Sentiment Analysis (SA) in Dravidian codemixed text is a hot research area right now. In this regard, the “Second Shared Task on SA in Code-mixed Tamil and Tulu” at Dravidian- LangTech (EACL-2024) is organized. Two tasks namely SA in Tamil-English and Tulu- English code-mixed data, make up this shared assignment. In total, 64 teams registered for the shared task, out of which 19 and 17 systems were received for Tamil and Tulu, respectively. The performance of the systems submitted by the participants was evaluated based on the macro F1-score. The best method obtained macro F1-scores of 0.260 and 0.584 for code-mixed Tamil and Tulu texts, respectively.

Hate and offensive language detection is the task of detecting hate and/or offensive content targetting a person or a group of people. Despite many efforts to detect hate and offensive content on social media platforms, the problem remains unsolved till date due to the ever growing social media users and their creativity to create and spread hate and offensive content. To address the automatic detection of hate and offensive content on social media platforms, this paper describes the learning models submitted by our team - MUCS to “Hate and Offensive Language Detection in Telugu Codemixed Text (HOLD-Telugu): DravidianLangTech@EACL” - a shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024 invites the research community to address the challenges of detecting hate and offensive language in Telugu language. In this paper, we - team MUCS, describe the learning models submitted to the above mentioned shared task. Three models: Three models: i) LR model - a Machine Learning (ML) algorithm fed with TF-IDF of n-grams of subword, word and char_wb are in the range (1, 3), (1, 3), and (1, 5), ii) TL- a pretrained BERT models which makes use of Hate-speech-CNERG/bert-base-uncased-hatexplain model and iii) Ensemble model which is the combination of ML classifieres( MNB, LR, GNB) trained CountVectorizer with word and char ngrams of range (1, 3) and (1, 5) respectively. Proposed LR model trained with TF-IDF of subword, word and char n-grams outperformed the other models with macro F1 scores of 0.6501 securing 15th rankin the shared task for Telugu text.

Sentiment Analysis (SA) is a field of computational study that analyzes and understands people’s opinions, attitudes, and emotions toward any entity. A review of an entity can be written about an individual, an event, a topic, a product, etc., and such reviews are abundant on social media platforms. The increasing number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. In spite of this, SA of social media text is challenging because the code-mixed text is complex. To address SA in code-mixed Tamil and Tulu text, this paper describes the Machine Learning (ML) models submitted by our team - MUCS to “Sentiment Analysis in Tamil and Tulu - Dravidian- LangTech” - a shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024. Linear Support Vector classifier (LinearSVC) and ensemble of 5 ML classifiers (k Nearest Neighbour (kNN), Stochastic Gradient Descent (SGD), Logistic Regression (LR), LinearSVC, and Random Forest Classifier (RFC)) with hard voting trained using concatenated features obtained from word and character n-ngrams vectoized from Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and CountVectorizer. Further, Gridsearch algorithm is employed to obtain optimal hyperparameter values.The proposed ensemble model obtained macro F1 scores of 0.260 and 0.550 for Tamil and Tulu languages respectively.

This paper provides a comprehensive summary of the “Homophobia and Transphobia Detection in Social Media Comments” shared task, which was held at the LT-EDI@EACL 2024. The objective of this task was to develop systems capable of identifying instances of homophobia and transphobia within social media comments. This challenge was extended across ten languages: English, Tamil, Malayalam, Telugu, Kannada, Gujarati, Hindi, Marathi, Spanish, and Tulu. Each comment in the dataset was annotated into three categories. The shared task attracted significant interest, with over 60 teams participating through the CodaLab platform. The submission of prediction from the participants was evaluated with the macro F1 score.

Misogynistic memes are a category of memes which contain disrespectful language targeting women on social media platforms. Hence, detecting such memes is necessary in order to maintain a healthy social media environment. To address the challenges of detecting misogynistic memes, “Multitask Meme classification - Unraveling Misogynistic and Trolls in Online Memes: LT-EDI@EACL 2024” shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024, invites researchers to develop models to detect misogynistic memes in Tamil and Malayalam. The shared task has two subtasks, and in this paper, we - team MUCS, describe the learning models submitted to Task 1 - Identification of Misogynistic Memes in Tamil and Malayalam. As memes represent multi-modal data of image and text, three models: i) Bidirectional Encoder Representations from Transformers (BERT)+Residual Network (ResNet)-50, ii) Multilingual Representations for Indian Languages (MuRIL)+ResNet-50, and iii) multilingual BERT (mBERT)+ResNet50, are proposed based on joint representation of text and image, for detecting misogynistic memes in Tamil and Malayalam. Among the proposed models, mBERT+ResNet-50 and MuRIL+ ResNet-50 models obtained macro F1 scores of 0.73 and 0.87 for Tamil and Malayalam datasets respectively securing 1st rank for both the datasets in the shared task.

