44 results about "Product reviews" patented technology

There is significant manual work in analyzing product reviews. Even if there are sufficient resources, human reviews can be inconsistent and subjective. A product review analysis platform leveraging engineered prompts and a large language model can address some of these issues. The platform includes a pipeline to summarize reviews, produce sentiment scores to rating categories, detect negative sentiment, extract main categories tags, extract sub-categories tags within a main categories tag, and produce weekly summaries. A dashboard can be included to visualize the enriched reviews. In some cases, synthetic users may fill in data gaps. An action recommendation engine can be included to determine appropriate resolutions. In some cases, the feature vectors generated by the large language model in response to receiving an engineered prompt can be stored in a vector database along with appropriate resolutions, such that incoming reviews can be routed appropriately using the vector database.

Approaches presented herein relate to performing of sentiment analysis on various types of content, such as product reviews. The resulting sentiment data can be provided for various uses, such as to allow for sentiment-based search or to make sentiment-based recommendations. An example system collects and processes product reviews from various sources. A first language model (such as an LLM or VLM) may be used to analyze a review to generate a summary and perform sentiment analysis. A second language model may be used to perform further analysis based on the sentiment data to infer correlation between comments and reviews and an influence of the review and the comments. Such an approach can analyze the influence of user comments on subsequent reviews from the same commentator, top concerns and issues highlighted by the commentator, inherent bias, features evaluated, etc. The generated sentiment data and associated timestamp data can be stored and indexed in a database for subsequent retrieval or analysis.

The invention discloses a multi-type context-aware dialogue recommendation method based on a hybrid expert model. The aim of the invention is fulfilled by a hybrid expert framework comprising a plurality of expert module coordination systems. The knowledge graph expert model is responsible for extracting and coding structured entity relationship information, the dialogue expert model is used for deeply mining key contents and user intentions in dialogues, and the comment experts are used for analyzing user emotion and preference information in commodity comments. In this way, the system can make full use of semantic information of the structured data and the unstructured data, and the problem of semantic gaps caused by data heterogeneity in the prior art is solved. On the basis, a coordination system is introduced to serve as a core module for coordination and integration. The coordination system can dynamically allocate the output of the expert module according to the weights and contributions of different context information to generate a final recommendation result, so that efficient fusion of multiple types of context information is realized, the problems of semantic alignment and inconsistency between different types of data are solved, and the performance of the dialogue recommendation method is improved.

The invention provides a product comment sentiment analysis method based on MS-PSO and deep learning, and belongs to the field of sentiment analys.The method comprises the steps that a product comment text and sentiment classification are obtained, and text data are preprocessed; the preprocessed text is converted into a dynamic word level representation tensor through DistilBERT, and an emotion classification label is converted into an integer code; hyper-parameters of the mixed feature deep learning network are optimized and connected in series through a random mirroring particle swarm algorithm; according to the optimal hyper-parameter, training to obtain a parallel-series mixed feature deep learning network model; preprocessing text data to be subjected to sentiment classification, encoding the preprocessed text data through a DistilBERT model, and inputting the encoded text data into the trained model to obtain sentiment classification; according to the method, the product comment sentiment analysis precision can be improved, and a basis is provided for enterprises to know user requirements and improve products.

The invention belongs to the technical field of natural language processing, and discloses a commodity comment-oriented user personalized preference modeling method based on comparative learning, which comprises the following steps of: constructing a corpus and carrying out data preprocessing; performing data enhancement on the documents to obtain paired document pairs; performing theme distribution on the paired documents through LLM, and inputting a result into vector representation of theme distribution of the documents; performing theme distribution on the paired documents through LLM to obtain document-theme distribution, and inputting the user index into a user inference network to obtain user preference distribution; performing point multiplication on the document-theme distribution and the user preference distribution to obtain personalized document-theme distribution; sampling prior distribution from the Dirichlet distribution; and the prior loss of Dirichlet prior distribution alignment and the contrast learning loss are minimized, so that the user inference network is trained. According to the invention, the preference of the e-commerce platform user can be effectively analyzed.

