Twitter Sentiment Analysis Using TF-IDF and Machine Learning Classifiers
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Abstract
One of the foremost microblogging platforms is Twitter, which serves as a rich repository for the measurement of live user opinions and sentiments. The subject of this study is Twitter sentiment analysis using cutting-edge machine learning techniques. A machine learning pipeline is being constructed that includes three classifiers. Logistic Regression, Support Vector Machine, and Random forest. Also, We utilize Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction.The data set used in this study is known as the Sentiment140 data set, which comprises 1,600,000 tweets gathered via the Twitter API. These classifiers are measured using accuracy and F1 scores. The results When it comes to sentiment classification, the model is notable for its high accuracy and We are getting an F1 score of 0.87 which is higher than state-of-the-art methods. The findings from this study have important implications for comprehension of public opinion brand perception and societal trends.
In the digital age, our scholarly work contributes to the enhancement of machines by improving the accuracy and granularity of sentiment analysis. Notable Learning applications are to be found in the dynamic sphere of social media, in order to reveal the possibilities of informed decision making and trend prediction.
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References
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