Context-aware sentiment classification

Sentiment Analysis is a growing research area of Natural Language Processing which aims at identifying positive and negative opinions and emotions from a textual data. Presently. Sentiment Analysis research works range from document level classification to sentence-level. phrase-level or aspect/feature level analysis. Context-awareness is of key important for sentiment classification since particular evaluation phrase in one context may express a positive sentiment while in another context it may express a negative sentiment. For example, the adjective "unpredictable" may have a negative orientation in an automotive review, in a phrase such as "unpredictable steering", but it could have a positive orientation in a movie review, in a phrase such as "unpredictable plot". Further, not only the context of the documents is important but also the domain of the feature or aspect that we evaluate plays an important role. Hotel reviews may contain "hot water", which has a positive semantic orientation, whereas "hot room" has a negative orientation. Thus, contextual polarity plays a vital role in sentiment classification.