Sentiment Lexicon

Sentiment lexicons are structured collections of words and their associated sentiment polarities (positive, negative, neutral), used as a foundation for sentiment analysis—the automated identification of emotional tone in text. Current research focuses on improving lexicon-based methods through techniques like expanding lexicons to include idioms and nuanced expressions, and integrating them with machine learning models such as Random Forests and transformer-based architectures (e.g., BERT), often for improved accuracy in diverse languages and domains. This work is significant for advancing natural language processing capabilities and has broad applications in areas like social media monitoring, market research, and detecting potentially fraudulent text generation.