A Systematic Review of the Sentiment Analysis Models Used in Handling Polarity Shift

Abstract

Purpose – This comprehensive review aims to analyze sentiment analysis models, focusing on their effectiveness in managing polarity shifts (positive, neutral, and negative sentiments). The investigation delves into specific aspects, including feature selection/extraction techniques, data augmentation, input datasets, and evaluation metrics. The goal is to provide valuable insights for both theoretical understanding and practical applications, guiding advanced research.

Method – Following Kitchenham’s framework, the review conducts an exhaustive literature search, emphasizing entities embedded within sentiment analysis models. This methodological approach explores intricate challenges associated with polarity shifts, revealing opportunities for future research.

Results – Key findings highlight the efficacy of sentiment analysis models in handling polarity shifts. Recursive Feature Elimination emerges as the most effective wrapper-based feature selection technique, with Word2Vec standing out for word embeddings. Notably, negation is identified as a significant polarity shifter. The study also identifies common datasets, machine learning, and deep-learning models, emphasizing the effectiveness of hybrid models and ensembles.

Conclusion – This systematic review offers a comprehensive analysis of sentiment analysis models, providing insights into their status and key trends. The findings contribute to advancing the field, offering valuable guidance for researchers, practitioners, and developers working on polarity shift challenges, particularly in addressing implicit negation.

Recommendations – Future research should prioritize refining models addressing implicit negation, requiring empirical investigations to assess their effectiveness. Additionally, efforts should focus on developing standardized evaluation metrics to accurately capture the intricacies of polarity shifts, ensuring a comprehensive assessment of model performance. Implementing these recommendations will advance the state-of-the-art.

Research Implications – The review suggests practical and theoretical implications by highlighting effective models and their embedded entities. Recognition of explicit negation underscores the need for models capable of discerning and addressing negated sentiments. Practitioners are encouraged to adopt diverse strategies, and research opportunities lie in exploring implicit negation for ongoing innovation in sentiment analysis research.