Homophobic/Transphobic (H/T) content includes hatred and discriminatory comments directed at Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ) individuals on social media platforms. As this unfavourable perception towards LGBTQ individuals may affect them physically and mentally, it is necessary to detect H/T content on social media. This demands automated tools to identify and address H/T content. In view of this, in this paper, we - team MUCS describe the learning models submitted to “Homophobia/Transphobia Detection in social media comments:LT-EDI@EACL 2024” shared task at European Chapter of the Association for Computational Linguistics (EACL) 2024. The learning models: i) Homo_Ensemble - an ensemble of Machine Learning (ML) algorithms trained with Term Frequency-Inverse Document Frequency (TFIDF) of syllable n-grams in the range (1, 3), ii) Homo_TL - a model based on Transfer Learning (TL) approach with Bidirectional Encoder Representations from Transformers (BERT) models, iii) Homo_probfuse - an ensemble of ML classifiers with soft voting trained using sentence embeddings (except for Hindi), and iv) Homo_FSL - Few-Shot Learning (FSL) models using Sentence Transformer (ST) (only for Tulu), are proposed to detect H/T content in the given languages. Among the models submitted to the shared task, the models that performed better for each language include: i) Homo_Ensemble model obtained macro F1 score of 0.95 securing 4th rank for Telugu language, ii) Homo_TL model obtained macro F1 scores of 0.49, 0.53, 0.45, 0.94, and 0.95 securing 2nd, 2nd, 1st, 1st, and 4th ranks for English, Marathi, Hindi, Kannada, and Gujarathi languages, respectively, iii) Homo_probfuse model obtained macro F1 scores of 0.86, 0.87, and 0.53 securing 2nd, 6th, and 2nd ranks for Tamil, Malayalam, and Spanish languages respectively, and iv) Homo_FSL model obtained a macro F1 score of 0.62 securing 2nd rank for Tulu dataset.

In recent years, there has been a growing focus on Sentiment Analysis (SA) of code-mixed Dravidian languages. However, the majority of social media text in these languages is code-mixed, presenting a unique challenge. Despite this, there is currently lack of research on SA specifically tailored for code-mixed Dravidian languages, highlighting the need for further exploration and development in this domain. In this view, “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)- 2023 is organized. This shred consists two language tracks: code-mixed Tamil and Tulu and Tulu text is first ever explored in public domain for SA. We describe the task, its organization, and the submitted systems followed by the results. 57 research teams registered for the shared task and We received 27 systems each for code-mixed Tamil and Tulu texts. The performance of the systems (developed by participants) has been evaluated in terms of macro average F1 score. The top system for code-mixed Tamil and Tulu texts scored macro average F1 score of 0.32, and 0.542 respectively. The high quality and substantial quantity of submissions demonstrate a significant interest and attention in the analysis of code-mixed Dravidian languages. However, the current state of the art in this domain indicates the need for further advancements and improvements to effectively address the challenges posed by code-mixed Dravidian language SA.

Sentiment Analysis (SA) is a field of computational study that focuses on analyzing and understanding people’s opinions, attitudes, and emotions towards an entity. An entity could be an individual, an event, a topic, a product etc., which is most likely to be covered by reviews and such reviews can be found in abundance on social media platforms. The increase in the number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. However, SA of social media text is challenging due to the complex nature of the code-mixed text. To tackle this issue, in this paper, we team MUCS, describe learning models submitted to “Sentiment Analysis in Tamil and Tulu” -DravidianLangTech@Recent Advances In Natural Language Processing (RANLP) 2023. Using fastText embeddings to train the Machine Learning (ML) models to perform SA in code-mixed Tamil and Tulu texts, the proposed methodology exhibited F1 scores of 0.14 and 0.204 securing 13th and 15th rank for Tamil and Tulu texts respectively.

