Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

• Published in: IEEE access Vol. 9; pp. 24249 - 24262
• Main Authors: Chugh, Aarti, Sharma, Vivek Kumar, Kumar, Sandeep, Nayyar, Anand, Qureshi, Basit, Bhatia, Manjot Kaur, Jain, Charu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blogs; Classification; Data mining; Deep learning; deep recurrent neural network; Distance measurement; Domains; Experimentation; Feature extraction; fuzzy K-nearest neighbours; Industrial research; Information retrieval; Monkeys; Optimization; Optimization algorithms; Recurrent neural networks; Sentiment analysis; Sentiment classification; SentiWordNet; Social networking (online); Telecommunications; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3055507

The epidemic increase in online reviews' growth made the sentiment classification a fascinating domain in academic and industrial research. The reviews assist several domains, which is complicated to gather annotated training data. Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed. In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN). In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user's seeking time. Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews. The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments. To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure. With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 97.7%, precision of 95.5%, recall of 94.6%, and F1-score 96.7%, respectively.