Open AccessPredicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model
Author(s) -
Mayank Kumar Nagda*,
Sankalp Sinha,
E Poovammal
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f8933.088619
Subject(s) - computer science , artificial intelligence , microblogging , convolutional neural network , benchmark (surveying) , social media , deep learning , sentiment analysis , polarity (international relations) , layer (electronics) , recurrent neural network , machine learning , artificial neural network , support vector machine , chemistry , genetics , geodesy , organic chemistry , biology , world wide web , cell , geography
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.