Abstract: The present system and method for analysing sentiment is a response to the growing availability of informal, opinionated texts like blog posts and product review websites, a field of Sentiment Analysis has sprung up in the past decade to address the interrogation about how people feel about certain topics. Sentiment Analysis expands the traditional fact-based text analysis to enable opinion-oriented information systems. The present system and method for analysing sentiment presents a novel learning system and method for classifying text as positive, negative, or objective. It is mainly based on fuzzy set theory and clustering.

Abstract: The present system and method for analysing sentiment is a response to the growing availability of informal, opinionated texts like blog posts and product review websites, a field of Sentiment Analysis has sprung up in the past decade to address the interrogation about how people feel about certain topics. Sentiment Analysis expands the traditional fact-based text analysis to enable opinion-oriented information systems. The present system and method for analysing sentiment presents a novel learning system and method for classifying text as positive, negative, or objective. It is mainly based on fuzzy set theory and clustering.

Abstract: 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.

Abstract: A social media monitoring and text analysis method for automated classification of user posts on the web, using a facet based relevance assessment model, comprise a semantic indexing server, which builds a faceted classification index of text objects, and a query server, which receives and analyzes the user's query. A query thus processed is then sent from the query server to the semantic indexing server through an interface in order to perform a search in the faceted classification index. The search system and method further comprise a result handler, which provides the user with a search result set comprising a list of unexpected links and a list of result elements. The list of unexpected links corresponds to filters which allow the user to narrow down or refine the original query.

Abstract: 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.

Abstract: A social media monitoring and text analysis method for automated classification of user posts on the web, using a facet based relevance assessment model, comprise a semantic indexing server, which builds a faceted classification index of text objects, and a query server, which receives and analyzes the user's query. A query thus processed is then sent from the query server to the semantic indexing server through an interface in order to perform a search in the faceted classification index. The search system and method further comprise a result handler, which provides the user with a search result set comprising a list of unexpected links and a list of result elements. The list of unexpected links corresponds to filters which allow the user to narrow down or refine the original query.

Abstract: A search system and method for uncovering unexpected links between different concepts related to a user's query during a search comprise a semantic indexing server, which builds a faceted classification index of text objects, and a query server, which receives and analyzes the user's query. A query thus processed is then sent from the query server to the semantic indexing server through an interface in order to perform a search in the faceted classification index. The search system and method further comprise a result handler, which provides the user with a search result set comprising a list of unexpected links and a list of result elements. The list of unexpected links corresponds to filters which allow the user to narrow down or refine the original query.

Abstract: A system and method for query expansion allows the refinement and expansion of a keyword query search by combining a key concept with semantically related concepts proposed by the system and associated with that key concept. The semantically related concepts may be grouped together in a cluster, which is then presented to the user in the form of a tooltip. Once a semantically related concept is selected from the cluster, a new search is launched. This new search may use as query terms the combination of at least part of the key concept and the selected semantically related concept to thereby expand the original search.