37 results about "Movie reviews" patented technology

A system for providing interactive television program guide features and other features and information related to a specific user interest or programming category in niche hubs is provided. All of the television programming features provided by user television equipment that relate to a specific user interest or programming category may be accessed from the niche hub. For example, a movie lovers niche hub may provide programming features such as television program listings for movies, video-on-demand listings for movies, pay-per-view listings for movies, web site links related to movies, movie-related merchandise, movie news groups, movie chat groups, movie e-mail clubs, movie contests, movie trivia questions, movie actor interviews, movie reviews, movie channel package ordering, etc. The programming features of the niche hubs may be transmitted from a server, database, or other storage facility via a television distribution facility. User television equipment may be connected via two-way communications paths to transmit messages to each other.

The invention discloses a text classification method combining dynamic word embedding with part-of-speech tagging, and provides the text classification method based on a deep neural network through combining dynamic word embedding with part-of-speech tagging. The method can fully utilize the advantages that a large-scale corpus can provide more accurate grammar and semantic information, and can also adjust word embedding by combining with the features of the corpus during a model training process, and thus the features of the corpus can be better learned. Meanwhile, classification accuracy can be further improved by combining with part of speech information of words in sentences. The invention also comprehensively utilizes the advantages of LSTM in the aspect of learning context information of words and part of speech in the sentences, and the advantages of CNN in the aspect of learning text local features. The classification model provided by the invention has the advantages of high accuracy and strong universality, and achieves good effect in some famous public corpuses including IMDB corpus, Movie Review and TREC.

A system for providing interactive television program guide features and other features and information related to a specific user interest or programming category in niche hubs is provided. All of the television programming features provided by user television equipment that relate to a specific user interest or programming category may be accessed from the niche hub. For example, a movie lovers niche hub may provide programming features such as television program listings for movies, video-on-demand listings for movies, pay-per-view listings for movies, web site links related to movies, movie-related merchandise, movie news groups, movie chat groups, movie e-mail clubs, movie contests, movie trivia questions, movie actor interviews, movie reviews, movie channel package ordering, etc. The programming features of the niche hubs may be transmitted from a server, database, or other storage facility via a television distribution facility. User television equipment may be connected via two-way communications paths to transmit messages to each other.

Systems and methods are provided for communicating filtered event program information and broadcasting user searchable event program information to vehicles. For example, there is provided a system for broadcasting event program information to at least one vehicle that is filtered according to the vehicle's current location. In one embodiment, the system comprises one or more antennas, a broadcast receiver unit having a data filter, and a navigational device that displays event program information by venue within a set distance from the vehicle's current location. The display further comprises multi-leveled sub-menus that provide a user with a variety of movie related information such as screening times, cast listings, movie reviews, movie trailers, etc. The system further comprises a memory buffer unit in the receiver unit that stores the vehicle's last reported location data and cross-referenced databases of movie program information periodically updated so as to minimize bandwidth requirements of the system.

Systems and methods are provided for communicating filtered event program information and broadcasting user searchable event program information to vehicles. For example, there is provided a system for broadcasting event program information to at least one vehicle that is filtered according to the vehicle's current location. In one embodiment, the system comprises one or more antennas, a broadcast receiver unit having a data filter, and a navigational device that displays event program information by venue within a set distance from the vehicle's current location. The display further comprises multi-leveled sub-menus that provide a user with a variety of movie related information such as screening times, cast listings, movie reviews, movie trailers, etc. The system further comprises a memory buffer unit in the receiver unit that stores the vehicle's last reported location data and cross-referenced databases of movie program information periodically updated so as to minimize bandwidth requirements of the system.

The invention discloses a movie recommendation method and system based on an improved deep belief network, and the method comprises the steps: obtaining the weight of each movie according to the moviecomment record of a target user, the total number of comments of each movie and the number of users with the same comment record as the target user, and constructing a comment feature vector of the target user; calculating comment similarity according to the comment feature vector of the target user, and constructing a similar user set for the users whose comment similarity is higher than a threshold; constructing a deep belief network by adopting a knowledge migration method, and optimizing the deep belief network model based on a partial least square method to obtain a trained deep belief network; and inputting the movie comment records of the target user and the similar user set into the trained deep belief network, and outputting a movie recommendation result. The recommendation system is combined with deep learning, personalized movie recommendation is made for the target user by efficiently extracting historical comment data of the user on the movie, and the recommendation quality of the recommendation system is improved.

