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.

Бесплатный доступ

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

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