A Comparison of Opinion Mining Algorithms by Using Product Review Data

A Comparison of Opinion Mining Algorithms by Using Product Review Data

Автор: Sumaiya Sultana, Sumaiya Rahman Eva, Nayeem Hasan Moon, Akinul Islam Jony, Dip Nandi

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 4 vol.14, 2022 года.

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After release of Web 2.0 in 2004 user spawned contents on the internet eminently in abundant review sites, online forums, online blogs, and many other sites. Entire user generated contents are considerable bunches of unorganized text written in different languages that encompass user emotions about one or more entities. Mainly predictive analysis exerts the existing data to forecast future outcomes. Currently, a massive amount of researches are being engrossed in the area of opinion mining, also called sentiment analysis, opinion extraction, review analysis, subjective analysis, emotion analysis, and mood extraction. It can be an utmost choice whilst perceiving the meaning and patterns in prevailing data. Most of the time, there are various algorithms available to work with polling. There are contradictory opinions among researchers regarding the effectiveness of algorithms. We have compared different opinion mining algorithms and presented the findings in this paper.

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Sentiment analysis, Opinion Mining, Supervised Learning Algorithms, Unsupervised Learning Algorithms, Boosting Algorithms

Короткий адрес: https://sciup.org/15018506

IDR: 15018506   |   DOI: 10.5815/ijieeb.2022.04.04

Список литературы A Comparison of Opinion Mining Algorithms by Using Product Review Data

