“ENHANCING THE ACCURACY OF TWITTER SENTIMENT USING CLASSIFICATION ALGORITHM”
Keywords:
Twitter, sentiment, Web data, text miningAbstract
SA (Sentiment Analysis) as well as OM (Opinion Mining) are methods for performing such a comprehension. SA problems may be solved adequately via manual training. But, entirely automatic systems for analysing feelings which need no manual interventions have not been formulated thus far because of the several problems present within the field.
Sentiment Analysis provides huge number of opportunities by uncovering the opinions and views from the unstructured Twitter data set. To conclude, this research has explained that an efficient sentiment analysis can be performed on an event, Demonetization in India 2016. Throughout the continuation of this research various data analysis tools were applied to gather, clean, mine and determine review from the dataset. This analysis can help them to spot a positive turn in viewer’s opinion of their brand image. Uncovering positive trends early on can permit them to make educated decisions. It is shown in this research the approach of supervised machine learning classifier ‘SVM’ & Naïve Baise so SVM has a major effect on the overall accuracy of the analysis. This approach has an accuracy of around 89.03% for classification.
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