compared with
Current by laura.tolosi
on Dec 08, 2014 14:27.

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Sentiment extraction is not one task, but requires solutions of several classical NLP sub-problems:
* negation: which verbs, adjectives or adverbs, otherwise with a specific polarity, are negated and thus polarity is changed? E.g. like (positive), don't like (negative); convenient (positive), not convenient (negative). Or, even more complicated expressions, such as in : I would hate to miss that movie (hate, miss are negative, together the opinion on the movie is positive).
* negation: which verbs, adjectives or adverbs, otherwise with a specific polarity, are negated and thus polarity is changed? E.g. _like_ (positive), _don't like_ (negative); _convenient_ (positive), _not convenient_ (negative). Or, even more complicated expressions, such as in : _I would hate to miss that movie_ (_hate_, _miss_ are negative, together the opinion on the movie is positive).
* segmentation (which words refer to which entity)
* named entity extraction, or concept extraction: target concepts, for which the sentiment needs to be evaluated, have to be identified in the text
* anaphora identification: concepts are often referenced by "it", "that", "him", etc, that need to be disambiguated
* domain specificity: some words are positive in some context domains, but negative in other domains. An unpredictable movie is something, however an unpredictable kitchen robot is probably bad.
* domain specificity: some words are positive in some context domains, but negative in other domains. An _unpredictable movie_ is something good, however an _unpredictable kitchen robot_ is probably bad.