| Autor: |
Tumu, Parthasarathi, Manchenasetty, Vaghindra, Rege, Manjeet |
| Předmět: |
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| Zdroj: |
Issues in Information Systems; 2020, Vol. 21 Issue 3, p59-65, 7p |
| Abstrakt: |
Consumer reviews are key indicators for product credibility and central to almost all product manufacturing companies to align and alter the products to the needs of customers. Using Sentiment analysis approach, these reviews can be analyzed for positive, negative and neutral feedback. There are many techniques designed to do Sentiment analysis and opinion mining in the past on drug reviews to study their effectiveness and side-effects on the people. In this paper, an approach is presented which is a combination of context-based sentiment analysis using Ngram and tf-idf word vectorization method to find the sentiment class – positive, negative, neutral and use this sentiment class in Naïve Bayes and Random Classifiers to predict user review emotion. Our validation process involved measuring the model performance using quality metrics. The results showed that the proposed solution outperformed conventional sentimental analysis techniques with an overall accuracy of 89%. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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