Sentiment Analysis on Code-Switched D ravidian Languages with Kernel Based Extreme Learning Machines (2022)

Abstract: Code-switching refers to the textual or spoken data containing multiple languages. Application of natural language processing (NLP) tasks like sentiment analysis is a harder problem on code-switched languages due to the irregularities in the sentence structuring and ordering. This paper shows the experiment results of building a Kernel based Extreme Learning Machines(ELM) for sentiment analysis for code-switched Dravidian languages with English. Our results show that ELM performs better than traditional machine learning classifiers on various metrics as well as trains faster than deep learning models. We also show that Polynomial kernels perform better than others in the ELM architecture. We were able to achieve a median AUC of 0.79 with a polynomial kernel.

## introduction because of the expansion of user-generated material, it is now possible to automatically detect linked attitudes. a "sentiment" is a good or negative opinion, emotion, feeling, or thinking conveyed by a sentiment bearer (user). in general, sentiment analysis attempts to extract certain sentiments from text automatically @xcite @xcite @xcite . sentiment analysis seeks to analyse textual patterns in order to find a sentiment at the word, phrase, or document level. sentiment analysis is widely used in a variety of sectors today, including public-health monitoring, electoral patterns, predicting terrorist actions, and social network analysis @xcite . dravidian languages, tamil, kannada and malayalam are widely spoken by over 250 million people, but still is a a sparse language for nlp tasks @xcite @xcite . dravidian languages are a spoken mostly in southern india, north-east sri lanka, and south-west pakistan (anita and @xcite . there have been tiny but important immigrant groups in mauritius, myanmar, singapore, malaysia, indonesia, the philippines, the united kingdom, australia, france, canada, germany, south africa, and the united states since the colonial era @xcite @xcite . tamil is a member of the southern branch of the dravidian languages, a group of about 26 languages indigenous to the indian subcontinent. it is also classed as a member of the tamil language family, which contains the languages of around 35 ethnolinguistic groups, including the irula and yerukula languages. the influence of english in the regions where these languages are spoken is higher due to the colonial history and the medium of schooling @xcite . however the ease of expression of sentiments switches between the words in the dravidian language and english with most of the bilinguists versatile in both, especially on online social platforms @xcite . the sentiment analysis of text written in code-switched language between the dravidian languages and english is analysed in this paper through a novel kernel based elm. ## dataset we conducted our experiments on the labelled data from the youtube comments using three codemixed benchmark datasets published for dravidian languages. kannada code-switched corpus, published by hande et al. (2020) was our primary source. similarly tamil code-switched corpus, published by chakravarthi et al. (2020b) was used. for malayalam code-switched corpus, we used the data published by chakravarthi et al. (2020a). the multi-class dataset contains manually labelled sentiments for code-switched data. this dataset is an imbalanced one with a skew towards the labels containing 'positive' sentiments. the split between various classes is shown in ## experiment setup a multi-staged pipeline was setup for our experiments as depicted in figure 1 . ## observations and analysis the combination of features and data-balancing techniques from the pipeline was evaluated separately with each of the elm kernels. performance metrics like accuracy as well as the receiver operating characteristic (roc) curve was determined for each of the dataset. we also measured the area under the roc curve (auc) for each combination of the dataset as observed in