Sentiment on Twitter Data Set using Recurrent Neural Network – Long Short Term Memory
Gunjal P Jain1, SThenmalar2
1Gunjal P Jain, B.tech Student, Department of CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
2Prof. Dr S Thenmalar, Assistant Professor, Department of CSE Department of CSE , Assistant Professor, Department of CSE,SRM Institute of Science and Technology Chennai, Tamil Nadu, India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1206-1211| Volume-8 Issue-11S September 2019 | Retrieval Number: K124409811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1244.09811S19
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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: Social media is a combination of different platforms where a huge amount of user-generated data is collected. People from various parts of the country express their opinions, reviews, feedback and marketing strategies through social media such as Twitter, Facebook, Instagram, and YouTube. It is vital to explore, gather data, analyze them and consolidate the people views for better decision making. Sentiment analysis is a natural language processing for information extraction that identifies the user’s views. It is used for extracting reviews and opinions about the satisfaction of products, the events, and people for understanding the current trends of product or user’s behavior. The paper reviews and analyses the existing general approaches and algorithms for sentiment analysis. The proposed system selected to perform sentiment analysis on Twitter data set is Long Short Term Memory [LSTM] and evaluated with Naive Bayes Approach
Keywords: Map-Reduce, Sentiment Analysis, Big Data, Social Data, LSTM, Naive Bayes, Twitter
Scope of the Article: Big Data Analytics