There are provided systems and methods for intelligent detection and acquisition of authentic product reviews for cross-platform availability. A service provider may provide computing services to merchants and users for processing various interactions, such as purchasing items electronically. When items or other products are purchased, users may leave reviews. To determine an authenticity of the reviews, the service provider may utilize an intelligent system that may include an LLM or other generative AI. The system may automatically generate questions for users that may be designed by the LLM to elicit responses that verify whether a review is authentic and / or relevant. This may include receiving responses and scoring those responses to verify the user is providing an authentic review. If so, the review may be written to a blockchain, which may allow the review to be pushed to users and / or automatically injected to product pages and checkout flows.

The invention discloses a platform risk event intelligent response system and method based on artificial intelligence, and relates to the technical field of risk detection.The method comprises the steps that commodity information published on an e-commerce platform and commodity comment information in historical transactions are collected, and the difference degree of the commodity information and the commodity comment information is calculated; screening target commodities with the difference exceeding the standard, and calculating a risk early warning coefficient in combination with the information modification rate, the complaint rate and the return rate; dividing risk levels by taking the risk early warning coefficient and the difference degree as two-dimensional indexes, matching a corresponding risk response strategy according to the risk levels, constructing a multi-dimensional evaluation system to calculate a response effect coefficient, and dynamically adjusting the risk response strategy; according to the invention, intelligent identification and dynamic response of risk events can be realized, and user rights and interests and platform reputation are guaranteed.

A user device may receive, from a server device, a three-dimensional model of a product, and may display the three-dimensional model of the product, with a product review option, in an augmented reality user interface. The user device may display a product feature review panel, product review features, and product feature hotspots, with the three-dimensional model in the augmented reality user interface, based on selection of the product review option. The user device may receive a review of a product feature, via one of the product feature hotspots associated with the product feature and provided in the augmented reality user interface, and may submit the review of the product feature to the server device.

The invention provides a scientific and technical literature viewpoint sentence recognition method and system based on emotion enhancement, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining a target literature to be subjected to viewpoint sentence recognition; a viewpoint sentence recognition model is constructed, and the viewpoint sentence recognition model comprises a context feature extraction component, a local text feature extraction component and a weighted emotion feature fusion enhancement component; and inputting the target literature into the viewpoint sentence recognition model, and outputting and obtaining a viewpoint sentence recognition result. The technical problems that in the prior art, a viewpoint sentence recognition model mainly focuses on viewpoint sentence mining in the fields of social media, product comments and the like, depends on dominant sentiment words for analysis, viewpoint sentences which are generally lack of dominant sentiment words in the scientific and technical literatures are generally hard to migrate directly, and adaptability to the scientific and technical literatures is insufficient are solved.

The invention discloses a product comment generation method and device. The method comprises the following steps: when a confirmation instruction is received, obtaining user evaluation demand information of a to-be-evaluated product; generating a plurality of initial product comments corresponding to the to-be-evaluated product by using a comment generation module of a product comment generation model according to the user evaluation demand information; a comment scoring module of the product comment generation model is used for scoring each of the plurality of initial product comments to obtain a score value of each of the plurality of initial product comments, and the comment scoring module is a module used for scoring an output result of the comment generation module in the product comment generation model; and selecting the product comment with the highest score from the plurality of initial product comments according to the score as the product comment of the to-be-evaluated product. According to the method and the device, the technical problems that product comments generated by a product comment generation technology in related technologies are greatly different from real user comments, and the reliability is relatively low are solved.

A product review detection method based on reflective chain reasoning belongs to the technical field of natural language processing (NLP). By structuring relevant background information into background feature representations and combining a "reflective chain" reasoning mechanism, the reasoning conclusion is gradually refined. Based on few-shot reasoning, this method can effectively improve the model's detection ability for low-scoring background-dependent cases through the generation and screening evaluation of feature combinations. At the same time, the screening and scoring mechanism of feature combinations can quickly filter out irrelevant information, reduce redundant calculations, and improve detection efficiency. By introducing termination conditions, this method can timely terminate the exploration of irrelevant paths during the reasoning process, thereby further optimizing the use of computing resources. The present invention exhibits stronger accuracy and context adaptation ability, can understand complex semantics more deeply, and significantly improves the ability to capture metaphorical expressions.

