CNN-OLSTM: Convolutional Neural Network with Optimized Long Short-Term Memory Model for Twitter based Sentiment Analysis

Abstract

Social Networking continues the growth of web users, with people sharing their ideas and opinions daily in the form of texts, pictures, videos, and speech. Text classification is still an important issue because these large texts were derived from diverse sources and different thinking people. The shared concept must be incomplete, random, noisy, and in the form of different languages. To solve this issue, in this paper, Convolutional Neural Network with Optimized Long Short Term Memory Model (CNN-OLSTM) based sentimental analysis is proposed. The presented approach contains pre-processing, word2vec conversion, and prediction. Initially, data are pre-processed by using tokenization, stop word removal, and stemming. After the pre-processing, the skip-gram model (SGM) based word2vec conversion is performed. Then, the extracted vectors are given to the CNN-OLSTM classifier to classify a tweet as positive or negative polarity. In this, the CNN model effectively reduces the dimension of the input vector using max-pooling layers and convolutional layers. Also, the LSTM model is capable of catching long-term dependencies between word sequences. To enhance the LSTM performance, the rain optimization algorithm (ROA) is effectively used. The results show that the suggested CNN-OLSTM approach outperforms conventional deep neural networks in terms of accuracy.

KEYWORDS:

• Convolution neural network
• Long short-term memory
• Rain optimization algorithm
• Sentimental analysis
• Skip-gram model

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Harika Vanam

Harika Vanam was born in Hanamkonda, a town in Telangana State in India, in 1985. She completed her bachelors and master's degree in computer science engineering. Currently, she is pursuing her PhD in Sathyabama Institute of Science and Technology (Deemed to be University) Since 2018. Her topic of research is in big data field. She is working in teaching field and has 10+ years of experience in teaching subjects like Java, data mining, python etc. Corresponding author. Email: [email protected]

Jeberson Retna Raj

Jeberson Retna Raj is working as associate professor in Sathyabama Institute of science and Technology(Deemed to be University), Chennai, in the Indian state of Tamilnadu since 2006. He completed his master's degree in computer science and engineering and then completed his doctorate research. His specialization areas include big data analytics, image processing, internet of things, cloud computing. He published various research papers on these topics in international journals and international conferences. He conducted many workshops and conferences in Sathyabama Institute of Science and Technology. Email: [email protected]