Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model
Mayank Kumar Nagda1, Sankalp Sinha2, Poovammal E3
1Mayank Kumar Nagda, Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
2Sankalp Sinha, Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
3Poovammal E, Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India.
Manuscript received on July 30, 2019. | Revised Manuscript received on August 25, 2019. | Manuscript published on August 30, 2019. | PP: 4368-4173 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8933088619/2019©BEIESP | DOI: 10.35940/ijeat.F8933.088619
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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: In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.
Keywords: Deep Learning, Convolutional Neural Networks, LSTM, Natural Language Processing, Sentiment Analysis.