Sentiment Analysis on Tweets for Trains Using Machine Learning SpringerLink

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Sentiment analysis is a well-liked theme in the herbal language processing NLP domain. People at present share their stay experience in restaurants, shopping malls, hotels and their travel adventure in taxis, buses, trains and airplanes. Online social media deliver a platform for the folks to share their reports of stay and travel in the type of text, images and videos. Twitter is one of the frequent and renowned social media structures around the world. In this study, we are using tweets data in appreciate to comfort facilities in Indian long route superfast trains.

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This tweet data is used to analyze the hidden sentiments using computer learning thoughts akin to help vector machines SVM, Random forest RF and back propagation neural networks BPNN. The effects show that BPNN provides high accuracy with more training on the data. The effects accomplished from SVM and RF was also sufficient but BPANN won the race with more education on the data.

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