The embodiment of the invention provides a short English movie review classification method and device, and the method comprises the steps: obtaining a feature word set of a to-be-classified short English movie review text; performing prediction classification on the feature word set through a pre-trained first-layer classification model to obtain a first prediction result set, splicing the firstprediction result set, and taking arithmetic mean to obtain a new test set; predicting the new test set through a pre-trained second-layer classification model to obtain a second prediction result set, and minimizing the second prediction result set; performing maximum normalization processing to obtain a normalized result set; and processing the normalized result set by adopting a simple majoritymethod to obtain a classification result of the to-be-classified short English movie review. According to the technical scheme of the invention, the accuracy and robustness of the classification result are greatly improved.

The invention relates to a movie user portrait construction method, which comprises the following steps of 1, selecting users who have published Chinese movie comments in a movie community website, and collecting static data and dynamic data of the users; 2, constructing a three-layer label system of a movie user portrait according to the obtained multi-dimensional data of the sample movie user; 3, according to the corresponding relation between the multi-dimensional data of the movie users and the labels in the label system, the first-layer labels and the second-layer labels of the movie users are predicted from bottom to top according to the label hierarchy, and a perfect single-user portrait model is constructed; and step 4, carrying out movie preference analysis on the group movie users with a certain type of common characteristics, generating a third-layer label of the movie user portrait, and constructing the group user portrait. According to the invention, the original data of the user is analyzed and mapped to generate the label system, so that the attribute labeling of the movie user and the construction of the group user portrait model of the crowd with the same attributeare realized.

The invention discloses a movie review emotion visualization method for box office prediction, and the method comprises the steps: collecting movie information and movie review data, and carrying outthe preprocessing of the collected data; for the movie review data, using a word segmentation vector method to extract movie review emotion types and intensity features; designing movie review emotionvisual codes to represent emotion types and intensities; designing movie review emotion space-time distribution; and film review emotion visual layout and box office prediction visual layout are realized. According to the method, the defects of lack of data attribute association analysis and lack of comprehensive analysis of film review emotion types and intensities in the current situation are overcome, and the film review emotion types and intensities are coded and mapped by using visual visual elements and flexible interaction.

The invention provides a neural collaborative filtering model recommendation method based on lambda Mart, and the method comprises the following steps: S1, inputting user information which comprises user basic information and movie comment information, and the movie comment information comprises scored movie information and unscored movie information; S2, enabling the embedding layer to map the user information into a user feature vector, and mapping the movie comment information into a movie feature vector; S3, inputting the user feature vector and the movie feature vector into a neural collaborative filtering model, extracting high-order feature information, and extracting sorting information at the same time; and S4, processing the high-order feature information and the sorting information to obtain a recommendation result, and outputting the recommendation result. According to the method, an LMNCF model is put forward, a neural collaborative filtering model is improved, implicit high-order feature information is extracted through nonlinear feature processing of a multi-layer perceptron, sorting information is extracted through a lambda Mart personalized sorting algorithm, and recommendation is more accurate.

The invention discloses a movie comment sentiment analysis method based on document vectors. The method comprises the following steps: firstly, carrying out data cleaning and preprocessing on movie comments to construct an emotion analysis data set; and training an improved document vector model by using comments of the movie and scores corresponding to the comments; inputting the movie comments into an improved document vector model to obtain word vectors and document vectors of the movie comments, averaging the word vectors corresponding to the movie comments, and splicing the word vectors with the document vectors to generate feature vectors; and finally, performing emotion classification on the movie comments by using the feature vectors generated based on the movie comments and the score training classification model corresponding to the movie comments. By using the improved document vector generation method, the movie comment sentiment classification accuracy is improved.

The invention relates to a movie recommendation system for relieving data sparsity. The movie recommendation system comprises an initial data input module and a recommendation list module, the initialdata input module comprises movie introduction data, movie comment data, actual scoring data and theater group personnel information data; the movie introduction data and the actual scoring data areprocessed by an improved convolutional neural network and then transmitted to a recommendation list module; establishing a mathematical model according to the theater group personnel information data,and embedding the mathematical model into the recommendation list module; and the movie comment data is processed through an algorithm and then transmitted to a recommendation list module. The invention provides a movie recommendation system and method for relieving data sparsity. The movie introduction data, the movie comment data, the actual scoring data and the theater group personnel information data are collected, the improved convolutional neural network and the mathematical model are utilized to perform movie information fusion, the new recommendation list is generated, the accuracy ofthe system is improved, and the problem of data sparsity of the user-scoring matrix is relieved.

