Aspect-Based Sentiment Analysis (ABSA), a subfield of Natural Language Processing (NLP), aims to identify the sentiment polarity of particular features within social media content or text reviews. Unlike conventional sentiment analysis, which categorizes a whole text as positive, negative, or neutral, ABSA connects emotions to specific qualities, therefore providing more detailed insights useful for companies, product creators, and academics. Using a dataset of user-generated reviews, each tagged with aspect terms and their locations, this study focuses on aspect-term polarity estimation. Major difficulties include recording changes in context-dependent emotion caused by the surrounding language and word choices. Initially, conventional machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbor (KNN) were used. Acknowledging the shortcomings of single models, such as feature dependency and overfitting, this work also uses an ensemble learning technique with voting classifiers, investigating both hard (majority rule) and soft voting (probabilistic averaging). Experimental findings show that these ensemble-based voting classifiers regularly beat individual models, attaining better sentiment classification accuracy and increasing model robustness. The results confirm the efficiency of ensemble methods in promoting ABSA and enhancing sentiment analysis for practical use.
Siddiqua et al. (Sat,) studied this question.
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