LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1288–1293 Barcelona, Spain (Online), December 12, 2020. 1288 LT3 at SemEval-2020 Task 9: Cross-lingual Embeddings for Sentiment Analysis of Hinglish Social Media Text Pranaydeep Singh and Els Lefever LT3, Language and Translation Technology Team Department of Translation, Interpreting and Communication – Ghent University Groot-Brittanni¨elaan 45, 9000 Ghent, Belgium firstname.lastname@ugent.be Abstract This paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. The first approach uses cross-lingual embeddings resulting from projecting Hinglish and pre-trained English FastText word embeddings in the same space. The second approach incorporates pre-trained English embeddings that are incrementally retrained with a set of Hinglish tweets. The results show that the second approach performs best, with an F1-score of 70.52% on the held-out test data. 1 Introduction The emergence of Web 2.0 has allowed people to easily share their opinion on a variety of topics. Whereas in the past companies and policy makers used to conduct surveys to know the opinion of people on certain products, services or policies, they now have access to a wide range of easily accessible data to gather the public’s sentiment (Liu, 2012). To automatically derive opinions from text, researchers have designed the task of sentiment analysis (SA), which deals with “the computational study of opinions, sentiments and emotions expressed in text” (Kumar and Sebastian, 2012). An important challenge when applying sentiment analysis to user- generated data is caused by code-mixing and non-standard language use. In linguistics, code-mixing traditionally refers to the embedding of linguistic units (phrases, words, morphemes) into an utterance of another language (Myers-Scotton, 1993). The phenomenon of code-mixing frequently occurs in spoken languages, such as the combination of English with Hindi (so-called “Hinglish”), or English with Spanish (so-called “Spanglish”). More recently, code-mixing is increasingly being used in written text as well, as non-native English speakers often combine English with their mother tongue when using social media. In the case of Hinglish, an additional challenge is added because people do not only mix languages, but also use English phonetic typing to write Hindi words instead of using the Devanagari script. In order to investigate Sentiment Analysis for Code-mixed Social Media Text, Patwa et al. (2020) have organized the SentiMix task, which consists in predicting the sentiment of a given code-mixed tweet. The sentiment labels are positive, negative, or neutral, and the code-mixed languages are English-Hindi and English-Spanish. Besides the sentiment labels, the authors also provide language tags at the word level, being en (English), spa (Spanish), hi (Hindi), mixed, and univ (e.g., symbols, @ mentions, hashtags). This paper presents our research performed for the “Hinglish” (English-Hindi) sentiment analysis subtask of SemEval-2020 Task 9. The remainder of this paper is structured as follows. In Section 2, we provide an overview of the related research. Section 3 describes the data used to train and evaluate the system. Section 4 introduces the two approaches developed …

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