Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction

Proceedings of the 28th International Conference on Computational Linguistics, pages 6703–6709 Barcelona, Spain (Online), December 8-13, 2020 6703 Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction Appidi Abhinav Reddy, Vamshi Krishna Srirangam, Suhas Darsi and Manish Shrivastava Language Technologies Research Centre (LTRC) Kohli Centre on Intelligent Systems(KCIS) International Institute of Information Technology, Hyderabad, India. (abhinav.appidi, v.srirangam, darsi.suhas)@research.iiit.ac.in m.shrivastava@iiit.ac.in Abstract Emotion prediction is a critical task in the field of Natural Language Processing (NLP). There has been a significant amount of work done in emotion prediction for resource-rich languages. There has been work done on code-mixed social media corpus but not on emotion prediction of Kannada-English code-mixed Twitter data. In this paper, we analyze the problem of emotion prediction on corpus obtained from code-mixed Kannada-English extracted from Twitter anno- tated with their respective ‘Emotion’ for each tweet. We experimented with machine learning prediction models using features like Character N-Grams, Word N-Grams, Repetitive characters, and others on SVM and LSTM on our corpus, which resulted in an accuracy of 30% and 32%, respectively. 1 Introduction Identification and analysis of emotions in user-generated data in social media like Twitter, Facebook, Reddit, etc., is essential in understanding the daily trends and human behavior. Emotion prediction aims at identifying and analyzing such emotions like ‘Happy, ‘Sad,’ ‘Angry,’ ‘Fear,’ ‘Surprise,’ and ‘Disgust’ types present in the text. Original works were focussed more on monolingual text (Alm et al., 2005; Chen et al., 2010) due to the large-scale availability of monolingual texts. India has twenty-three significant languages with over seven hundred and twenty dialects. The major- ity of people are multilingual, and they tend to mix words from different languages in speech and written text. This method of interchanging languages is commonly addressed by terms ‘Code-switching’ and ‘Code-mixing’ as described by Lipski (1978). Code-mixing refers to the use of words from different languages in the same sentence. Code-switching refers to the use of words or phrases from different languages within the same speech context. We can understand the difference between code-switching and code-mixing from the positions of al- tered elements. Code-mixing refers to the intrasentential modification of codes, whereas code-switching refers to the intersentential modification of codes. We observe code-switching and code-mixing fre- quently on social media platforms. Since the available resources are limited for Kannada-English code- mixed text, we primarily focus on the creation of corpus and annotating the code-mixed tweets with their respective emotions, in this paper. Here are some examples from a corpus of code-mixed Kannada-English generated from Twitter data and its translation in English. T1: “Nam placement officer helidda ee thara helidre ond company lu kelsa sigalla anta...I had 2 offers before I left college, ondu IT innondu core...” Translation: “Our placement officer said,‘if you talk like this, you wont get a single job.’ I had 2 offers before i left college. One was IT, the other was core.” This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/. 6704 T2: “Eshwarappa …

Content truncated to 500 words