The internet is rich in directional text (i.e., text containing opinions and emotions). World Wide Web provides volumes of text-based data about consumer preferences, stored in online review websites, web forums, blogs, etc. Sentiment analysis is a technique to classify people's opinions in product reviews, blogs or social networks has emerged as a method for mining opinions from such text archives. It uses machine learning methods combined with linguistic attributes/features in order to identify among other things the sentiment polarity (e.g., positive, negative, and neutral) We investigated supervised learning by incorporating linguistic rules and constraints that could improve the performance of calculations and classifications.
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