120 results about "Product reviews" patented technology

Methods, for facilitating electronic commerce using a social network, including: registering users on a social-network product-review site, wherein each user can be connected to other users by designating the other users as friends; identifying product purchases, of the users, made in merchant locations; collecting purchase details, and associated-user identities; sending a notification to an associated user requesting a user-specific product review from the associated user upon the associated user receiving the purchase details associated with the associated user; obtaining the user-specific product review and permissions from the associated user, wherein the permissions designate to share the user-specific product review and a relevant associated-user identity with associated-user friends; determining product-related information being accessed in various locations by a product-seeking user; ascertaining an identity of the product-seeking user; and presenting related-product reviews, based on the product-related information and related permissions, of user-related friends to the product-seeking user in the various locations.

Methods, systems, and apparatuses for generating and providing review information for products are described. Product reviews for a product are collected from multiple websites over the Internet. The product reviews may be collected in any manner, such as by crawling the Internet to collect product review information for the product. Review information may be collected for multiple versions/releases of the product. Websites, RSS feeds, consumer reports, and other Internet sources may be parsed for product reviews for the product. Product reviews and product review ratings received from multiple websites may be weighted and normalized into common form. Product reviews may be weighted based on a reputation of the reviewers who submitted them. Product reviews may also be filtered based on time of submission. One or more summary ratings for the product are generated based on the collected product reviews. The summary ratings are displayed.

A networked system collects, aggregates, and distributes the collective wisdom of consumers. An aggregation module obtains, from each of a plurality of reviewing consumers: i) a file comprising a motion video clip depicting a video product review of a product; and ii) the reviewing consumer's indication of at least one categorization data element identifying the product. A motion video clip database stores each motion video clip. A search database stores, for each motion video clip, the at least one product categorization data element identifying the product depicted in the motion video clip. A search module receives a search term from a searching consumer and identifies a selected group of video product reviews. The selected group of video product reviews comprises those video product reviews that are a relevant video product review. A relevant video product review comprises a video product review that is associated with a product categorization data element corresponding to the search term. A monetization module debits a first predetermined value from a merchant's account upon each occurrence of a target event. The target event may comprise the searching consumer initiating interaction with a link associated with web content comprising additional information about the product depicted in the video product review.

A system and method for determining quality of written product reviews to distinguish the user reviews for further use or processing. In one embodiment, the opinion analyzer system includes a processor, a parser module, an prose analyzer module, a characteristic features database, and a language value rules database. In another embodiment, the method comprises the steps of parsing language of a portion of a user review, extracting characteristic feature from the user review, determining a quality parameter based on the extracted characteristic feature, determining a raw score based on the quality parameter, and determining quality of the user review based on the raw score.

A method, system and computer program product for providing relevant product reviews to a user. A user's session on a website is monitored to gather information related to reviewing and writing product reviews as well as to buying and browsing habits. The actions performed by the user in connection with the monitored user session are populated in a database. Once populating the database is completed, the personality report of the user, which includes personality traits, is generated using the populated database. Also, a list of reviewers that are favored by the user is generated. By using the personality traits of the user to match reviews of interest as well as providing reviews from those reviewers favored by the user, the user can have more confidence in the usefulness or relevance of these reviews.

A method, system, computer program product, and device for providing product information, such as, for example, product price comparison information, product reviews, retailer information, retailer reviews, and information about similar products, to a user utilizing a mobile data processing system is provided. In one embodiment, a user, using a product code reader, such as, for example, an RFID reader, reads a product code from a tag, such as, for example, an RFID tag, located proximate to a product. The product code information determined from the product code is transmitted to a server. The server receives the product code information and retrieves the product information from a database. The server then formats the product information into, for example, a web page, and transmits the web page back to the mobile data processing system which receives the web page and presents the web page to the user.

The invention belongs to the data mining and natural language processing technical field, specifically a product comment analyzing method and system with learning supervising function. According to the method, multiple categories (product features) are artificially defined aiming at a specific product; firstly, product feature aspect classification is carried out to collected user comments by a machine learning and training classifier successively; then emotion analysis is carried out to the comment texts classified by a training classifier; and finally quantitative comments on each feature of the product are summarized for a user through comprehensively counting the product features related to a great deal of comment texts and corresponding emotional tendencies. The method and the system of the invention are relatively efficient, rapid, simple and convenient; the classified contents are all user concerned contents; the analysis result is provided to the user visually and clearly; and the work of viewing a great deal of comments can be removed.

Consumer-generated product reviews are a mainstay of electronic commerce and a key factor in the consumer decision process. A shopper's typical online purchase decision can involve tedious reading of multiple reviews written by previous purchasers in order to find and compare products for purchase. Disclosed herein is an automated system, and associated methods, that identifies the product attributes discussed in consumer reviews and summarizes consumer sentiment for each attribute for each product thus, offering immense value to consumers and retailers alike.

A method for operation of a search and recommendation engine via an internet website is described. The website operates on a server computer system and includes accepting text of a product review or a service review, initializing a set of words with seed words, predicting meanings of the words in the set of words based on confidence scores inferred from a graph and using the meanings of the words to make a recommendation for the product or the service that was a subject of the product review or the service review. The search and recommendation engine is also described.

