LA - SAC o: A Study of Learning Approaches for Sentiments Analysis in C ode-Mixing Texts (2021)

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.

## introduction feelings, opinions, or reviews of customers in online shops or social media such as youtube, facebook, whatsapp, instagram, twitter, etc. are called sentiments. feedback or opinion about whatever is available on the internet can also be seen as sentiments @xcite . increase in the number of people using social media and online platforms have given rise to increasing amount of text data in general and sentiments or opinions in particular. sentiments or reviews posted by users affect the popularity of a post, and video in social media or a product in online shops @xcite @xcite . for example, positive sentiments could be a reason for making a post/product popular whereas negative reviews could go to the extent of rejecting or discarding a particular post/product. the sentiments extracted from users' posts and customers feedbacks tend to be valuable information not only for a particular user to be encouraged to watch a movie or buy a product but also for many social media companies and online shops or even movie makers, automakers etc. to improve their weaknesses and strengths. for instance, the negative feedbacks and sentiments gained from users about confusion in the new policy of whatsapp about sharing data and location, and business messaging has substantially reduced the popularity of this platform 1 . hence, sentiment analysis (sa), the task of automatically analyzing these sentiments or reviews posted by the users for the proper identification and classification of sentiments is becoming crucial these days @xcite . researchers motivated to explore sa have developed many tools and techniques to analyze sentiments written in rich resource languages such as english, spanish, etc. but, most of the people would like to express opinions in their native language in native script or other language script due to freedom to use any language and any script in online platforms @xcite . for example, south indian people may use dravidian languages (telugu, tamil, malayalam, and kannada) to post their sentiments @xcite . in the 2nd century bce, the dravidian languages were first attested to as a tamili 2 script inscribed however due to technical constraints and difficulty in using indian languages scripts users usually use roman script instead of their native scripts along with english words @xcite . mixing two or more languages in a text using a common language script or different languages scripts is called code-mixing and code-mixed texts are increasing with the popularity of social media and online shopping (ansari and govilkar, 2018; chakravarthi, 2020a). sa task is more challenging with code-mixed texts since the sentiments or reviews written in words of different languages increases the complexity of analyzing such texts due to code-mixing at various levels such as words, phrases, and sentences @xcite @xcite . further, as there are no rules governing the formation of code-mixed texts such texts usually contain incomplete and incorrect sentences, short forms of words and words with repetitive letters, e.g. hellooooo, aaaaaa, soooorrryyyy @xcite @xcite . these complexities give rise to building new features structures such as combined vocabulary and grammar of different languages which cannot be handled by conventional sa models as they fail to capture the meaning of the sentences in code-mixed text @xcite . in view of the complexities and challenges of code-mixed texts, this paper describes the three proposed sa 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 sa in tamil-english (ta-en) and malayalam-english (ma-en) code-mixed texts. while saco-ensemble and saco-keras models utilize a set of features comprised of char sequences, bytepair encoded words, and syntactic ngrams, saco-ulmfit model uses the knowledge obtained from a source model called language model in training the sentiment analysis classifier as target model. ## methodology using three different learning approaches, we propose three models namely, saco-ensemble -a ml approach, saco-keras -a dl approach and saco-ulmfit -a tl approach for the sa of ta-en and ma-en code-mixed texts. the first two models are trained on vectors generated using count vectorizer foot_1 from a feature set of char sequences, byte-pair encoding (bpemb) @xcite encoded words and syntactic n-grams. subsection 3.1 gives details of feature engineering module for ml and dl approaches. details of the proposed models are presented in section 3.2. ## conclusion and future work this paper describes the three proposed models, namely, saco-ensemble, saco-keras, and saco-ulmfit based on ml, dl and tl for the task of sa for ta-en and ma-en code-mixed texts. ta-en and ma-en datasets are used to train and evaluate the proposed models. further, romanized tamil and malayalam unannotated datasets from dakshina dataset are used to train the code-mixed lms. the results obtained using proposed models are compared with our previous model, saco-hvc and with the top 2 ranked models of "sentiment analysis for dravidian languages in code-mixed text" shared task in fire 2020. the results illustrate that saco-ensemble and saco-keras models have shown reasonable performance while saco-ulmfit exhibited an average performance. analysis of the results shows that saco-ensemble model based on ml approach using a feature set of char sequences, bpemb sub-words, and syntactic n-grams outperformed the other two proposed models for both datasets. however, the comparison of the proposed models with top 2 ranked models illustrates only a reasonable performance. a dl based saco-keras model fed with the same feature set as that of saco-ensemble model achieved results with very less difference compared to saco-ensemble model. this illustrates that there is no much difference between the performances of ml and dl approaches. the saco-ulmfit model based tl approach did not perform well which could be due to insufficient unannotated texts for training lms. as future work, we will collect more raw texts from youtube and twitter to build a rich code mixed lm that hopefully will improve the performance of saco-ulmfit. further, we plan to explore different feature sets and feature selection models to improve the performance of our proposed models.