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Evomsa: A multilingual evolutionary approach for sentiment analysis - 2020

Evomsa: A Multilingual Evolutionary Approach For Sentiment Analysis

Research Area:  Machine Learning


Sentiment analysis (SA) is a task related to understanding peoples feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages, e.g., English, Spanish, and Arabic, among others. In this contribution, we propose an SA system, namely EvoMSA, that unifies our participating systems in various SA competitions, making it domain-independent and multilingual by processing text using only language-independent techniques. EvoMSA is a classifier, based on Genetic Programming that works by combining the output of different text classifiers to produce the final prediction. We analyzed EvoMSA on different SA competitions to provide a global overview of its performance. The results indicated that EvoMSA is competitive obtaining top rankings in several SA competitions. Furthermore, we performed an analysis of EvoMSAs components to measure their contribution to the performance; the aim was to facilitate a practitioner or newcomer to implement a competitive SA classifier. Finally, it is worth to mention that EvoMSA is available as open-source software.


Author(s) Name:  Mario Graff; Sabino Miranda-Jimenez; Eric S. Tellez; Daniela Moctezuma

Journal name:  IEEE Computational Intelligence Magazine

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/MCI.2019.2954668

Volume Information:  ( Volume: 15, Issue: 1, Feb. 2020) Page(s): 76 - 88