The application provides an e-commerce demand analysis method and system based on a large language model, which optimizes product characteristics and market strategies by analyzing user-generated content (UGC). The method includes: first, collecting UGC data such as product reviews and user Q&A from e-commerce platforms. Then, using a large language model to deeply analyze these data, extract product attributes and sentiment information, and convert them into structured data. Next, generate a dual importance matrix at the market level to evaluate the importance of product attributes in purchase decision and post-purchase satisfaction. Further, for the products of the merchant, calculate the actual performance value of the attributes, generate a dual importance-performance matrix at the product level, and identify optimization needs. Finally, the analysis results are visualized and a report is generated to provide market insights and decision support for merchants. This method helps merchants accurately position product optimization direction and improve market competitiveness.

1. Name of the Design Product: Product Review Rating Module of the Graphical User Interface for Electronic Devices. 2. Use of the Design Product: The design product is used for an electronic device. 3. Design Key Points of the Design Product: Lies in the part of the graphical user interface for which protection is requested. 4. Picture or Photograph Most Indicating the Design Key Points: Variation State Diagram. 5. Use of the Graphical User Interface: Overall Use: Interaction in the review area; Local Use: Implement product review rating through changes in user operations, and view reviews of the same product. 6. Other Situations Requiring Explanation - Reference Diagram Explanation: This design protects the product review rating module; if the product reviews in the review area have ratings, as shown in the main view, select the "View Reviews of the Same Product" option below the review, and a floating window will be displayed on the page, allowing you to view all reviews of this product within the entire site, as shown in the variation state diagram; the usage state reference diagrams are the effect diagrams showing this user interface, and the main view and variation state diagram correspond to usage state reference diagrams 1 - 2 respectively.

The present invention discloses a product recommendation method and system; wherein, the method comprises: obtaining user comments and product reviews; inputting the user comments and product reviews into a trained product recommendation model, and outputting product recommendation results; wherein, the working principle of the trained product recommendation model comprises: encoding the user comments and product reviews respectively to obtain user comment encoding results and product comment encoding results; performing feature extraction on the user comment encoding results and product comment encoding results respectively to obtain user comment features and product comment features; performing weighted interactive fusion on the user comment features and product comment features, and extracting a splicing vector of the user comments and the item reviews from the fusion result; performing score prediction on the splicing vector, sorting the products in descending order of the scores, and outputting a product recommendation list.

The invention provides a commodity recommendation method and device, equipment, a medium and a product, and is applied to the technical field of artificial intelligence, and the method comprises the steps: building a knowledge graph of a commodity based on the commodity information of a target type of commodity, the information of a shop to which the commodity belongs, and the emotion information obtained from the comment text of the commodity; based on the knowledge graph of the commodity, respectively obtaining preference representation of the user for the commodity, evaluation representation of the commodity and information vector representation of the commodity; calculating a recommendation score of the commodity based on the preference representation of the user for the commodity, the evaluation representation of the commodity and the information vector representation of the commodity; and recommending the commodity based on the recommendation score of the commodity. According to the method, the commodity recommendation efficiency is improved.

The invention provides a rapid evaluation method for stamping product quality. The rapid evaluation method comprises the steps that stamping quality defects of a product are identified, defective parts, corresponding to the stamping quality defects of the product, of the product are obtained, local sample pieces corresponding to the defective parts of the product are manufactured, and AUDIT quality evaluation of all the local sample pieces is qualified; carrying out defect marking on the local sample; taking the defect part of the product as a review part; and respectively comparing the stamping quality defect of each review part of the product with the stamping quality defect of each corresponding local sample, and evaluating the stamping quality qualification condition of each review part. The method has the advantages that the quality defect change of the review part of the product can be comprehensively and timely reflected, the comparison is more accurate and flexible, and meanwhile, the product quality monitoring labor division is refined; the method has efficient timeliness, can quickly identify defects, carries out sample piece risk prompt, and greatly saves the quality judgment time caused by product quality fluctuation.

