Cuckoo Search Optimized Improved Opinion Mining and Classification

Cuckoo Search Optimized Improved Opinion Mining and Classification
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-10
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

Abstract
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.

Keywords
Cuckoo Search, Naïve Bayes, Opinion Mining, Sentiment Analysis, Support Vector Machine.

Reference
[1] B. Liu, "Sentiment Analysis and Opinion Mining, " Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167, 2012.
[2] A. Buche, D. Chandak, and A. Zadgaonkar, "Opinion Mining and Analysis: A Survey," Arxiv Prepr. Arxiv1307.3336, 2013.
[3] Ray P, "Document Level Sentiment Analysis for Product Review Using Dictionary Based Approach," SSRG International Journal of Computer Science Engeering, [Internet]. 2017 [Cited 2022 Sep 30]; vol. 4, no. 6, pp. 24–9, 2017.
[4] M. Z. Asghar, F. M. Kundi, S. Ahmad, A. Khan, and F. Khan, "T-Saf: Twitter Sentiment Analysis Framework Using a Hybrid Classification Scheme, Expert System, vol. 35, no. 1, pp. E12233, 2018.
[5] Surendiran R, Duraisamy K, "An Approach In Semantic Web Information Retrieval," SSRG- International Journal of Electronics and Communication Engineering, [Internet], [Cited 2022 Sep 30], vol. 1, no. 1. pp. 17–21, 2014.
[6] G. Vinodhini and R. M. Chandrasekaran, "Sentiment Analysis and Opinion Mining: A Survey," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, no. 6, pp. 282–292, 2012.
[7] B. Liu and Others, "Sentiment Analysis and Subjectivity," Handbook Natural Language Process, vol. 2, pp. 627–666, 2010.
[8] D. Sharma and M. Sabharwal, "Sentiment Analysis for Social Media Using Svm Classifier of Machine Learning," The International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9, pp. 39–47, 2019.
[9] Z. Jianqiang, G. Xiaolin, and Z. Xuejun, "Deep Convolution Neural Networks for Twitter Sentiment Analysis," IEEE Access, vol. 6, pp. 23253–23260, 2018.
[10] S. Tedmori and A. Awajan, "Sentiment Analysis Main Tasks and Applications: A Survey," Journal of Information Processing Systems, vol. 15, no. 3, pp. 500–519, 2019.
[11] G. Saha, S. Roy, and P. Maji, "Sentiment Analysis of Twitter Data Related to Covid-19," In Impact of Ai and Data Science In Response to Coronavirus Pandemic, Springer, pp. 169–191, 2021.
[12] A.Kowcika, A. Gupta, K. Sondhi, N. Shivhre, and R. Kumar. "Sentiment Analysis for Social Media." International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, pp. 216-221, 2013.
[13] A. C. Pandey, D. S. Rajpoot, and M. Saraswat, Twitter Sentiment Analysis Using Hybrid Cuckoo Search Method, Information Processing & Management, vol. 53, no. 4, pp. 764–779, 2017.
[14] K. Zvarevashe and O. O. Olugbara, A Framework for Sentiment Analysis with Opinion Mining of Hotel Reviews, In 2018 Conference on Information Communications Technology and Society- ICTAS, pp. 1–4, 2018.
[15] F. Zarisfi Kermani, F. Sadeghi, and E. Eslami Solving the Twitter Sentiment Analysis Problem Based on A Machine Learning-Based Approach, Evolutionary Intelligence, vol. 13, no. 3, pp. 381–398, 2020.
[16] S. S. Aljameel et al, A Sentiment Analysis Approach to Predict An Individual’s Awareness of the Precautionary Procedures to Prevent Covid-19 Outbreaks In Saudi Arabia, International Journal of Environmental Research and Public Health, vol. 18, no. 1, pp. 2018, 2021.
[17] G. Angiani et al, "A Comparison Between Preprocessing Techniques for Sentiment Analysis in Twitter," 2016.
[18] I. Hemalatha, G. P. S. Varma, and A. Govardhan Preprocessing the Informal Text for Efficient Sentiment Analysis, International Journal of Emerging Trends in Science and Technology, vol.1, no. 2, pp. 58–61, 2012.
[19] R. Duwairi and M. El-Orfali, A Study of the Effects of Preprocessing Strategies on Sentiment Analysis for Arabic Text, Journal of Information Science, vol. 40, no. 4, pp. 501–513, 2014.
[20] S. Pradha, M. N. Halgamuge, and N. T. Q. Vinh, "Effective Text Data Preprocessing Technique for Sentiment Analysis in Social Media Data," In 2019 11th International Conference on Knowledge and Systems Engineering- KSE, pp. 1–8, 2019.
[21] Shubhi Kulshrestha, Ankur Goyal, "Cuckoo Search Algorithm and Bf Tree Used for Anomaly Detection in Data Mining, " International Journal of Computer and Organization Trends, vol. 9, no. 4 , pp. 11-18, 2019. Crossref, https://doi.org/10.14445/22492593/IJCOT-V9I4P303.
[22] M. Javed and S. Kamal, "Normalization of Unstructured and Informal Text in Sentiment Analysis," International Journal of Advanced Computer Science and Applications, vol. 9, no. 10, 2018.
[23] A. P. Jain and P. Dandannavar, "Application of Machine Learning Techniques to Sentiment Analysis," In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology - ICATCCT, pp. 628–632, 2016.
[24] M. Avinash and E. Sivasankar, "A Study of Feature Extraction Techniques for Sentiment Analysis," In Emerging Technologies In Data Mining and Information Security, Springer, pp. 475–486, 2019.
[25] K. Orkphol and W. Yang, "Word Sense Disambiguation Using Cosine Similarity Collaboratewith Word2VEC and Wordnet, Future Internet, vol. 11, no. 5, pp. 114, 2019.
[26] X.-S. Yang and S. Deb, "Engineering Optimisation by Cuckoo Search," Arxiv Prepr. Arxiv1005.2908, 2010.
[27] M. Elhoseny, H. Elminir, A. Riad, and X. Yuan, "A Secure Data Routing Schema for Wsn Using Elliptic Curve Cryptography and Homomorphic Encryption," Journal of King Saud University - Computer and Information Sciences, 2016, Doi: 10.1016/J.Jksuci.2015.11.001.
[28] V. Vapnik, "The Nature of Statistical Learning Theory," Springer Science \& Business Media, 1999.
[29] A. Goel, J. Gautam & S. Kumar, "Real Time Sentiment Analysis of Tweets Using Naive Bayes," In 2016 2nd International Conference on Next Generation Computing Technologies NGCT, pp. 257-261, 2016.
[30] A. Krouska, C. Troussas, and M. Virvou, "The Effect of Preprocessing Techniques on Twitter Sentiment Analysis," In 2016 7th International Conference on Information, Intelligence, Systems & Applications - IISA, pp. 1–5, 2016.
[31] uska, C. Troussas, and M. Virvou, "The Effect of Preprocessing Techniques on Twitter Sentiment Analysis," In 2016 7th International Conference on Information, Intelligence, Systems & Applications - IISA, pp. 1–5, 2016.