On the Effect of Emotion Identification from Limited Translated Text Samples Using Computational Intelligence

Abstract

Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results show that when English text is translated to French, classification accuracy is higher than others, i.e., 99.04%. Whereas, when the same is translated to Chinese language, its detection rate is lowest among target languages. It is concluded that emotions remain preserved after translation to some extent. Framework consists of TFIDF features. PCA and Discriminant Analysis perform good to detect emotions from translated data.