Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Behera, Ranjan Kumar; Jena, Monalisa; Rath, Santanu Kumar; Misra, Sanjay
• Is Part Of: Information processing & management. Volume 58:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Deep learning -- Big data -- Sentiment analysis -- Word embedding -- RNN -- CNN -- LSTM; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102435
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Co-LSTM is a classifier for sentiment analysis of social media reviews. Co-LSTM leverages the best features of both convolutional neural network and Long short-term memory in order to model the classifier. Word embedding model has been applied in constructing vocabulary dictionary. Co-LSTM is compared with other machine learning and deep learning models. Abstract: Analysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.