Cuckoo Search Optimized Improved Opinion Mining and Classification
Cuckoo Search Optimized Improved Opinion Mining and Classification
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Priyanka, Kirti Walia
|DOI : 10.14445/22315381/IJETT-V70I10P206|
How to Cite?
Priyanka, Kirti Walia, "Cuckoo Search Optimized Improved Opinion Mining and Classification ," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 44-53, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P206
Opinion mining presents one of the most prominent fields in sentiment analysis to deal with the enormous content generated by social media. Opinion mining is used to track people's moods based on any product and helps collect different reviews against the product in many fields. This paper aimed to identify the individual’s emotions and sentiments expressed by their with various subjects and products. The present work comprises pre-processing of the extracted tweets data, followed by TF-IDF-based feature extraction and feature selection, and optimization using the Cuckoo Search algorithm. The sentiment classification is performed using Naïve Bayes and Support Vector Machine into positive, negative, and neutral opinions. In the simulation analysis, 10000 samples were analyzed in terms of precision, recall, and accuracy. The overall analysis shows that CS-SVM outperformed the CS-Naïve Bayes classifier with an average accuracy of 93%. The success of the proposed CS-optimized sentiment classification is further justified by comparative analysis against the existing studies.
Cuckoo Search, Naïve Bayes, Opinion Mining, Sentiment Analysis, Support Vector Machine.
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