A system and method for analyzing documents, such as posts, on-line reviews and comments from people based on topics of the documents, to determine general sentiment of users is disclosed. Topics from the documents and their corresponding sentiment polarities are extracted. The documents are regarded to be constituted by a series of topics. The sentiment for a topic is represented by a quadruple (k, so, h, i), where k is the topic, so is the sentiment opinion, h is the comment or post holder, and i is the document. A quintuple (k, sup, p, n, ne) is used to illustrate the topics and corresponding sentiments and is stored in S, where sup indicates the frequency of the topic, and p (positive), n (negative) and ne (neutral) are different types of opinions of the users. From the quintuple set S, every topic is related to three kinds of sentiment opinions (positive, negative, and neutral), enabling determination of popular topics in documents as well as the users' sentiment polarities.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method performed by a computer system to determine sentiments of users for topics comprising: providing a plurality of on-line documents to form a corpus, wherein a document comprise one or more comments, wherein comments of the document comprise one or more holders; pre-processing the documents of the corpus to form a structure data representation of the documents; and processing the pre-processed documents, wherein the processing comprises extracting topics from the documents of the corpus, mapping the documents to the topics to form comment-topic pairs, analyzing the comment-topic pairs to determine the sentiments of the topics, and identifying relationships of a comment-comment structure within the topics of each document in the corpus. 2. The computer-implemented method of claim 1 wherein pre-processing comprises vector space modelling for generating document vectors. 3. The computer-implemented method of claim 1 wherein extracting topics comprises a Latent Dirichlet allocation (LDA) technique. 4. The computer-implemented method of claim 1 wherein the corpus comprises N number of documents and extracting topic comprises generating: θil, which is a K-dimensional vector for the distribution of topics in the lth comment of the ith document and K is the total number of possible topics of all documents in the corpus; φk, which is a V-dimensional vector for the distribution of words in topic kth and V is the capacity of words dictionary; zilj, which is the topic for the jth word in the lth comment of the ith document; and wijl, which is the jth word in the ith comment of the ith document. 5. The computer-implemented method of claim 1 wherein mapping documents to topics comprises mapping comments of documents to topics to form comment-topic pairs. 6. The computer-implemented method of claim 5 wherein mapping comprises mapping one or more topics to each comment of the documents to form one or more comment-topic pairs for each comment. 7. The computer-implemented method of claim 1 wherein analyzing the comment-topic pairs comprises natural language processing (NLP). 8. The computer-implemented method of claim 6 wherein analyzing the comment-topic pairs comprises assigning a sentiment opinion (so) based on its holder (h) for each comment-topic pair. 9. The computer-implemented method of claim 8 wherein the sentiment opinion comprises +1 for a positive sentiment (p), a −1 for a negative sentiment (n) and a 0 for a neutral sentiment (ne). 10. The computer-implemented method of claim 9 wherein analyzing the comment-topic pairs generates a quintuple (k, sup, p, n, ne) for each comment-topic pair to form a set S of quintuples. 11. The computer-implemented method of claim 1 comprises visualizing the document-topic structures of the documents and comment-comment structures within each document. 12. The computer-implemented method of claim 1 wherein: the corpus comprises N documents and an ith document comprises Mi comments, where i is from 1 to N; extracting topics comprises extracting K topics from the N documents; and analyzing the comment-topic pairs comprises defining a data set T of quadruple and initialize to null, defining a K-dimensional vector sup, for each document i, where i=1 to N, defining Mi=the number of comments for document i, for each comment l of document i, where l=1 to Mi, obtaining a holder h from comment l, and for each topic k in comment l, increasing sup(k) by θik, calculating sentiment opinion so for topic k of comment l of document i, and inserting (k, so, h, <i, 1>) into T, analyzing data set T after all comments l of all documents i are analyzed to remove quadruples in which k and h are the same except the latest one to result in T with quadruples which have unique k and h, and creating a data set S of quintuples comprising defining S and setting it to null, and for each topic k in T, initialize variables p, n, and ne to zero, for each sentiment so for topic k, if so equals to 1, then increase p by 1, if so equals to −1, then increase n by 1, if so equals to 0, then increase ne by 1, and insert (k, sup(k), p, n, ne) into S. 13. The computer-implemented method of claim 12 performs an attitude analysis when comment l of document i does not have any topics and comment l is a reply comment, wherein the attitude analysis comprises: if comment l is a reply of comment l′ and l′<l setting attitude att of l to l′, for each topic k′ in comment l′ θlk′, <-θl′k′, increasing sup(k′) by θlk′, reading sentiment so′ for (k′comment(<i, l>)), updating sentiment so for (k, (<i, l>) to so′*att, and inserting (k′, so, h, (<i, l>)). 