The Role of WordNet Similarity in the Affective Analysis Pipeline

Alejandra Segura Navarrete, Christian Vidal Castro, Clemente Rubio Manzano, Claudia Martínez Araneda


Sentiment Analysis (SA) is a useful and important discipline in Computer Science, as it allows having a knowledge base about the opinions of people regarding a topic. This knowledge is used to improve decision-making processes. One approach to achieve this is based on the use of lexical knowledge structures. In particular, our aim is to enrich an affective lexicon by the analysis of the similarity relationship between words. The hypothesis of this work states that the similarities of the words belonging to an affective category, with respect to any other word, behave in a homogeneous way within each affective category. The experimental results show that words of a same affective category have a homogeneous similarity with an antonym, and that the similarities of these words with any of their antonyms have a low variability. The novelty of this paper is that it builds the bases of a mechanism that allows incorporating the intensity in an affective lexicon automatically.


Natural language processing, computational linguistics, affective computing, sentiment analysis, knowledge representation

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