Sentiment Analysis In Literature
AN EFFICIENT SENTIMENT ANALYSIS APPROACH FOR PRODUCT REVIEW USING TURNEY ALGORITHM ABSTRACT: Sentiment analysis can be done by Classification.It is one of the most important tasks for different application such as text categorization, tone recognition, image classification. Mostly existing supervised classification methods are based on traditional statistics, which can provide ideal results. The aim of the project is to increase the accuracy and to report the manufacturer about the negatives of the product. The major problem is polarity categorization.There are two levels of categorization and they are Sentence-level Categorization and Review-level Categorization. Review-level categorization …show more content…
Customer opinion is more important for the success of the product. In olden days people hear reviews about the product and then they decide the quality of the product. Sentiment analysis is used in social media. Sentiment analysis is also called as Opinion mining. Sentiment analysis or Opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis is mostly used natural language processing, text analysis and computational linguistics to extract subjective information from dataset. There are two types of sentences: Subjective and Objective sentences. Mostly subjective sentence contains more sentiment than objective sentences. The subjectivity of words depends on the context of the words. Objective document may also contain subjective sentence. To overcome these difficulties, we implement proposed system with Turney algorithm which also increases the accuracy of the reviews. Here, we use Precision, Recall and F-measure as metrics which calculates the accuracy. Precision gives good accuracy than others. Our project is implemented in java and also we use Amazon reviews as Dataset to our project. Finally, our project calculates the Polarity of the reviews. If the reviews contain more negatives, our system will recommend the manufacturer to recover the negatives in their products during their forthcoming manufacturing process to increase their …show more content…
Here, p(word1) and p(word2) gives the probability of the word occurs independently. The ratio between p(word1,word2) and p(word1) p(word2) gives the degree of statistical dependence between those words. After the calculation of PMI we have to calculate the semantic orientation. Thus, SO is calculated as SO(phrase) = PMI(phrase, {positive paradigms}) - PMI(phrase,{negative paradigms}) ---(2) If the phrase is with positive seed word, the phrase is positive. If the phrase is with negative seed word, the phrase is negative. PMI-IR issues query to search engine to find the number of hits (matching document). The AltaVista NEAR operator is used to search documents which is more efficient than AND operator. From equation (1) and (2) we can derive this equation with NEAR operator. SO(phrase)=log2
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Declaration Of Sentiment Analysis
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How To Read Literature Like An English Professor Analysis
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How To Read Literature Like A Professor Analysis
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Rhetorical Analysis In Backpacks Vs. Briefcases
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Pros And Cons Of Social Media Analytics
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