Sentiment Analysis using LSTM
Ashok Tholusuri1, Manish Anumala2, Bhagyaraj Malapolu3, G. Jaya Lakshmi4
1Ashok Tholusuri, Student Scholar, Department of IT, V.R. Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
2Manish Anumala, Student Scholar, Department of IT, V.R. Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
3Bhagyaraj Malapolu, Student Scholar, Department of IT, V.R. Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
4G.Jaya Lakshmi, Assistant Professor, Department of IT, V.R. Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 1338-1340 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F12350986S319/19©BEIESP | DOI: 10.35940/ijeat.F1235.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Extracting the sentiment of the text using machine learning techniques like LSTM is our area of concern. Classifying the movie reviews using LSTM is our problem statement. The reviews dataset is taken from the IMDB movie review dataset. Here we will classify a review based on the memory in the neural network of a LSTM cell state. Movie reviews often contain sensible content which describe the movie. We can manually decide whether a movie is good or bad by going through these reviews. Using machine learning approach we are classifying the movie reviews such that we can say that a movie is good or bad. LSTM is effective than many other techniques like RNN and CNN.
Keywords: Sentiment Analysis, LSTM, Machine Learning, Natural Language Processing, IMDB Reviews, Text Analytics.
Scope of the Article: Predictive Analysis