The invention provides a text classification method of a gating loop unit based on residual jump connection. The method is characterized in that on the basis of an existing gating loop unit, residual information is introduced by utilizing jump connection and gating limitation of an expressway network is combined, and meanwhile, an unsaturated activation function and batch standardization are used to improve a definition formula of the gating loop unit, so that the gating loop unit can capture an ultra-long-term dependency relationship while coping with the gradient disappearance problem, and residual information is limited not to occupy a main position by utilizing gating of the expressway network. According to the method, the trained network model is utilized to automatically classify the Chinese texts and classify the positive / negative evaluation of the movie review, so that the labor cost is reduced. A plurality of groups of contrast experiments prove that the method has a certain applicability in a text classification task.

The invention discloses a movie comment sentiment classification method and device based on a graph neural network, and the method comprises the steps: constructing a graph data structure based on the specific grammar information of a text sentence and the window co-occurrence relation of text words, obtaining the adjacent matrixes of a text co-occurrence graph and a syntactic dependency graph respectively, and carrying out the classification of the movie comment sentiment. Further inputting the word embedded vector matrix and the adjacent matrix into a graph convolutional network module to obtain a weighted feature matrix, and performing pooling operation on the weighted feature matrix of the text co-occurrence graph and the weighted feature matrix of the syntactic dependency graph by adopting a mixed pooling module; and obtaining a feature vector of the text co-occurrence graph and a feature vector of the syntactic dependency graph, fusing the feature vectors, and inputting the fused feature vectors into a classifier to obtain a classification result. According to the method, the network representation obtained by learning not only contains the information of adjacent words, but also contains the information of relatively far words related to grammatical relationships, so that the accuracy of sentiment classification is improved.

The invention provides an algorithm for automatically generating movie labels based on movie reviews. The algorithm in the invention fully considers the missing problem existing in the data set of thecurrently labeled movies. Firstly, an unsupervised algorithm with weights is adopted to automatically replenish the labels for the training set from the movie reviews. At the same time, the inventionalso fully considers the relationship between the similarity of each attribute and the similarity of labels of two movies, and predicts the mapping from each attribute to the similarity of labels bya machine learning method, instead of adopting simple similarity such as cosine similarity to calculate the rough similarity relationship. Finally, after the candidate multisets of labels are obtainedby using the traditional K-nearest neighbor algorithm, the method does not use simple evaluation criteria to sort and select the label set, but uses the graph algorithm based on label co-occurrence relationship to determine the order of the candidate labels, thus deciding the final label set.

The invention discloses a movie comment emotion analysis method and device, belongs to the field of data analysis, and particularly relates to a comment analysis method and device. The objective of the invention is to solve the problem of low accuracy in sentiment analysis of movie comments by using an existing sentiment analysis method. The method comprises the following steps: firstly, carryingout pretreatment such as word segmentation and syntactic analysis on film review corpora; Judging whether the words belong to words in a film review attribute feature word dictionary or not, inquiringand judging brother nodes of father nodes of the words, and obtaining the words corresponding to the nodes closest to the words in the sentences as emotion words; Traversing the W-SVM sentiment dictionary and the SO-VEC sentiment dictionary, and judging which category in the sentiment dictionary the emotional word belongs to, and recording the emotional value of the word; And finally updating theword sentiment value according to the negative word number between the word and the sentiment word; And finally, determining the movie review fine-grained emotion according to the word emotion values. The method is suitable for emotion analysis of movie comments.

The invention provides a personalized movie recommendation method and device and electronic equipment, and the method comprises the steps of determining a movie label set of a plurality of movies in advance, wherein the movie label set comprises a plurality of movie labels; generating a user label set of the user according to the viewing record of the user, wherein the user label set comprises a plurality of user labels; determining the overall association degree between the user and the movie according to the association degree between the plurality of user tags and the plurality of movie tags; and selecting one or more movies from the plurality of movies as target movies according to the overall association degree of the plurality of movies, and pushing the target movies to the user. According to the personalized movie recommendation method and device and the electronic equipment provided by the embodiment of the invention, the personalized movie label is formed on the basis of the movie review information, the overall matching degree between the user and each movie can be determined more finely and accurately, accurate recommendation can be realized in a targeted manner on the basis of the characteristics of the user, and personalized movie recommendation is realized.

