Abstract
One of the most intriguing areas of research these days is sentiment analysis from Twitter. In order to create such systems, it blends data mining methodologies with natural language processing techniques. We presented an effective Twitter sentiment analysis method in this study. A machine learning model was developed using the suggested approach to identify both good and negative tweets. During the training phase, our model employed various methods to represent the input labelled tweets using various feature sets. For more accurate results, the classifier ensemble is shown various basis classifiers throughout the classification phase. The suggested technique may be used to gauge users' opinions based on their tweets, which is highly beneficial for a variety of uses, including product reviews, political polarity identification, and marketing.
Published Version
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