A searching method in the Internet that correlates publicly available product reviews for products by initially predetermining a set of review terms indicative of a favorable review and also predetermining a set of review terms indicative of an unfavorable review. From a requesting station, databases in the Internet are searched for the product reviews as follows. Product reviews are distinguished from other documents mentioning the product that may also be in the searched databases. Each distinguished product review is then analyzed using the predetermined review terms indicative of favorable reviews and predetermined review terms indicative of unfavorable reviews. An overall determination is made as to whether each individual product review was favorable, unfavorable or balanced.

Systems and methods for clustering user reviews are disclosed in which a plurality of user reviews are extracted from electronic documents. The electronic documents contain user reviews of a plurality of items of interest. A set of user reviews is identified in the plurality of user reviews as being associated with the same item of interest in the plurality of items of interest. Item identifying information included in the electronic documents is used for this identification. The set of user reviews is then associated with the same item of interest. Examples of item identifying information include unique product identifiers, brand names, model numbers, and category information. In some instances, the item identifying information is extracted from metadata included in the electronic document. In some instances, the electronic documents are obtained from e-commerce websites or product-review websites.

A method and system for automatically ranking product reviews according to review helpfulness. Given a collection of reviews, the method employs an algorithm that identifies dominant terms and uses them to define a feature vector representation. Reviews are then converted to this representation and ranked according to their distance from a ‘locally optimal’ review vector. The algorithm is fully unsupervised and thus avoids costly and error-prone manual training annotations. In one embodiment a Multi Layer Lexical Model (MLLM) approach partitions the dominant lexical terms in a review into layers, creates a compact unified layers lexicon, and ranks the reviews according to their weight with respect to unified lexicon, all in a fully unsupervised manner. When used to rank book reviews, it was found that the invention significantly outperforms the user votes-based ranking employed by Amazon.

The invention is a system and method for evaluating written work-product which includes several categories in which work-product is rated using a unique set of Levels and categories. An exemplary embodiment of the rating scheme includes a rating scale of integers from 1 to 5, inclusive, in which rating below 3 is considered unacceptable. After a particular piece of work-product is reviewed, another piece of work-product produced by the same person may also be reviewed. Typically, each review is performed by two reviewers. In the event that two reviewers disagree as to the rating of a particular work-product item, a third reviewer, or arbitrator, is used to rate the work-product again to determine a rating for the product. Each creator of work-product may have two or more items of their work-product reviewed to determine the overall quality of the creator's work.

The invention discloses a CRFs (conditional random fields) and SVM (support vector machine) based method for extracting fine-granularity sentiment elements in product reviews. The method comprises the steps as follows: a, a CRFs model is adopted, review language characteristics are taken as sequences, then position labelling is performed on review languages according to the sequences, corresponding rules are adopted to perform stratified filtering on error labels, and extraction for sentiment subjects and sentiment words is finished; and b, an SVM model is adopted to perform sentiment orientation analysis on word pairs according to the extracted sentiment subjects and sentiment words as well as introduced sentence structure features. According to the invention, the sentiment subjects and the sentiment words in review sentences are extracted together, further, sentiment classification accuracy in the sentiment orientation analysis is improved, so that the sentiment element extraction and sentiment judgment are improved, and F value is up to 76.3%; due to introduction of word meaning codes, the generalization ability and the robustness of a system are improved by virtue of the word meaning codes, and the accurate rate and recall rate of review result analysis are greatly improved.

Review bias identification systems and methods are presented. A bias in one or more review elements can be identified by deriving a measure of how a review outlet's product review deviates from an industry average or composite review of the product. A bias engine generates a bias vector for a review outlet where the vector can include multiple bias metrics associated with one or more product properties. The bias engine can further present one or more recommendations of associating the product with a review outlet based on the bias vector.

The invention discloses a product review attribute-level emotion classification method based on rules and neural networks. The method includes the steps: firstly, acquiring review data and filtering Chinese participles and stop words from a review text; secondly, screening a product attribute set by the aid of a rule template, building a <attribute and review> sample set, performing emotion tagging on the attribute of each review, and building a <attribute, review and emotion> training set; building a neural network emotion classification model based on bilateral attention, and training the model by the aid of the training set; finally, filtering Chinese participles and stop words from testing data, screening a product attribute set, building a <attribute and review> testing set, and performing emotion classification by the aid of an emotion classification model. According to the method, attribute emotion category forecasting accuracy can be greatly and effectively improved by the aidof context information of attributes in the reviews.

The invention discloses an emotion analysis method for Chinese texts based on computer information processing technology. Comments on Chinese products are subjected to word segmentation. By utilizing a bag-of-words model, vector representations of product comments are generated. The vector of every comment is inputted to a visible unit of a limited Boltzmann machine (RBM) in deep learning. Sentimental characteristics of Chinese texts are extracted by the RBM and the extracted emotional characteristics are inputted to a SVM for text emotion classification. The emotion analysis method for the Chinese texts based on computer information processing technology is capable of improving relevance of emotional semantics of characteristics while the SVM is capable of improving accuracy of emotion classification of comments on Chinese products.