Open Access

ARTICLE

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering: An Innovative Multilingual Approach for Social Media Reviews

Zohaib Ahmad Khan¹, Yuanqing Xia^1,*, Ahmed Khan², Muhammad Sadiq², Mahmood Alam³, Fuad A. Awwad⁴, Emad A. A. Ismail⁴

1 School of Automation, Beijing Institute of Technology, Beijing, 100081, China
2 Department of Computer Science and Technology, University of Science and Technology Bannu, KPK, Bannu, 28100, Pakistan
3 School of Computer Science and Engineering, Central South University, Changsha, 410083, China
4 Department of Quantitative Analysis, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh, 11587, Saudi Arabia

* Corresponding Author: Yuanqing Xia. Email:

(This article belongs to the Special Issue: Requirements Engineering: Bridging Theory, Research and Practice)

Computers, Materials & Continua 2024, 79(2), 2771-2793. https://doi.org/10.32604/cmc.2024.046897

Received 18 October 2023; Accepted 12 December 2023; Issue published 15 May 2024

Abstract

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexicon is developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentiment scores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. In the second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis of Roman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrieval metrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The results showcase the potential for improving software engineering tasks related to user feedback analysis and product development.

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Keywords

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