%0 Conference Proceedings
%T A Study of Synthetic Oversampling for Twitter Imbalanced Sentiment Analysis
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%A Ah-Pine, Julien
%A Soriano-Morales, Edmundo-Pavel
%< avec comité de lecture
%B Workshop on Interactions between Data Mining and Natural Language Processing (DMNLP 2016)
%C Riva del Garda, Italy
%3 Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing
%8 2016-09-23
%D 2016
%K Synthetic sampling
%K Sentiment analysis
%K Social media
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Computer Science [cs]/Document and Text Processing
%Z Computer Science [cs]/Social and Information Networks [cs.SI]Conference papers
%X The majority of Twitter sentiment analysis systems implicitly assume that the class distribution is balanced while in practice it is usually skewed. We argue that Twitter opinion mining using learning methods should be addressed in the framework of imbalanced learning. In this work, we present a study of synthetic oversampling techniques for tweet-polarity classification. The experiments we conducted on three publicly available datasets show that these methods can improve the recognition of the minority class as well as the geometric mean criterion.
%G English
%2 https://hal.science/hal-01504684/document
%2 https://hal.science/hal-01504684/file/paper_dmnlp_16.pdf
%L hal-01504684
%U https://hal.science/hal-01504684
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ ERIC
%~ LYON2
%~ UDL
%~ UNIV-LYON