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Sentiment extraction (or opinion mining) is the task of inferring the kind of subjective attitude that is expressed in text, either as a whole or with respect to some product, company, person, event, etc. Typically, of high interest is to quantify the polarity of the sentiment - either positive or negative. More refined sentiment analysis can be done to identify specific emotional layers like anger, fear, hope, love, hate, etc.
Sentiment extraction is a very difficult NLP problem, mainly because algorithms must uncover subjective human emotions, that are often mixed, subtle, marked by irony.. Even a human reading subjective text can have a hard time quantifying the polarity of the opinion (what is a 20% positive opinion, or a 90% negative opinion)?
NLP subproblems: negation, segmentation (which words refer to which entity), anaphora (cross-references)
- Supervised (annotated corpora)
- Unsupervised (dictionaries)