AAAI Publications, Tenth International AAAI Conference on Web and Social Media

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iFeel 2.0: A Multilingual Benchmarking System for Sentence-Level Sentiment Analysis
Matheus Lima Diniz Araujo, João Paulo Diniz, Lucas Bastos, Elias Soares, Manoel Junior, Miller Ferreira, Filipe Ribeiro, Fabrício Benevenuto

Last modified: 2016-03-31

Abstract


Sentiment analysis became a hot topic, specially with the amount of opinions available in social media data. With the increasing interest in this theme, several methods have been proposed in the literature. Recent efforts have showed that there is no single method that always achieves the best prediction performance for different datasets. Additionally, novel methods have not being extensively compared with other methods and across different datasets, specially methods that are not designed to the English language. Consequently, researchers tend to accept any popular method as a valid methodology to measure sentiments, a practice that is usual in science. In this context, we propose iFeel 2.0, an online web system that implements 19 sentence-level sentiment analysis methods and allows users to easily label a dataset with all of them. iFeel aims at easing the comparison of new methods with baseline approaches and can also be helpful for those interested in using sentiment analysis, allowing them to choose an appropriate sentiment analysis method that works fine for a new dataset. We also incorporate a multiple language feature to allow methods designed for specific languages to be easily compared with a baseline approach that simply translates the input data to English and run these 19 methods. We hope this system can represent an important contribution to this field. Sentiment analysis became a hot topic, specially with the amount of opinions available in social media data.With the increasing interest in this theme, several methods have been proposed in the literature. Recent effortshave showed that there is no single method that always achieves the best prediction performance for different datasets. Additionally, novel methods have not being extensively compared with other methods and across different datasets, specially methods that are not designed to the English language.Consequently, researchers tend to accept any popular method as a valid methodology to measure sentiments, a practice that is usual in science.In this context, we propose iFeel 2.0, an online web system that implements 19 sentence-level sentiment analysis methods and allows users to easily label a dataset with all of them. iFeel aims at easing the comparison of new methods with baseline approaches and can also be helpful for those interested in using sentiment analysis, allowing them to choose an appropriate sentiment analysis method that works fine for a new dataset.We also incorporate a multiple language feature to allow methods designed for specific languages to be easily compared with a baseline approach that simply translates the input data to English and run these 19 methods. We hope this system can represent an important contribution to this field.

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