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

作　　者：Zohaib Ahmad Khan Yuanqing Xia Ahmed Khan Muhammad Sadiq Mahmood Alam Fuad AAwwad Emad A.A.Ismail 

机构地区：[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

出　　处：《Computers, Materials & Continua》2024年第5期2771-2793,共23页计算机、材料和连续体（英文）

基　　金：Researchers supporting Project Number(RSPD2024R576),King Saud University,Riyadh,Saudi Arabia.

摘　　要：Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources 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 lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关 键 词：Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]