The present system and method for analyzing sentiment is based on fuzzy set theory and clustering to classify text as positive, negative, or objective. The method for training and testing a document collection to analyze sentiment comprises computing a frequency matrix comprising at least one row and at least column vectors, executing term reduction of the terms, enumerating categories, computing centroids of the enumerated categories and computing a fuzzy polarity map. The row vectors may correspond to terms and the column vectors may correspond to documents. The frequencies of terms in a document indicate the relevance of the document to a query.
Please help me write a proper abstract based on the patent claims. CLAIM: 1) A method for training a document collection to analyze sentiment comprising the steps: a) to compute a frequency matrix comprising at least one row and at least column vectors; b) to execute term reduction of the terms; c) to enumerate categories; d) to compute centroids of the enumerated categories e) to compute fuzzy polarity map. 2) A method for training a document collection to analyze sentiment as claimed in claim 1, wherein the at least one row vector of the matrix correspond to terms and the at least one column vector correspond to documents. 3) A method for training a document collection to analyze sentiment as claimed in claim 1, wherein the frequencies of terms in a document are to indicate the relevance of the document to a query. 4) A method for training a document collection to analyze sentiment as claimed in claim 2, wherein every term of the frequency matrix is conceived as a separate dimension of a vector space and wherein a document is a formal vector. 5) A method for training a document collection to analyze sentiment as claimed in claim 1, wherein the distance between the vectors is measured by a geometrically-motivated function. 6) A method for training a document collection to analyze sentiment as claimed in claim 5, wherein the geometrically motivated function is one of the following functions: L1 norm, L2 norm and cosine function. 7) A method for training a document collection to analyze sentiment as claimed in claim 1, wherein the step to execute term reduction comprises an initial step to apply one or more linguistic pre-treatments. 8) A method for training a document collection to analyze sentiment as claimed in claim 7, wherein the first linguistic pre-treatment is based on the extraction of all linguistic units used for document representation. 9) A method for training a document collection to analyze sentiment as claimed in claim 8, wherein the step to apply several linguistic pre-treatment is done through chained filters 10) A method for training a document collection to analyze sentiment as claimed in claim 1, wherein the step to enumerate categories uses one or more training collections which are divided in a plurality of categories based on the sentiment type analysis to be executed. 11) A method for training a document collection to analyze sentiment as claimed in claim 10, wherein a polarity detection requires three categories: positive, negative and objective, and wherein a subjectivity detection requires the three other categories: subjective, objective and other. 12) A method for training a document collection to analyze sentiment as claimed in claim 10, wherein the step to compute category centroids assigns a cluster to each enumerated category. 13) A method for training a document collection to analyze sentiment as claimed in claim 1, wherein the step to enumerate categories uses an unsupervised clustering method. 14) A method for training a document collection to analyze sentiment as claimed in claim 13, wherein the unsupervised clustering method is the Spherical k-means method. 15) A method for training a document collection to analyze sentiment as claimed in claim 14, wherein clusters are generated and are used as an input to the Spherical k-means method. 16) A method to test a document collection trained to identify sentiment against an input text comprising the steps: a) to identify terms based on the input text and to reduce the terms; b) to detect one or more opinion based on the indexer result and/or on the fuzzy polarity map against the input text; c) to classify the collection of documents based on the detected opinion. 17) A method to test a document collection trained to identify sentiment as claimed in claim 16, wherein the computation of term reduction uses one or more filters to filter out all terms except those belonging to the following grammatical categories: adjective (JJ), adverb (RB), noun (NN) and verb (VB). 18) A method to test a document collection trained to identify sentiment as claimed in claim 16, wherein the step to detect one or more opinion displays the computed opinion values in a polyhedral which vertices are the maximum possible values for the number of dimensions observed. 19) A method to test a document collection trained to identify sentiment as claimed in claim 18, wherein the polyhedral is a triangle, wherein the edges of the triangle represent one of three classifications: positive, negative, and objective and wherein a term is located in this space as a point, representing the extent to which it belongs to each of the classifications. 20) A method to test a document collection trained to identify sentiment as claimed in claim 16, wherein the step to detect one or more opinion maps the opinions as a barycentric mapping. 21) A method to test a document collection trained to identify sentiment as claimed in claim 16, wherein the method further comprises the step to compute the text polarity. 22) A method to test a document collection trained to identify sentiment as claimed in claim 21, wherein the step to compute the text polarity comprises the steps to: a) build a polarity histogram of all terms belonging to the input text, b) compute skewness of the histogram; c) categorize documents in terms of the computed skewness. 23) A method to test a document collection trained to identify sentiment as claimed in claim 16, wherein the method further comprises the step to compute the text subjectivity. 24) A method to test a document collection trained to identify sentiment as claimed in claim 21, wherein the step to compute the text polarity comprises the steps to: a) build a polarity histogram of all terms belonging to the input text, b) compute kurtosis of the histogram, c) categorize documents in terms of the computed kurtosis.