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Negative Polarity Levels for Sentiment Analysis
Abstract
The goal of Sentiment analysis is defining the attitude of a writer with respect to some topics or the overall sentiment polarity of a text, such as positive or negative. Online sentiments have a big effect in making a decision in business. There are several challenges in analyzing and evaluating sentiments. More than 60% of sentiments face a negative polarity challenge. In most research, negative consists of two levels: implicit and explicit. But we present new criteria for analyzing negative sentiments. The criteria include five negative levels that can effect on the word or sentence polarity. This paper proposes a new technique to improve the accuracy by analyzing negative reviews. We investigate the effect of evaluating negation in sentiment analysis based on word level. We also discuss the negative words and phrases types, with respect the conflict with several expressions. Our experimental results indicate that by evaluating and classifying for negative, precision relative to human ratings increases with 10%.
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References
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