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.