  • Kasturi Dewi Varathan, Anastasia Giachanou, and Fabio Crestani, “Comparative opinion mining: a review,” Journal of the Association for Information Science and Technology, vol. 68, no. 4, pp. 811–829, 2017.
  • Amandeep Kaur and Vishal Gupta, “A survey on sentiment analysis and opinion mining techniques,” Journal of Emerging Technologies in Web Intelligence, vol. 5, no. 4, pp. 367–371, 2013.
  • Vidisha M Pradhan, Jay Vala, and Prem Balani, “A survey on sentiment analysis algorithms for opinion mining,” International Journal of Computer Applications, vol. 133, no. 9, pp. 7–11, 2016.
  • Ayesha Rashid, Naveed Anwer, Muddaser Iqbal, and Muhammad Sher, “A survey paper: areas, techniques and challenges of opinion mining,” International Journal of Computer Science Issues, vol. 10, no. 6, pp. 18, 2013.
  • Donia Gamal, Marco Alfonse, El-Sayed M El-Horbaty, and Abdel-Badeeh M Salem, “Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains,” Machine Learning and Knowledge Extraction, vol. 1, no. 1, pp. 224–234, 2019.
  • Vivek Kumar Singh, Rajesh Piryani, Ashraf Uddin, and Pranav Waila, “Sentiment analysis of movie reviews: A new feature- based heuristic for aspect-level sentiment classification,” in 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMc4s), IEEE, pp. 712-717.
  • Hajar Rehioui and Abdellah Idrissi, “New clustering algorithms for twitter sentiment analysis,” IEEE Systems Journal, vol. 14, no. 1, pp. 530–537, 2019.
  • Kerstin Denecke and Yihan Deng, “Sentiment analysis in medical settings: New opportunities and challenges,” Artificial intelligence in medicine, vol. 64, no. 1, pp. 17–27, 2015.
  • M Edison and A Aloysius, “Concepts and Methods of Sentiment Analysis on Big Data,” International Journal of Innovative Research in Science, Engineering and Technology, vol. 5, no. 9, pp. 16288–16296, 2016.
  • Monika Mishra, Jaydeep Chopde, Maitri Shah, Pankti Parikh, Rakshith Chandan Babu, and Jongwook Woo, “Big data predictive analysis of amazon product review,” in KSII The 14th Asia Pacific International Conference on Information Science and Technology, APIC-IST, KSII, Beijing, China.
  • Karen Robson, Mana Farshid, John Bredican, and Stephen Humphrey, “Making sense of online consumer reviews: A methodology,” International Journal of Market Research, vol. 55, no. 4, pp. 521–537, 2013.
  • Mohamed Elhag M Abo, Nordiana Ahmad Kharman Shah, Vimala Balakrishnan, and Ahmed Abdelaziz, “Sentiment analysis algorithms: evaluation performance of the Arabic and English language,” in 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), IEEE, pp. 1–5.
  • Mukesh Kumar, AJ Singh and Disha Handa, “Literature survey on student’s performance prediction in education using data mining techniques,” International Journal of Modern Education and Computer Science, vol. 7, no. 6, pp. 46-49, 2017.
  • Almarabeh and Hilal, “Analysis of students' performance by using different data mining classifiers,” International Journal of Modern Education and Computer Science, vol. 9, no. 8, pp. 9, 2017.
  • Megha Rathi and Vikas Pareek, “Spam mail detection through data mining - A comparative performance analysis,” International Journal of Modern Education and Computer Science, vol. 5, no. 12, pp. 31, 2013.
  • Zahid Ullah, Muhammad Fayaz and Asif Iqbal, “Critical analysis of data mining techniques on medical data,” International Journal of Modern Education and Computer Science, vol. 8, no. 2, pp. 42, 2016.
  • Anaıs Collomb, Crina Costea, Damien Joyeux, Omar Hasan, and Lionel Brunie, “A study and comparison of sentiment analysis methods for reputation evaluation,” Rapport de recherche RR-LIRIS-2014-002, 2014.
  • Bakhtawar Seerat and Farouque Azam, “Opinion mining: Issues and challenges (a survey),” International Journal of Computer Applications, vol. 49, no. 9, 2012.
  • Eivind Bjørkelund, Thomas H Burnett, and Kjetil Nørvåg, “A study of opinion mining and visualization of hotel reviews,” in Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services, pp. 229– 238.
  • Ali Harb, Michel Plantié, Gerard Dray, Mathieu Roche, François Trousset, and Pascal Poncelet, “Web Opinion Mining: How to extract opinions from blogs?,” in Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology, pp. 211–217.
  • Kumar Ravi and Vadlamani Ravi, “A survey on opinion mining and sentiment analysis: tasks, approaches and applications,” Knowledge-based systems, vol. 89, pp. 14–46, 2015.
  • Bohlouli, Mahdi and Bigham, Bahram and Narimani, Zahra and Vasighi, Mahdi and Ansari, Ebrahim, “Data Science: From Research to Application,” 2020. DOI: 10.1007/978-3-030-37309-2.
  • G Sneka and CT Vidhya, “Algorithms for Opinion Mining and Sentiment Analysis: An Overview. International Journal of Advanced Research in Computer Science and Software Engineering,” vol. 6, no. 2, pp. 1–5, 2016.
  • Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” arXiv preprint cs/0205070, 2002.
  • Sonia Anastasia and Indra Budi, “Twitter sentiment analysis of online transportation service providers,” in 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), IEEE, pp. 359–365.