Abusive language detection in user-generated online content has become a pressing concern due to its negative impact on users and challenges for policy makers. Online platforms are faced with the task of moderating abusive content to mitigate societal harm, adhere to legal requirements, and foster inclusivity. Despite numerous methods developed for automated detection of abusive language, the problem continues to persist. This ongoing challenge necessitates further research and development to enhance the effectiveness of abusive content detection systems and implement proactive measures to create safer and more respectful online spaces. To address the automatic detection of abusive languages in social media platforms, this paper describes the models submitted by our team - MUCS to the shared task “Abusive Comment Detection in Tamil and Telugu” at DravidianLangTech - in Recent Advances in Natural Language Processing (RANLP) 2023. This shared task addresses the abusive comment detection in code-mixed Tamil, Telugu, and romanized Tamil (Tamil-English) texts. Two distinct models: i) AbusiveML - a model implemented utilizing Linear Support Vector Classifier (LinearSVC) algorithm fed with n-grams of words and character sequences within word boundary (char_wb) features and ii) AbusiveTL - a Transfer Learning (TL ) model with three different Bidirectional Encoder Representations from Transformers (BERT) models along with random oversampling to deal with data imbalance, are submitted to the shared task for detecting abusive language in the given code-mixed texts. The AbusiveTL model fared well among these two models, with macro F1 scores of 0.46, 0.74, and 0.49 for code-mixed Tamil, Telugu, and Tamil-English texts respectively.

Sentiment Analysis (SA) examines the subjective content of a statement, such as opinions, assessments, feelings, or attitudes towards a subject, person, or a thing. Though several models are developed for SA in high-resource languages like English, Spanish, German, etc., uder-resourced languages like Dravidian languages are less explored. To address the challenges of SA in low resource Dravidian languages, in this paper, we team MUNLP describe the models submitted to “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)-2023. n-gramsSA, EmbeddingsSA and BERTSA are the models proposed for SA shared task. Among all the models, BERTSA exhibited a maximum macro F1 score of 0.26 for code-mixed Tamil texts securing 2nd place in the shared task. EmbeddingsSA exhibited maximum macro F1 score of 0.53 securing 2nd place for Tulu code-mixed texts.

User-generated social media texts are a blend of resource-rich languages like English and low-resource Dravidian languages like Tamil, Kannada, Tulu, etc. These texts referred to as code-mixing texts are enriching social media since they are written in two or more languages using either a common language script or various language scripts. However, due to the complex nature of the code-mixed text, in this paper, we - team MUCSD, describe a Machine learning (ML) models submitted to “Sentiment Analysis in Tamil and Tulu” shared task at DravidianLangTech@RANLP 2023. The proposed methodology makes use of ML models such as Linear Support Vector Classifier (LinearSVC), LR, and ensemble model (LR, DT, and SVM) to perform SA in Tamil and Tulu languages. The proposed LinearSVC model’s predictions submitted to the shared tasks, obtained 8th and 9th rank for Tamil-English and Tulu-English respectively.

Social media is widely used to spread fake news, which affects a larger population. So it is considered as a very important task to detect fake news spread on social media platforms. To address the challenges in the identification of fake news in the Malayalam language, in this paper, we - team MUCS, describe the Machine Learning (ML) models submitted to “Fake News Detection in Dravidian Languages” at DravidianLangTech@RANLP 2023 shared task. Three different models, namely, Multinomial Naive Bayes (MNB), Logistic Regression (LR), and Ensemble model (MNB, LR, and SVM) are trained using Term Frequency - Inverse Document Frequency (TF-IDF) of word unigrams. Among the three models ensemble model performed better with a macro F1-score of 0.83 and placed 3rd rank in the shared task.

