dirichlet assignment model, extracting the hot topics of consumer interest. The calculation formula is as follows: Where θ represents the document-topic distribution, z represents the topic distribution, w represents the words, and α and β are the hyperparameters of LDA; Trend Analysis: An autoregressive integral moving average model was used to perform time series analysis on the evaluation data to identify trends in consumer behavior and demand. The calculation formula is as follows: Among them, y t Here is the predicted value for time t, and c is a constant term. It is the autoregressive coefficient, θ i It is the moving average coefficient, ∈ t This is the error term.

8. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 7, characterized in that, The semantic understanding and question identification module includes: Semantic parsing: The results of content analysis are subjected to semantic parsing using dependency parsing algorithms to identify grammatical relations and dependency structures in sentences; Problem identification: Named entity recognition and keyword extraction algorithms are used to identify the problems and needs that consumers care about. The named entity recognition algorithm uses a conditional random field model to recognize named entities and information in the evaluation. The keyword extraction algorithm uses TF-IDF and TextRank algorithms to extract keywords and identify the core issues and needs in the evaluation.

9. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 8, characterized in that, The intelligent reply generation module includes: Template matching: Selects a template from a predefined response template library using similarity calculation. The calculation formula is as follows: Where T is the template vector, Q is the question vector, and t i and q i These are the template and the feature values ​​of the problem, respectively. Natural Language Generation: A sequence-to-sequence model based on an attention mechanism is used to generate a natural language response based on the extracted evaluation information and recognition results. The calculation formula is as follows: e ij =a(s i-1 ,h j ); Where x is the input sequence, y is the output sequence, and P(y) t |y 1:t-1 (x) is the word y generated at time step t. t The probability, α ij It is attention weight, e ij It is the attention scoring function, s i-1 It's the decoder state, h j It is the encoder output.

10. The intelligent analysis and response system for consumer reviews based on artificial intelligence technology according to claim 9, characterized in that, The multilingual support and human-computer collaboration module includes: Multilingual translation: Employing an attention-based sequence-to-sequence model to achieve translation between multiple languages; Multilingual sentiment analysis and response generation: performing sentiment analysis and response generation on translated evaluation content; Manual review and intervention interface: Provides an interface to support users in reviewing, editing, and confirming the generated response content.

Images