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    <abstract>Substantial amount of text data which is increasingly being generated and shared on the internet and social media every second affect the society positively or negatively almost in any aspect of online world and also business and industries. Sentiments/opinions/reviews’ of users posted on social media are the valuable information that have motivated researchers to analyze them to get better insight and feedbacks about any product such as a video in Instagram, a movie in Netflix, or even new brand car introduced by BMW. Sentiments are usually written using a combination of languages such as English which is resource rich and regional languages such as Tamil, Kannada, Malayalam, etc. which are resource poor. However, due to technical constraints, many users prefer to pen their opinions in Roman script. These kinds of texts written in two or more languages using a common language script or different language scripts are called code-mixing texts. Code-mixed texts are increasing day-by-day with the increase in the number of users depending on various online platforms. Analyzing such texts pose a real challenge for the researchers. In view of the challenges posed by the code-mixed texts, this paper describes three proposed models namely, SACo-Ensemble, SACo-Keras, and SACo-ULMFiT using Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL) approaches respectively for the task of Sentiments Analysis in Tamil-English and Malayalam-English code-mixed texts.</abstract>
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%0 Conference Proceedings
%T LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts
%A Balouchzahi, Fazlourrahman
%A Shashirekha, H. L.
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Kumar M, Anand
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%S Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv
%F balouchzahi-shashirekha-2021-la
%X Substantial amount of text data which is increasingly being generated and shared on the internet and social media every second affect the society positively or negatively almost in any aspect of online world and also business and industries. Sentiments/opinions/reviews’ of users posted on social media are the valuable information that have motivated researchers to analyze them to get better insight and feedbacks about any product such as a video in Instagram, a movie in Netflix, or even new brand car introduced by BMW. Sentiments are usually written using a combination of languages such as English which is resource rich and regional languages such as Tamil, Kannada, Malayalam, etc. which are resource poor. However, due to technical constraints, many users prefer to pen their opinions in Roman script. These kinds of texts written in two or more languages using a common language script or different language scripts are called code-mixing texts. Code-mixed texts are increasing day-by-day with the increase in the number of users depending on various online platforms. Analyzing such texts pose a real challenge for the researchers. In view of the challenges posed by the code-mixed texts, this paper describes three proposed models namely, SACo-Ensemble, SACo-Keras, and SACo-ULMFiT using Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL) approaches respectively for the task of Sentiments Analysis in Tamil-English and Malayalam-English code-mixed texts.
%U https://aclanthology.org/2021.dravidianlangtech-1.14
%P 109-118

Markdown (Informal)

[LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts](https://aclanthology.org/2021.dravidianlangtech-1.14) (Balouchzahi & Shashirekha, DravidianLangTech 2021)

• LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts (Balouchzahi & Shashirekha, DravidianLangTech 2021)

ACL

• Fazlourrahman Balouchzahi and H L Shashirekha. 2021. LA-SACo: A Study of Learning Approaches for Sentiments Analysis inCode-Mixing Texts. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 109–118, Kyiv. Association for Computational Linguistics.