The present invention relates to a method for emotional analysis of film reviews based on Python, including a data acquisition stage, a data preprocessing stage and a data analysis stage; the data acquisition stage uses Python to build a web crawler; the data preprocessing stage performs word segmentation and stop words The operation; the data analysis stage adopts the naive Bayesian machine learning method, combined with the sentiment dictionary method to carry out predictive analysis. Compared with other common sentiment analysis techniques, the present invention achieves the best in accuracy and recall.

The invention relates to the technical field of movie recommendation methods, in particular to a movie recommendation method based on literature learning. The movie recommendation method comprises thefollowing steps: S1, obtaining movie watching data of a plurality of users; S2, analyzing the movie reviews, and finding out a reference movie; S3, reading a new movie in the server, and reading movie information corresponding to the new movie; S4, comparing the movie information of the new movie in the server with the movie information of the reference movie; and S5, recommending a new movie ofwhich the movie introduction is similar to that of the reference movie to the user. According to the movie recommendation method, the movies with the highest repeated statement frequency in the moviereviews are screened out by analyzing the movie reviews of the users, and the movies can generate more resonance among the users, and new movies recommended on the basis of the movies can be acceptedby the users more easily, and the recommendation success rate is high.

The invention discloses a method for predicting attributes of movie review website users based on a deep neural network, and belongs to the fields of data mining, machine learning, and the like. The present invention decomposes the matrix of the sparse user rating data in the movie review website, decomposes the user rating matrix into a user-feature matrix and a movie-feature matrix, uses these two matrices to partially fill the user vector, and the filled user rating data is used as User's feature vector. The user's feature vector data is input into the fully connected deep neural network, combined with multi-task learning technology, the network model is trained, and the label scores of each task are obtained; decision analysis is performed according to the label scores of each task, and the final classification result is obtained. The invention uses the deep neural network technology to automatically predict the attributes of movie review website users, solves the problem of data sparsity, and has higher classification accuracy and efficiency.

The invention provides a method for automatically recommending a movie review based on a video and a digital television set, and belongs to the field of internet. The method includes the following steps: recording a movie review which matches a video; if a user views a complete video, detecting a video name; based on the video name, matching a movie review and recommending to the user. According to the invention, the method herein enables the user to automatically the movie review after the user views a complete video by recording the movie review which matches the video, obviates the need for manual searching of movie reviews, such that the user can understand the movice more and the effects of movie viewing is increased.

The invention discloses a movie review emotion visualization method for box office prediction, which collects movie information and movie review data, and preprocesses the collected data; for the movie review data, uses word segmentation vector method to extract movie review emotion type and intensity features; designs movie reviews Emotional visual coding indicates the type and intensity of emotion; design the temporal and spatial distribution of film review emotion; realize the visual layout of film review emotion and box office prediction. The present invention makes up for the lack of correlation analysis of data attributes and the lack of comprehensive analysis of movie review emotion types and strengths in the current situation, and uses intuitive visual elements and flexible interactions to encode and map movie review emotion types and strengths.

The invention discloses a method for pushing a movie review based on video playing time and a digital television. The invention belongs to the field of internet, and provides a method for pushing a movie review based on video playing time and a digital television. The method includes the following steps: recording the matching relation between the video playing time and a movie review segment; when a user watches a movie, detecting the video playing time; if the current time is the video playing time which matches the movie review segment, pushing the movie review to the user. According to the invention, the method herein, by recording the matching relation between the video playing time and the movie review segment, enables the user to browse movie reviews that match the plot of the movie when the user watches the movie, deepens user's understanding of the movie and improves user experience in terms of watching movie.

The invention discloses a movie review information retrieval system and method based on emotion analysis. The system comprises: a comment information search module, a comment information crawling module, a comment information cleaning module, a comment information extraction module, a comment information analysis module, a comment information recommendation module and a comment information viewingmodule. By crawling the movie-related comment information on the website and cleaning and extracting the comment information, the emotion and quality of comments can be analyzed, so as to enable theuser to accurately select the high-quality movie and improve the quality of the user view through viewing the movie comments.

