Methods
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). More sophisticated methods try to detect the holder of a sentiment (i.e. the person who maintains that affective state) and the target (i.e. the entity about which the affect is felt). To mine the opinion in context and get the feature which has been opinionated, the grammatical relationships of words are used. Grammatical dependency relations are obtained by deep parsing of the text.
Open source software tools deploy machine learning, statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media. Knowledge-based systems, instead, make use of publicly available resources, e.g., WordNet-Affect, SentiWordNet, and SenticNet, to extract the semantic and affective information associated with natural language concepts.
Read more about this topic: Sentiment Analysis
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