The present invention provides a product review sentiment analysis method based on MS-PSO and deep learning, which belongs to the field of sentiment analysis. The method comprises the following steps: obtaining product review text and sentiment classification, and preprocessing text data; converting the preprocessed text into a dynamic word-level representation tensor through DistilBERT, and converting the sentiment classification label into an integer code; optimizing the hyperparameters of a parallel-connected hybrid feature deep learning network through a random mirror particle swarm algorithm; training a parallel-connected hybrid feature deep learning network model based on the optimal hyperparameters; preprocessing the text data to be sentimentally classified, encoding it through a DistilBERT model, and then inputting it into the trained model to obtain sentiment classification. The method can improve the accuracy of sentiment analysis of product reviews, and provide a basis for enterprises to understand user needs and improve products.

The invention discloses a commodity comment generation method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining initial comment information corresponding to a target commodity, generating a prompt word corresponding to the target commodity by using a preset prompt word generation template according to the initial comment information, the commodity information of the target commodity and the user information of the target user; determining a target feature corresponding to the initial comment information based on a preset multi-head attention model, and generating a to-be-confirmed comment corresponding to the target commodity according to the cue word, the target feature and a preset comment generation large model; and obtaining evaluation information generated by the target user based on the to-be-confirmed comment, and determining a target comment corresponding to the target commodity based on the evaluation information. In this way, the commodity comment steps can be simplified, the workload and difficulty of commodity comment writing are reduced, the user is helped to effectively complete commodity comments, and the commodity comment quality is improved.

An object of a system according to an embodiment is to accurately and quickly analyze review information of a product, extract important information, and generate and update a summary.SOLUTION: A system includes a collection part, an analysis part, a summarization part, and an update part. The collection unit collects data of product reviews. The analysis unit analyzes the review information collected by the collection unit using a natural language processing technique. The summarizer extracts important information based on the analysis result obtained by the analyzer, and generates a summary. The updating unit updates the summary generated by the summarizing unit in real time.SELECTED DRAWING: Figure 1

The invention discloses an Amazon commodity score star level prediction method based on a long and short term memory neural network forest. The method comprises the steps of 1, collecting data of a to-be-predicted commodity detail page and data of a commodity comment page of an Amazon platform; 2, extracting webpage label contents from the data of the commodity detail page and the data of the commodity comment page through document analysis, and storing the webpage label contents into a database; step 3, extracting feature data used for constructing a long-short term memory neural network forest model according to webpage label contents; step 4, constructing a long-short term memory neural network forest model for prediction; 5, training and storing the neural network forest model of each to-be-predicted commodity; 6, inputting recent commodity data to be predicted into the neural network forest model to carry out grading star-level prediction; and step 7, carrying out visual display on the prediction result in a chart form. According to the method, prediction of commodity scoring star levels is realized, and commodity sales, inventory management and iteration are better managed and controlled.

A computer-implemented method for counting and extracting opinions in product reviews is provided. The method includes inputting a hypothesis opinion, a product name, and product reviews relating to a product, applying a decontextualization component to the product reviews by using the product name, applying the decontextualization component to the hypothesis opinion by using the product name, applying an entailment model to classify each sentence of the decontextualized product reviews against the decontextualized hypothesis opinion, and outputting one or more sentences classified as entailing the hypothesis opinion and a count of corresponding reviews.

