Sentiment Classification-How to Quantify Public Emotions Using Twitter

2. Sentiment Analysis

Now a day’s studies have increasingly focused on semantic analysis as a process that extracts sentiment information that is different from statistical, syntactic or semantic information. Specifically, semantic analysis deals with the extraction of user’s opinion and emotions, extraction over different blogs, discussion forum, social sites etc. people’s interaction and curiosity towards social media and internet, has attracted many researchers to explore the user’s behavior towards a specific topic.

There is different classification of Sentiment analysis: Document level, sentence level, aspect level. Document level sentiment classification deals with the extraction of opinion paring with words from reviews and detecting the polarity of these opinionated terms (Pang et al., 2002; Turney, 2002). Sentence level classification identify a sentence as subjective or objective and hence, also called as subjectivity classification (Wiebe et al., 1999). These subjective sentences are considered as small documents and further classified by extracting and classifying opinion as positive and negative. Aspect/feature level; gives a more fine-grained model, which extracts opinions expressed against different aspect/ features of the entity. This involves extraction of opinions and aspects, and categorizing them into similar classes, determining the polarity of opinions and summarization of results. Figure 1 shows the different classification levels and subtask of aspect level sentiment analysis (Liu, 2012, Liu & Zhang, 2012).