lexiconbased

Lexicon-based refers to a class of methods in natural language processing that rely on predefined lexical resources rather than statistical models learned from labeled data. In sentiment analysis and emotion detection, a lexicon-based approach uses a lexicon—a collection of terms each annotated with a sentiment polarity, valence score, or emotion category. Text is analyzed by identifying occurrences of lexicon terms and aggregating their scores to produce an overall sentiment or emotional profile. Some systems also apply rules for negation, booster words, and punctuation, or adjust scores based on part-of-speech or negation scope.

Lexicons can be language-specific and domain-independent. Popular sentiment lexicons include AFINN, SentiWordNet, and the Hu and Liu Opinion Lexicon, as well as emotion-focused resources such as the NRC Emotion Lexicon. Lexicon dictionaries may provide binary presence/absence information or graduated scores, and may be static or dynamically expanded through semi-automated methods.

Strengths of lexicon-based methods include interpretability, computational efficiency, and low data requirements, making them suitable for rapid prototyping and deployment in low-resource languages. Limitations include difficulty handling context-dependent meaning, irony or sarcasm, polysemy, and domain shifts. Performance often lags behind data-driven machine learning approaches, but hybrid systems that combine lexicon signals with supervised models can achieve improved results. Applications span social media monitoring, customer feedback analysis, and lightweight language tooling.