A Hybrid Multilingual Sentiment Classification Using Fuzzy Logic and Semantic Similarity

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 915)


Classifying tweets into classes (Positive, Negative or neutral) or extracting their sentiments know in recent years a great development, and researchers try to find new methods and approaches that give good results. In this paper, we propose a new hybrid approach based on the semantic similarity using the WordNet dictionary and the fuzzy logic with its three important steps (Fuzzification, Rule Inference/aggregation and Defuzzification) for classifying tweets into three classes: positive, negative or neutral. The experimental results show that our approach outperforms some other methods from the literature.


Opinion mining Sentiment analysis Twitter Fuzzy logic Information retrieval systems Semantic similarity Wordnet Big data Hadoop 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Sciences and TechnicsSultan Moulay Slimane UniversityBeni MellalMorocco

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