This invention discloses an intelligent response system and method for platform risk events based on artificial intelligence, relating to the field of risk detection technology. The invention collects product information published on e-commerce platforms and product review information from historical transactions, calculating the degree of difference between the two; it filters target products with excessive differences, and calculates a risk warning coefficient by combining information modification rate, complaint rate, and return rate; it uses the risk warning coefficient and the degree of difference as two-dimensional indicators to classify risk levels, and matches corresponding risk response strategies according to the risk levels. Simultaneously, it constructs a multi-dimensional evaluation system to calculate the response effect coefficient, dynamically adjusting the risk response strategy. This invention can achieve intelligent identification and dynamic response to risk events, protecting user rights and platform reputation.

A method for providing product information by an electronic device according to an embodiment of the present disclosure may comprise the steps of: requesting a detailed information page for a product from a server on the basis of a user input; receiving, from the server, the detailed information page including review information on the product, wherein the review information includes one or more purchaser reviews for the product, and the one or more purchaser reviews respectively include video reviews and photo reviews for the product; and in a review area on the detailed information page, in which the review information is displayed, separately providing a first area in which one or more videos included in the respective video reviews and one or more photos included in the respective photo reviews are displayed, and a second area in which a best review among the one or more purchaser reviews is displayed.

This disclosure relates to automatic creation and display of keyword filters useable to filter product reviews on an e-commerce website. One aspect is a method for generating entities for filtering item reviews, the method comprising extracting attributes for an item associated with a set of item reviews, generating a list of entities relevant to the item based on the set of item reviews and the extracted attributes for the item, ranking the list of entities based on a frequency of terms related to each entity in the set of item reviews, selecting at least one entity from the list of entities based on the ranking, and associating the at least one entity with the item.

Disclosed by the present disclosure are a method and apparatus for displaying product comment information, an electronic device and a storage medium. The method for displaying product comment information is executed by a server and specifically comprises: acquiring product comment information of a user, the product comment information comprising comment text; inputting the comment text into a first model established in advance for error correction processing; determining whether the product comment information further comprises a comment image; if the product comment information comprises a comment image, then inputting the error-corrected comment text and the comment image together into a second model established in advance to detect the consistency of the image and the text, and when the image and the text are detected to be consistent, saving the product comment information in a display list; if the product comment information does not comprise a comment image, then directly saving the error-corrected comment text in the display list; and displaying the product comment information in the display list to the user.

The present invention discloses a cross-domain sentiment analysis method and a storage medium based on knowledge transfer. To solve the problem of difficult data annotation, it is usually necessary to transfer knowledge from other domains to the target domain. There are many ways of transfer, including model-based, instance-based, and relationship-based, which have been greatly developed in the context of deep learning. However, in the process of transfer, the role of external knowledge bases is always ignored. Therefore, trying to use the knowledge of external knowledge bases to enhance the effect of transfer learning has become a new method. The method of the present invention includes: constructing a semantic network module, a feature extraction module, and a domain adversarial training module. Process five product review data sets of Amazon, extract some words with relatively high frequencies of occurrence and connect them to ConceptNet to form a semantic network. Then use RGCN to process the semantic network and extract feature representations. Finally, add domain adversarial training to improve the robustness of the model.

The embodiment of the invention provides a product review method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: receiving a demand instruction, and screening out a target product file from a plurality of to-be-matched product files based on demand information carried by the demand instruction; the plurality of to-be-matched product files are in one-to-one correspondence with the plurality of three-dimensional models; based on a preset review rule corresponding to the demand instruction, determining a first type of review result corresponding to the target product file; displaying a three-dimensional model corresponding to the target product file through at least one review terminal, and obtaining a second type of review result corresponding to the target product file from the review terminal; the review terminal is VR equipment; and determining a review result of the target product file in combination with the first type of review result and the second type of review result. The method is used for achieving the effects of improving the design communication efficiency, reducing potential risks and ensuring the rationality and feasibility of a design scheme.

A language model-based method for extracting a product review keyword includes collecting, on the basis of information for specifying a product, review data associated with the product; using a language model so as to generate, on the basis of a plurality of predetermined questions, at least one piece of response data from at least some pieces of the review data; and extracting, on the basis of the at least one piece of response data, a review keyword associated with the product.