Abstract

Code-switching is commonly used in the free-form text environment, such as social media, and it is especially favored in emotion expressions. Emotions in codeswitching texts differ from monolingual texts in that they can be expressed in either monolingual or bilingual forms. In this paper, we first utilize two kinds of knowledge, i.e. bilingual and sentimental information to bridge the gap between different languages. Moreover, we use a term-document bipartite graph to incorporate both bilingual and sentimental information, and propose a label propagation based approach to learn and predict in the bipartite graph. Empirical studies demonstrate the effectiveness of our proposed approach in detecting emotion in code-switching texts.

Highlights

  • With the rapid development of Web 2.0, emotion analysis in social media has become of great value to market predictions and analysis (Liu et al, 2013; Lee et al, 2014)

  • Our first group of experiments is to investigate whether our proposed label propagation model with both bilingual and sentimental information can improve emotion detection in code-switching texts

  • We adopt F1-Measure (F1.) to measure the performance of each model in the respective emotions

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Summary

Introduction

With the rapid development of Web 2.0, emotion analysis in social media has become of great value to market predictions and analysis (Liu et al, 2013; Lee et al, 2014). In informal settings such as micro-blogs, emotions are often expressed by a mixture of different natural languages. Such a mixture of language is called codeswitching. Code-switching text is defined as text that contains more than one language (code). It is a common phenomenon in multilingual communities (Auer, 1999; Adel et al, 2013). [E1-E3] are three examples of codeswitching emotional posts containing both Chi-

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