Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

• Published in: Journal of theoretical and applied electronic commerce research Vol. 19; no. 2; pp. 1493 - 1516
• Main Authors: Almeida, Catarina, Castro, Cecilia, Leiva, Víctor, Braga, Ana Cristina, Freitas, Ana
• Format: Journal Article
• Language: English
• Published: Curicó MDPI AG 01.06.2024
• Subjects: Algorithms; Case studies; Classification; consumer sentiment; Context; COVID-19; Customer feedback; Customers; Data mining; Decision making; Electronic commerce; Industrial applications; lexical features; Linguistics; Machine learning; machine learning techniques; Methods; Natural language processing; Parameter estimation; Public relations; Python software; Regression analysis; Regression models; Reputation management; Retailing industry; SentiLex-PT; Sentiment analysis; Support vector machines; Portugal
• ISSN: 0718-1876; 0718-1876
• DOI: 10.3390/jtaer19020074

Sentiment analysis is a cornerstone of natural language processing. However, it presents formidable challenges due to the intricacies of lexical diversity, complex linguistic structures, and the subtleties of context dependence. This study introduces a bespoke and integrated approach to analyzing customer sentiment, with a particular emphasis on a case study in the Portuguese retail market. Capitalizing on the strengths of SentiLex-PT, a sentiment lexicon curated for the Portuguese language, and an array of sophisticated machine learning algorithms, this research constructs advanced models that encapsulate both lexical features and the subtleties of linguistic composition. A meticulous comparative analysis singles out multinomial logistic regression as the pre-eminent model for its applicability and accuracy within our case study. The findings of this analysis highlight the pivotal role that sentiment data play in strategic decision-making processes such as reputation management, strategic planning, and forecasting market trends within the retail sector. To the extent of our knowledge, this work is pioneering in its provision of a holistic sentiment analysis framework tailored to the Portuguese retail context, marking an advancement for both the academic field and industry application.