The invention discloses a data mining and processing method and system based on multimedia digital image review, which comprises: using web crawler technology to mine public film review data for at least one movie on social networks and carrying out a second mining of a first preset amount on user page data for publishing the film review data while mining the film review date; establishing the mapping relationship between the movie review data and the user page data; carrying out analysis of interest attributes for the user page data and obtaining interest attribute points in turn, classifying and aggregating the user page data whose interest attribute points matching quantity exceeds a second preset amount to form several user page data sets; detecting pending operation events, and mining and executing data which is opened by users of the pending operation events on the social networks, carrying out analysis of interest attributes for the data and obtaining interest attribute points in turn, and finding the user page data set whose the interest attribute point matching quantity exceeds the second preset amount. The data mining and processing method and system based on multimedia digital image review enable users to find valuable information more conveniently and quickly.

The invention discloses a method for analyzing the sentiment tendency of movie review opinions. Formulate multiple comment opinion extraction rules, use the comment opinion extraction rules to obtain opinion words and sentiment words from each comment sentence of the comment content of the comment information, and then save all opinion words and sentiment words as comment label thesaurus and opinion sentiment words respectively Library; mark each comment statement by keyword matching or manual marking, mark the comment label category and sentiment tendency; generate a comment sentiment analysis model composed of a comment label classification model and a label sentiment classification model; for the target film review , and use the sentiment analysis model of comment opinion to automatically generate comment label category label and sentiment tendency label. Can fully and accurately reflect the user's emotional expression of the film.

The invention discloses a movie recommendation method based on dimensional sentiment analysis. By crawling movie comment data, performing data preprocessing on the comment data, and extracting feature dimensions from the comment data after data preprocessing, the extracted Merge the feature dimensions, sort the merged feature dimensions, construct a sentiment dictionary suitable for the film field, use the constructed sentiment dictionary to perform sentiment analysis on movie comment data, obtain the movie genre model, and analyze the movie genre model Perform clustering operations to obtain recommended results and other steps. The advantages are: through the sentiment analysis of user movie reviews by feature dimension, the movie type model is calculated, which more accurately and comprehensively shows the characteristics of each feature dimension of the movie, thereby improving the quality level of the recommendation service, to a certain extent The problem of low accuracy of the traditional recommendation algorithm regardless of feature dimension has been improved.

The invention claims to protect a film review emotion analysis method based on an improved convolutional neural network model. Introducing a weight distribution layer between the input layer and the convolutional layer can analyze important parts of the text, reduce noise, and improve the processing characteristics. Convolution is used to build a model. The convolution method is to generate local features around words, and then use local maxima to combine to create fixed-size features. The gradient descent method is used in the convolutional layer to calculate, gradient dispersion may occur, and the gating mechanism is introduced to reduce the dispersion; secondly, the softmax layer is canceled in the new model, and the support vector machine layer is added; finally, the conditional random field is used Not only the feature function of the traditional model on the i-th label is processed, but also the information feature function of its front and rear positions. The invention improves on the traditional convolutional neural network and adds a conditional random field layer, so that high-level abstract features can be extracted and better classification ability is achieved.

The invention discloses a method for optimizing movie semantic personalized tags based on big data. By mining movie review data, alienated personalized movie tags are obtained; at the same time, text and vocabulary are obtained through neural network model vectorization, and the similarity of movie introduction text is obtained. The similarity between degree and label vocabulary, combined with the deviation of custom labels before and after optimization, establishes a machine learning model, and initializes the machine learning model through personalized labels. The present invention realizes the optimization of existing custom tags for movies, realizes merging redundant tags, corrects wrong tags, complements missing tags, and completes personalized tags; scientifically and effectively classifies and describes movie resources, and provides movie information retrieval Based on the basis, it solves a series of problems caused by artificial movie labels.

The invention discloses a data mining and processing method and system based on multimedia digital image review, which comprises: using web crawler technology to mine public film review data for at least one movie on social networks and carrying out a second mining of a first preset amount on user page data for publishing the film review data while mining the film review date; establishing the mapping relationship between the movie review data and the user page data; carrying out analysis of interest attributes for the user page data and obtaining interest attribute points in turn, classifying and aggregating the user page data whose interest attribute points matching quantity exceeds a second preset amount to form several user page data sets; detecting pending operation events, and mining and executing data which is opened by users of the pending operation events on the social networks, carrying out analysis of interest attributes for the data and obtaining interest attribute points in turn, and finding the user page data set whose the interest attribute point matching quantity exceeds the second preset amount. The data mining and processing method and system based on multimedia digital image review enable users to find valuable information more conveniently and quickly.