Sentiment analysis

From Wikipedia, the free encyclopedia

Sentiment analysis refers to a broad (definitionally challenged) area of natural language processing, computational linguistics and text mining. Generally speaking, it aims to determine the attitude of a speaker or a writer with respect to some topic. The attitude may be their judgement or evaluation (see appraisal theory), their affectual state (that is to say, the emotional state of the author when writing) or the intended emotional communication (that is to say, the emotional effect the author wishes to have on the reader).

A related term is polarity which also has a number of meanings (including the simple 'direction' of a verb - whether it is negated or not).

Computers can perform automated sentiment analysis of digital texts, using elements from machine learning such as latent semantic analysis, support vector machines, "bag of words" and Semantic Orientation — Pointwise Mutual Inclusion (See Peter Turney's work in this area).

Cornell has developed a database of movie reviews, with datasets that show sentiment polarity, sentiment scale datasets, and subjectivity.^[1] A number of companies offers sentiment analysis systems, e.g, Nstein Technologies' Nsentiment, SentiMetrix offers a framework to measure sentiments or opinions expressed in electronic media. Other companies offering related systems are Collective Intellect, Andiamo Systems and BuzzLogic.^[2] The company Corpora (now part of Infonic) developed a specialized program for sentiment analysis that has been used by Reuters.^[3] The open source data mining and text mining software RapidMiner also provides the means to perform sentiment analysis of text documents, web pages, and blog entries by means of text classification, clustering, and feature correlation analysis.^[4]