SENTIWORDNET is a publicly available lexical resource for opinion mining, developed by Andrea Esuli and Fabrizio Sebastiani. It assigns three numerical scores to each WordNet synset: Obj(s), Pos(s), and Neg(s), representing how objective, positive, and negative the terms in the synset are. The scores range from 0.0 to 1.0 and sum to 1.0 for each synset. The method used to develop SENTIWORDNET involves quantitative analysis of synset glosses and semi-supervised synset classification. The three scores are derived by combining results from eight ternary classifiers, each with similar accuracy but different classification behavior. The development of SENTIWORDNET is based on previous work on determining the opinion-related properties of terms. The method involves training ternary classifiers to classify synsets as Positive, Negative, or Objective. The classifiers are trained using a semi-supervised approach, where a small subset of labeled synsets is manually selected, and the rest are labeled automatically based on lexical relations. The scores for a synset are determined by the proportion of classifiers that assign it a particular label. The resource is freely available for research and includes a Web-based graphical user interface. SENTIWORDNET provides a fine-grained representation of opinion-related properties, allowing for more accurate sentiment analysis. The scores are derived from a combination of classifiers, ensuring a balanced and reliable assessment of the opinion-related properties of terms. The resource is evaluated using a subset of manually labeled synsets, which will serve as a gold standard for future evaluations. The results indicate that SENTIWORDNET is a valuable tool for opinion mining, offering a comprehensive and detailed representation of opinion-related properties in WordNet synsets.SENTIWORDNET is a publicly available lexical resource for opinion mining, developed by Andrea Esuli and Fabrizio Sebastiani. It assigns three numerical scores to each WordNet synset: Obj(s), Pos(s), and Neg(s), representing how objective, positive, and negative the terms in the synset are. The scores range from 0.0 to 1.0 and sum to 1.0 for each synset. The method used to develop SENTIWORDNET involves quantitative analysis of synset glosses and semi-supervised synset classification. The three scores are derived by combining results from eight ternary classifiers, each with similar accuracy but different classification behavior. The development of SENTIWORDNET is based on previous work on determining the opinion-related properties of terms. The method involves training ternary classifiers to classify synsets as Positive, Negative, or Objective. The classifiers are trained using a semi-supervised approach, where a small subset of labeled synsets is manually selected, and the rest are labeled automatically based on lexical relations. The scores for a synset are determined by the proportion of classifiers that assign it a particular label. The resource is freely available for research and includes a Web-based graphical user interface. SENTIWORDNET provides a fine-grained representation of opinion-related properties, allowing for more accurate sentiment analysis. The scores are derived from a combination of classifiers, ensuring a balanced and reliable assessment of the opinion-related properties of terms. The resource is evaluated using a subset of manually labeled synsets, which will serve as a gold standard for future evaluations. The results indicate that SENTIWORDNET is a valuable tool for opinion mining, offering a comprehensive and detailed representation of opinion-related properties in WordNet synsets.