Sentiment Analysis using Bi-directional Recurrent Neural Network for Telugu Movies
Kumar R G1, Shriram R2
1Kumar R G, Department of Computer Science & Engineering, Bharathiar University, Coimbatore, India.
2Dr Shriram R, Department of Computer Science & Engineering, Bharathiar University, Coimbatore, India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 241-245 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6137129219/2019©BEIESP | DOI: 10.35940/ijitee.B6137.129219
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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: Sentiment Analysis is the Natural Language Processing (NLP) is the active research area due to its vast application like stock market prediction, product re-views etc. The sentiment analysis in the regional languages are required for the film industries to increase their profit. Many existing methods has been applied on the sentiment analysis in the regional languages to increases the performance and still, it lags due in efficiency. In this research, the Bi-directional Recurrent Neural Network (BRNN) is applied to increase the performance of the sentiment analysis in the regional languages. The BRNN method has the advantages of rep-resenting the high and poor resources sentences in the common space and sentiment is analyzed based on the similarity measure. The proposed method is evaluated on the twitter data and compared this with the existing methods such as Random forest and Support Vector Machine (SVM). The proposed BRNN has the overall accuracy of 50.32%, while existing method of SVM has the overall accuracy of 38.73%.
Keywords: Bi-directional Recurrent Neural Network, regional languages, Sentiment Analysis, Support Vector Machine and twitter data.
Scope of the Article: Natural Language Processing