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Verb Oriented Sentiment Classification (Meta (Authors (Mostafa Karamibekr,…

- - - - From the sentiment perspective, a sentence can be objective or subjective.
      - While an objective sentence contains one or more facts about a product, topic, or an issue, a subjective sentence contains expressed opinion(s) about a product, feature, topic, or an issue.
      - When two or more products or features are compared in a subjective sentence, that sentence is called a comparative sentence
      - Subjectivity classification categorizes a sentence as objective or subjective, which is the first step of sentiment analysis. A simple technique uses a list of opinion terms to distinguish between subjective and objective sentences.
    - - BOW
        
        In the BOW approach, each document is seen as a set of words. As a result, the syntactic and semantic information between words are lost. The BOW approach is not useful when opinions about products and their features have to be analyzed
      - FBS
        
        FBS has emerged as an approach for analysis of sentiments regarding a product and its features. The result of sentiment classification is presented in various formats in different domains: positive/negative, like/dislike, recommended/not-recommended, good/bad, buy / don’t buy, excellent/boring(film), support/against[6], favorable/ unfavorable[11], bullish/bearish, or optimistic / pessimistic[3]
    - - Up until recently, most of the research and works in sentiment analysis have been done for the products and services. Traditional machine learning techniques use adjectives, adverbs, and nouns as features to classify the sentiment polarity of a document