Sentiment Analysis from Text Using LSTM and BERT

Chintaginjala Rajeswari, P. Viswanatha Reddy and R. Vasanthselvakumar

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 4, 226-230

Abstract: As a result of increase in internet usage, there is a massive amount of information available to web users, as well as a massive amount of new information being created daily. To facilitate internet pick-up, trading ideas, and disseminating assessments, the internet has evolved into a stage of large volumes of data. Facebook, and Twitter generate a lot of data every day. As a result, text handling is crucial in making decisions. Sentiment analysis has surfaced as a method for analysing Twitter data. In this paper, we collected a Kaggle dataset with world data scientists. It contains three variants of texts: neutral, positive, negative. First, we used NLP methods to clean the text data. Later, we applied LSTM techniques for classifying tweets in three different ways: positive, negative sentiment analysis. As we didn't require the fair-minded so we dropped the objective and just remembered to be the good and gloomy inclination. We achieved a fair precision for the portrayal of positive and negative tweets. This dataset is for to research tests in assessment.

Keywords: Deep learning; sentiment analysis; text; LSTM; BERT. (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390285
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