Sentiment Analysis from Text Using LSTM and BERT
Keywords:
Deep learning, sentiment analysis, text, LSTM, BERT.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.
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