EXPLORING ENHANCEMENT IN SENTIMENT ANALYSIS USING DESCRIPTIVE-SEMANTIC TECHNIQUES: A SYSTEMATIC LITERATURE REVIEW

Sentiment analysis (SA) is a critical component of natural language processing (NLP) that enables the extraction of subjective information from large volumes of textual data. However, achieving high accuracy and contextual understanding remains challenging due to the unstructured nature of text and the complexity of linguistic patterns. To address this issue, this study investigates applying descriptive and semantic analysis (DSA) techniques to improve sentiment reliability and contextual accuracy. This study conducts a systematic literature review (SLR) of 28 publications from 2019 to 2024 using the PRISMA methodology, focusing on works indexed in Electrical and Electronics Engineers (IEEE) and Science Direct (SD). The review identifies Exploratory Data Analysis, Term Frequency-Inverse Document Frequency, and Latent Dirichlet Allocation as the most prevalent DSA techniques to enhance SA. Four thematic areas emerged from the analysis: sector, purpose, algorithm, and method employed. The study concludes that while DSA techniques contribute significantly to addressing contextual and semantic limitations in SA, there remains a need for more integrated approaches to handle complex sentiment structures. The findings highlight key challenges, recent advancements, and directions for future research in this evolving field.