Sentiment analysis is a kind of text classification that classifies texts based on the sentimental orientation (SO) of opinions they contain. Sentiment analysis of product reviews has recently become very popular in text mining and computational linguistics research. The following example provides an overall idea of the challenge. The sentences below are extracted from a movie review on the Internet Movie Database: “It is quite boring...... the acting is brilliant, especially Massimo Troisi.” In the example, the author stated that “it” (the movie) is quite boring but the acting is brilliant. Understanding such sentiments involves several tasks. Firstly, evaluative terms expressing opinions must be extracted from the review. Secondly, the SO, or the polarity, of the opinions must be determined. For instance, “boring” and “brilliant” respectively carry a negative and a positive opinion. Thirdly, the opinion strength, or the intensity, of an opinion should also be determined. For instance, both “brilliant” and “good” indicate positive opinions, but “brilliant” obviously implies a stronger preference. Finally, the review is classified with respect to sentiment classes, such as Positive and Negative, based on the SO of the opinions it contains.
Research on Prediction of Different Categories of Video based on YOUTUBE Using Text Mining and Sentiment Analysis
