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A multilingual fuzzy approach for classifying Twitter data using fuzzy logic and semantic similarity

Youness Madani 1 Mohammed Erritali 1 Jamaa Bengourram 1 Francoise Sailhan 2 
2 CEDRIC - ROC - CEDRIC. Réseaux et Objets Connectés
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : In recent years, the classification of the social networks' data has witnessed an increasing interest. It aims at extracting opinions, emotions and attitudes from social networks' data such as Facebook comments or tweets. This new scientific research area is called sentiment analysis. (It is sometimes called opinion mining.) In this article, we propose a new method to classify tweets into three classes: positive, negative or neutral. The proposed method is a new hybrid approach based on the fuzzy logic with its three important steps (fuzzification, Rule Inference/aggregation and defuzzification) and the concepts of information retrieval system (IRS) by calculating the semantic similarity between a tweet to classify and two opinion documents (one for the positive opinion words and another one for the negative opinion words) using the WordNet dictionary. To remedy the calculation time’s problem—if we have a huge dataset of tweets—we decide to parallelize our work using the Hadoop framework with its distributed file system (HDFS) and the MapReduce programming model. The experimental results show that our approach outperforms some other methods from the literature as well as by using the fuzzy logic, we improve the results of the classification.
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Submitted on : Saturday, December 4, 2021 - 4:13:20 PM
Last modification on : Wednesday, September 28, 2022 - 5:52:47 AM




Youness Madani, Mohammed Erritali, Jamaa Bengourram, Francoise Sailhan. A multilingual fuzzy approach for classifying Twitter data using fuzzy logic and semantic similarity. Neural Computing and Applications, 2019, 32 (12), pp.8655-8673. ⟨10.1007/s00521-019-04357-9⟩. ⟨hal-03466138⟩



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