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
Famous quotes containing the word methods:
“We can best help you to prevent war not by repeating your words and following your methods but by finding new words and creating new methods.”
—Virginia Woolf (1882–1941)
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—Henry George (1839–1897)
“We are lonesome animals. We spend all our life trying to be less lonesome. One of our ancient methods is to tell a story begging the listener to say—and to feel—”Yes, that’s the way it is, or at least that’s the way I feel it. You’re not as alone as you thought.””
—John Steinbeck (1902–1968)