Sentiment analysis

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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 judgment or evaluation (see appraisal theory), their affective 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 Information (See Peter Turney's work in this area).

There are two main approaches, statistical and linguistic. Statistical rely heavily on mathematical and statistical comparison of the occurrences and number of negative or positive statements in the text, whereas linguistic approach tries to build a set of rules and compare the analysed text with them.