Sentiment Analysis of Twitter Tweets for Mobile Phone Brands
Abstract
Sentiment Analysis is a method of extracting useful insight from text or expressions that help make decisions for different fields like establishing a new business, purchasing electronic products, or overall community analysis. Different techniques for sentiment analysis have been used in various researches. This research used a machine learning classification technique to perform sentiment analysis with and without Stopwords on unigram features and Bigram words. We used the Support Vector Machine, Maximum Entropy, Naïve Byes, Logistic Regressions machine learning classifiers using Python. NLTK and scikit learn packages used to perform sentiment analysis in this work with Twitter API. The performance of classifiers is measured in terms of Accuracy, Precision, Recall, and F-measure. Results showed that the current method for calculating sentiment from tweets is better than previous methods, as results showed improved accuracy. It is also observed that Bigram features accuracy after removing Stopwords from data has been improved. By analyzing our results, it is observed that logistic regression gives the best performance, and we achieved 93.74% accuracy; using all feature sets and Maximum Entropy also provides significant results. By Comparing previous sentiment analysis results with our results, it is evidenced that our techniques of finding sentiment are significant.
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