14. The computer-implemented method of claim 12 wherein calculating the sentiment so for topic k of comment l of document i comprises: finding corresponding sentiment opinion of topic k in comment l in document i, and if found assigning 1, −1 or 0 to so; else if comment l is a reply comment of an earlier comment l′, finding attitude art of l to l′ and sentiment so′ of topic k in l′ and if found assigning so′*att to so else if not found, assigning 0 to so; else if not found assigning 0 to so. 15. A non-transitory computer-readable medium having stored thereon program code, the program code executable by a computer for determining sentiments of users for topics comprising: providing N of on-line documents to form a corpus, where i is from 1 to N, wherein a document i of the corpus comprises Mi comments; pre-processing the documents of the corpus to form a structure data representation of the documents; and processing the pre-processed documents, wherein processing comprises extracting K topics from the N documents, mapping the comments of the documents to the topics to form comment-topic pairs, and analyzing the comment-topic pairs to determine the sentiments of the topics, wherein analyzing comprises defining a data set T of quadruple and initialize to null, defining a K-dimensional vector sup, for each document i, where i=1 to N, defining Mi=the number of comments for document i, and for each comment l of document i, where l=1 to Mi, obtaining a holder h from comment l, for each topic k in comment l, increasing sup(k) by θlk, calculating sentiment opinion so for topic k of comment l of document i, and inserting (k, so, h, <i, l>) into T, analyzing data set T after all comments l of all documents i are analyzed to remove quadruples in which k and h are the same except the latest one to result in T with quadruples which have unique k and h, creating a data set S of quintuples comprising defining S and setting it to null, for each topic k in T, initialize variables p, n, and ne to zero, for each sentiment so for topic k, if so equals to 1, then increase p by 1, if so equals to −1, then increase n by 1, if so equals to 0, then increase ne by 1, and insert (k, sup(k), p, n, ne) into S, and identifying relationships of a comment-comment structure within topics of each document in the corpus; and visualizing document-topic and comment-comment structures. 16. The non-transitory computer-readable medium of claim 15 wherein the code comprises performing an attitude analysis when comment l of document i does not have any topics and comment l is a reply comment, the attitude analysis comprises: if comment l is a reply of comment l′ and l′<l setting attitude att of l to l′, for each topic k′ in comment l′ θlk′, <-θl′k′, increasing sup(k′) by θlk′, reading sentiment so′ for (k′ comment(<i, l>)), updating sentiment so for (k, (<i, l>) to so′*att, and inserting (k′, so, h, (<i, l>)). 17. The non-transitory computer-readable medium of claim 15 wherein calculating the sentiment so for topic k of comment l of document i comprises: finding corresponding sentiment opinion of topic k in comment l in document i, and if found assigning 1, −1 or 0 to so; else if comment l is a reply comment of an earlier comment l′, finding attitude att of l to l′ and sentiment so′ of topic k in l′ and if found assigning so′*att to so else if not found, assigning 0 to so; else if not found assigning 0 to so. 18. A computer system for determining sentiments of users for topics comprising: a data acquisition module for retrieving a plurality of on-line documents to form a corpus, wherein a document comprise one or more comments, wherein comments of the document comprise one or more holders; a pre-processor module for pre-processing the documents of the corpus to form a structure data representation of the documents; a processor module comprising a topic extractor unit, the topic extractor unit extracts topics from the documents of the corpus, a mapper unit, the mapper unit maps comments of the documents to the topics to form comment-topic pairs, an analyzer unit, the analyzer unit analyzes the comment-topic pairs to determine the sentiments of the topics, and a relationship identifier, the relationship identifier identifies relationships of comment-comment structures within topics of documents in the corpus; and a visualizer module for visualizing various relationships of the documents of the corpus. 19. The computer system of claim 18 wherein the mapper unit maps comments of the documents to topics to form comment-topic pairs. 20. The computer system of claim 18 wherein the pre-processor comprises a vector space modeler for generating comment vectors for the documents.