Hope plays a significant role in shaping human thoughts and actions and hope content has received limited attention in the realm of social media data analysis. The exploration of hope content helps to uncover the valuable insights into users’ aspirations, expectations, and emotional states. By delving into the analysis of hope content on social media platforms, researchers and analysts can gain a deeper understanding of how hope influences individuals’ behaviors, decisions, and overall well-being in the digital age. However, this area is rarely explored even for resource-high languages. To address the identification of hope text in social media platforms, this paper describes the models submitted by the team MUCS to “Hope Speech Detection for Equality, Diversity, and Inclusion (LT-EDI)” shared task organized at Recent Advances in Natural Language Processing (RANLP) - 2023. This shared task aims to classify a comment/post in English and code-mixed texts in three languages, namely, Bulgarian, Spanish, and Hindi into one of the two predefined categories, namely, “Hope speech” and “Non Hope speech”. Two models, namely: i) Hope_BERT - Linear Support Vector Classifier (LinearSVC) model trained by combining Bidirectional Encoder Representations from Transformers (BERT) embeddings and Term Frequency-Inverse Document Frequency (TF-IDF) of character n-grams with word boundary (char_wb) for English and ii) Hope_mBERT - LinearSVC model trained by combining Multilingual BERT (mBERT) embeddings and TF-IDF of char_wb for Bulgarian, Spanish, and Hindi code-mixed texts are proposed for the shared task to classify the given text into Hope or Non-Hope categories. The proposed models obtained 1st, 1st, 2nd, and 5th ranks for Spanish, Bulgarian, Hindi, and English texts respectively.

Homophobic/Transphobic (H/T) content includes hate speech, discrimination text, and abusive comments against Gay, Lesbian, Bisexual, Transgender, Queer, and Intersex (LGBTQ) individuals. With the increase in user generated text in social media, there has been an increase in code-mixed H/T content, which poses challenges for efficient analysis and detection of H/T content on social media. The complex nature of code-mixed text necessitates the development of advanced tools and techniques to effectively tackle this issue in social media platforms. To tackle this issue, in this paper, we - team MUCS, describe the transformer based models submitted to “Homophobia/Transphobia Detection in social media comments” shared task in Language Technology for Equality, Diversity and Inclusion (LT-EDI) at Recent Advances in Natural Language Processing (RANLP)-2023. The proposed methodology makes use of resampling the training data to handle the data imbalance and this resampled data is used to fine-tune the Multilingual Bidirectional Encoder Representations from Transformers (mBERT) models. These models obtained 11th, 5th, 3rd, 3rd, and 7th ranks for English, Tamil, Malayalam, Spanish, and Hindi respectively in Task A and 8th, 2nd, and 2nd ranks for English, Tamil, and Malayalam respectively in Task B.

Depression can lead to significant changes in individuals’ posts on social media which is a important task to identify. Automated techniques must be created for the identification task as manually analyzing the growing volume of social media data is time-consuming. To address the signs of depression posts on social media, in this paper, we - team MUCS, describe a Transfer Learning (TL) model and Machine Learning (ML) models submitted to “Detecting Signs of Depression from Social Media Text” shared task organised by DepSign-LT-EDI@RANLP-2023. The TL model is trained using raw text Bidirectional Encoder Representations from Transformers (BERT) and the ML model is trained using Term Frequency-Inverse Document Frequency (TF-IDF) features separately. Among these three models, the TL model performed better with a macro averaged F1-score of 0.361 and placed 20th rank in the shared task.

Sentiment Analysis (SA) employing code-mixed data from social media helps in getting insights to the data and decision making for various applications. One such application is to analyze users’ emotions from comments of videos on YouTube. Social media comments do not adhere to the grammatical norms of any language and they often comprise a mix of languages and scripts. The lack of annotated code-mixed data for SA in a low-resource language like Tulu makes the SA a challenging task. To address the lack of annotated code-mixed Tulu data for SA, a gold standard trlingual code-mixed Tulu annotated corpus of 7,171 YouTube comments is created. Further, Machine Learning (ML) algorithms are employed as baseline models to evaluate the developed dataset and the performance of the ML algorithms are found to be encouraging.

Code-mixing is the phenomena of mixing various linguistic units such as paragraphs, sentences, phrases, words, etc., of one language with that of the other language in any text. This code-mixing is predominantly used by social media users who know more than one language. Processing code-mixed text is challenging because of its characteristics and lack of tools that supports such data. Further, pretrained models can be used for the formal text and not for the informal text such as code-mixed. Developing efficient Machine Translation (MT) systems for code-mixed text is challenging due to lack of code-mixed training data. Further, existing MT systems developed to translate monolingual data are not portable to translate code-mixed text mainly due to its informal nature. To address the MT challenges of code-mixed text, this paper describes the proposed MT models submitted by our team MUCS, to the Code-mixed Machine Translation (MixMT) shared task in the Workshop on Machine Translation (WMT) organized in connection with Empirical models in Natural Language Processing (EMNLP) 2022. This shared has two subtasks: i) subtask 1 - to translate English sentences and their corresponding Hindi translations into Hinglish text and ii) subtask 2 - to translate Hinglish text into English text. The proposed models that translate the code-mixed English text to Hinglish (English-Hindli code-mixed text) and vice-versa, comprises of i) transliterating Hinglish text from Latin to Devanagari script and vice-versa, ii) pseudo translation generation using existing models, and iii) efficient target generation by combining the pseudo translations along with the training data provided by the shared task organizers. The proposed models obtained 5^th and 3^rd rank with Recall-Oriented Under-study for Gisting Evaluation (ROUGE) scores of 0.35806 and 0.55453 for subtask 1 and subtask 2 respectively.