  • Anuj Sharma and Shubhamoy Dey, “A document-level sentiment analysis approach using artificial neural network and sentiment lexicons,” ACM SIGAPP Applied Computing Review, vol. 12, no. 4, pp. 67–75, 2012.
  • Yaser Maher Wazery, Hager Saleh Mohammed, and Essam Halim Houssein, “Twitter sentiment analysis using deep neural network,” in 2018 14th International Computer Engineering Conference (ICENCO), IEEE, pp. 177–182.
  • Mehmet Saygın Seyfioğlu and Mustafa Umut Demirezen, “A hierarchical approach for sentiment analysis and categorization of Turkish written customer relationship management data,” in 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, pp. 361–365.
  • Vijay B Raut and DD Londhe, “Opinion mining and summarization of hotel reviews,” in 2014 International Conference on Computational Intelligence and Communication Networks, IEEE, pp. 556–559.
  • Jayashri Khairnar and Mayura Kinikar, “Machine learning algorithms for opinion mining and sentiment classification,” International Journal of Scientific and Research Publications, vol. 3, pp. 6, pp. 1–6, 2013.
  • C Jashubhai Rameshbhai and Joy Paulose, “Opinion mining on newspaper headlines using SVM and NLP,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 3, pp. 2152–2163, 2019.
  • Pravesh Kumar Singh and Mohd Shahid Husain, “Methodological study of opinion mining and sentiment analysis techniques,” International Journal on Soft Computing, vol. 5, no. 1, no. 11, 2014.
  • Anusha Sinha, Nishant Arora, Shipra Singh, Mohita Cheema, and Akthar Nazir, “Fake product review monitoring using opinion mining. International Journal of Pure and Applied Mathematics,” vol. 119, no. 12, pp. 13203–13209, 2018.
  • Muhammad Bilal, Huma Israr, Muhammad Shahid, and Amin Khan, “Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques,” Journal of King Saud University-Computer and Information Sciences, vol. 28, no. 3, pp. 330–344, 2016.
  • Vibhor Singh, Priyansh Saxena, Siddharth Singh, and S Rajendran, “Opinion mining and analysis of movie reviews,” Indian Journal of Science and Technology, vol. 10, no. 19, pp. 1–6, 2017.
  • Vijender Kumar Solanki, Nguyen Ha Huy Cuong, and Zonghyu Joan Lu, “Opinion mining: using machine learning techniques,” in Extracting knowledge from opinion mining, IGI Global, pp. 66–82.
  • JS Raikwal and Kanak Saxena, “Performance evaluation of SVM and k-nearest neighbor algorithm over medical data set,” International Journal of Computer Applications, vol. 50, no. 14, 2012.
  • Zeinab Shahbazi and Yung-Cheol Byun, “Product recommendation based on content-based filtering using XGBoost classifier,” Int. J. Adv. Sci. Technol, vol. 29, pp. 6979–6988, 2019.
  • Umair Younis, Muhammad Zubair Asghar, Adil Khan, Alamsher Khan, Javed Iqbal, and Nosheen Jillani, “Applying machine learning techniques for performing comparative opinion mining,” Open Computer Science, vol. 10, no. 1, pp. 461–477, 2020.
  • Palak Baid, Apoorva Gupta, and Neelam Chaplot, “Sentiment analysis of movie reviews using machine learning techniques,” International Journal of Computer Applications, vol. 179, no. 7, pp. 45–49, 2017.
  • Aurangzeb Khan, Umair Younis, Alam Sher Kundi, Muhammad Zubair Asghar, Irfan Ullah, Nida Aslam, and Imran Ahmed, “Sentiment classification of user reviews using supervised learning techniques with comparative opinion mining perspective,” in Science and Information Conference, Springer, pp. 23–29.
  • Balakrishnan Gokulakrishnan, Pavalanathan Priyanthan, Thiruchittampalam Ragavan, Nadarajah Prasath, and AShehan Perera, “Opinion mining and sentiment analysis on a twitter data stream,” in International Conference on Advances in ICT for Emerging Regions (ICTer2012), IEEE, pp. 182–188.
  • Duc-Hong Pham and Anh-Cuong Le, “A neural network based model for determining overall aspect weights in opinion mining and sentiment analysis. Indian Journal of Science and Technology,” vol. 9, no. 18, pp. 1–6, 2016.
  • Abdul Hafiz AbdulHafiz, “Novel Opinion mining System for Movie Reviews,” International Journal of Intelligent Systems and Applications in Engineering, vol. 8, no. 2, pp. 94–101, 2020.
  • Moin Khan and Kamran Malik, “Sentiment classification of customer’s reviews about automobiles in roman urdu,” in Future of Information and Communication Conference, Springer, pp. 630–640.
  • Miss Lovenika Kushwaha and Mr Sunil Damodar Rathod, “Opinion mining of customer reviews based on their score using machine learning techniques,” International Research Journal of Engineering and Technique, vol. 3, no. 03, pp. 2198–2203, 2016.
  • Yilmaz, Abdurrahim and Demircali, Ali Anil and Kocaman, Sena and Uvet, Huseyin, “Comparison of Deep Learning and Traditional Machine Learning Techniques for Classification of Pap Smear Images,” arXiv preprint arXiv: 2009.06366, 2020.
  • Kanani, Pratik and Padole, Mamta, “Deep learning to detect skin cancer using google colab,” International Journal of Engineering and Advanced Technology Regular Issue, vol. 8, no. 6, pp. 2167-2183, 2019.
  • Dos Santos, Ludmila Pereira, “Using Machine Learning and Neural Networks to Optimize Car Position When It is captured by Smart Cameras,” 2021.
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