Types Of Sentiment Analysis
Sentiment Analysis (SA) also known as Opinion Mining (OM) is the computational treatment of opinions, sentiments and subjectivity of text.The entity can represent individuals, events or topics. Mining opinions and sentiments from natural language is challenging, because it requires a deep understanding of the explicit and implicit, regular and irregular, and syntactical and semantic language rules. The two expressions SA or OM are interchangeable. They express a mutual meaning. Opinion Mining extracts and analyzes people’s opinion about an entity while Sentiment Analysis identifies the sentiment expressed in a text then analyzes it. Therefore, the target of
SA is to find opinions, identify the sentiments that people express, and then classify
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Documents will be equipped with sentiment annotations which are the output of the system (Feldman Ronen, 2013). According to Feldman and Ronen, Sentiment analysis can be classified into five types - Document-level sentiment analysis, Sentence-level sentiment analysis, Aspect-based sentiment analysis, Comparative sentiment analysis and Sentiment lexicon acquisition. Document-level SA aims to classify an opinion document as expressing a positive or negative sentiment or binary sentiment. It considers the whole document a basic information unit (talking about one topic). Sentence-level SA aims to classify sentiment expressed in each sentence. The first step is to identify whether the sentence is subjective or objective. If the sentence is subjective, Sentence-level SA will determine whether the sentence expresses positive or negative opinions. Classifying text at the document level or at the sentence level does not provide fine-grained analysis of the entity, to obtain these details, …show more content…
\\ \indent There are many applications,enhancements and challenges on SA algorithms that were proposed in the last few years. This survey aims to give a closer look on these aspects.It also provides a brief intro of the software available in the market for sentiment analysis with its limitations, followed by proposed method to overcome these limitations.
This survey paper can be useful for new comer in
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