L 3 C ube M aha S ent: A M arathi Tweet-based Sentiment Analysis Dataset (2021)

Abstract: Sentiment analysis is one of the most fundamental tasks in Natural Language Processing. Popular languages like English, Arabic, Russian, Mandarin, and also Indian languages such as Hindi, Bengali, Tamil have seen a significant amount of work in this area. However, the Marathi language which is the third most popular language in India still lags behind due to the absence of proper datasets. In this paper, we present the first major publicly available Marathi Sentiment Analysis Dataset - L3CubeMahaSent. It is curated using tweets extracted from various Maharashtrian personalities’ Twitter accounts. Our dataset consists of ~16,000 distinct tweets classified in three broad classes viz. positive, negative, and neutral. We also present the guidelines using which we annotated the tweets. Finally, we present the statistics of our dataset and baseline classification results using CNN, LSTM, ULMFiT, and BERT based models.

## introduction the use of social media has seen a sharp upward trend in recent years. it plays a big role in forming and shaping the views of people on various issues. from sharing facts and opinions to voicing dis sent and grievances, the platform has gained popu larity amongst many users @xcite . twitter is a significant social media plat form. it has been quite popular in india for the past few years. it has been used by many politicians, journalists, and activists to connect with people di rectly. these kinds of interactions are generally strong on emotions, and can be used for develop ing sentiment analysis systems @xcite . such systems have proven to be important for political analysis as well as identifying and curbing more complex issues such as fake news, harassment, hate speech, and bullying (schmidt and wiegand, @xcite . in this work, we consider basic sentiment analysis or polarity identification tasks. popular languages such as english, arabic, rus sian, mandarin @xcite as well as indian languages such as hindi, bengali and tamil have been ex plored on the sentiment task for a long time @xcite . many resources such as properly anno tated datasets, sentiwordnets, annotation guide lines have been developed for these languages @xcite . alternatively due to the low resource na ture of many languages, translated versions of the english datasets were used for analysis @xcite . however, such translated datasets are often noisy due to the limitation of current translation systems for low resource languages. marathi is an indian language spoken by around 83 million people and ranks as the third most spo ken language in india. but surprisingly, there is no significant work or resource for the task of senti ment analysis in marathi @xcite . a sentiment analysis dataset curated by iitbombay is available, but it has a very small size consist ing of only 150 samples @xcite . in this paper, we present l3cubemahasent foot_0 the largest publicly available marathi sentiment anal ysis dataset to date. this dataset is gathered using twitter. our work is summarized as follows: ## related work sentiment analysis is a fundamental task of nat ural language processing @xcite . the absence of a proper sentiment analysis dataset for the marathi language has led to limited re search in this area. in this section, we will review some of the works introducing data resources in in dian and other languages. @xcite presented an approach for crosslingual sentiment analysis using linked wordnets for marathi and hindi languages. for this purpose, they used vari ous blogs and travel editorials as a dataset which consisted of about 75 positive and 75 negative reviews. the wordnet approach showed an im provement of 1415 percent over the approach us ing a bilingual dictionary. the marathi dataset cre ated in this work is very small and cannot be used to train existing deep learning algorithms. bhardwaj et al. (2020) presented a hostility detec tion dataset in hindi. data was collected from various online platforms like twitter, facebook, whatsapp, etc., and was benchmarked using ma chine learning algorithms namely, support vector machine (svm), decision tree, random forest, and logistic regression. they also labeled each hostile post as either fake, hateful, offensive, or defama tion. patra et al. (2018) presented details of a shared task in a competition on sentiment analysis of code mixed data pairs of hindienglish and bengali english. the best performing team used svm for sentence classification. the sentiment analysis of codemixed englishhindi and englishmarathi text is also studied in (ansari and govilkar, 2018). nabil et al. (2015) introduced arabic sentiments tweets dataset consisting of 10,000 tweets classi fied as objective, subjective positive, subjective negative, and subjective mixed. they tried 4class ## conclusion in this paper, we have presented l3cubemahasent the first major publicly available dataset for marathi sentiment analysis which consists of ∼16000 distinct tweets. we also describe the an notation policy which we used for manually label ing the entire dataset. we performed 2class and 3class sentiment classification to provide a bench mark for future studies. the deep learning mod els used for sentiment prediction were cnn, bi lstm, ulmfit, mbert, and indicbert. the publicly available marathi fasttext embeddings were used with wordbased models. we report the best accuracy using indicbert and cnn with in dic fasttext word embeddings. we hope that our dataset will play a crucial role in advancing nlp research for the marathi language.