Open Access

ARTICLE

Aspect Level Songs Rating Based Upon Reviews in English

by Muhammad Aasim Qureshi¹, Muhammad Asif², Saira Anwar³, Umar Shaukat¹, Muhammad Adnan Khan^5,*, Amir Mosavi^6,7,8

1 Department of Computer Science, Bahria University Lahore, 54000, Pakistan
2 Lahore Institute of Science and Technology Lahore, 54792, Pakistan
3 Department of Multidisciplinary Engineering, Texas A&M University, College Station, 77843, USA
4 Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam 31441, Saudi Arabia
5 Department of Software, Gachon University, Seongnam, 13120, Korea
6 John von Neumann Faculty of Informatics, Obuda University, Budapest, 1034, Hungary
7 Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, 81107, Slovakia
8 Faculty of Civil Engineering, TU-Dresden, Dresden, 01062, Germany

* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2023, 74(2), 2589-2605. https://doi.org/10.32604/cmc.2023.032173

Received 09 May 2022; Accepted 12 June 2022; Issue published 31 October 2022

Abstract

With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to design and test the decision model. Different aspects of songs namely music, lyrics, song, voice and video are picked. For the reason, reviews of 20 songs are scraped from YouTube, pre-processed and formed a dataset. Different machine learning algorithms—Naïve Bayes (NB), Gradient Boost Tree, Logistic Regression LR, K-Nearest Neighbors (KNN) and Artificial Neural Network (ANN) are applied. ANN performed the best with 74.99% accuracy. Results are validated using K-Fold.

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Keywords

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