Emotion Analysis (EA) is the process of automatically analyzing and categorizing the input text into one of the predefined sets of emotions. In recent years, people have turned to social media to express their emotions, opinions or feelings about news, movies, products, services, and so on. These users’ emotions may help the public, governments, business organizations, film producers, and others in devising strategies, making decisions, and so on. The increasing number of social media users and the increasing amount of user generated text containing emotions on social media demands automated tools for the analysis of such data as handling this data manually is labor intensive and error prone. Further, the characteristics of social media data makes the EA challenging. Most of the EA research works have focused on English language leaving several Indian languages including Tamil unexplored for this task. To address the challenges of EA in Tamil texts, in this paper, we - team MUCS, describe the model submitted to the shared task on Emotion Analysis in Tamil at DravidianLangTech@ACL 2022. Out of the two subtasks in this shared task, our team submitted the model only for Task a. The proposed model comprises of an Ensemble of Logistic Regression (LR) classifiers with three penalties, namely: L1, L2, and Elasticnet. This Ensemble model trained with Term Frequency - Inverse Document Frequency (TF-IDF) of character bigrams and trigrams secured 4th rank in Task a with a macro averaged F1-score of 0.04. The code to reproduce the proposed models is available in github1.

This paper presents an outline of the shared task on translation of under-resourced Dravidian languages at DravidianLangTech-2022 workshop to be held jointly with ACL 2022. A description of the datasets used, approach taken for analysis of submissions and the results have been illustrated in this paper. Five sub-tasks organized as a part of the shared task include the following translation pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Sanskrit, Kannada to Malayalam and Kannada to Tulu. Training, development and test datasets were provided to all participants and results were evaluated on the gold standard datasets. A total of 16 research groups participated in the shared task and a total of 12 submission runs were made for evaluation. Bilingual Evaluation Understudy (BLEU) score was used for evaluation of the translations.

Social media has seen enormous growth in its users recently and knowingly or unknowingly the behavior of a person will be reflected in the comments she/he posts on social media. Users having the sign of depression may post negative or disturbing content seeking the attention of other users. Hence, social media data can be analysed to check whether the users’ have the sign of depression and help them to get through the situation if required. However, as analyzing the increasing amount of social media data manually in laborious and error-prone, automated tools have to be developed for the same. To address the issue of detecting the sign of depression content on social media, in this paper, we - team MUCS, describe an Ensemble of Machine Learning (ML) models and a Transfer Learning (TL) model submitted to “Detecting Signs of Depression from Social Media Text-LT-EDI@ACL 2022” (DepSign-LT-EDI@ACL-2022) shared task at Association for Computational Linguistics (ACL) 2022. Both frequency and text based features are used to train an Ensemble model and Bidirectional Encoder Representations from Transformers (BERT) fine-tuned with raw text is used to train the TL model. Among the two models, the TL model performed better with a macro averaged F-score of 0.479 and placed 18th rank in the shared task. The code to reproduce the proposed models is available in github page1.

Due to the rapid rise of social networks and micro-blogging websites, communication between people from different religion, caste, creed, cultural and psychological backgrounds has become more direct leading to the increase in cyber conflicts between people. This in turn has given rise to more and more hate speech and usage of abusive words to the point that it has become a serious problem creating negative impacts on the society. As a result, it is imperative to identify and filter such content on social media to prevent its further spread and the damage it is going to cause. Further, filtering such huge data requires automated tools since doing it manually is labor intensive and error prone. Added to this is the complex code-mixed and multi-scripted nature of social media text. To address the challenges of abusive content detection on social media, in this paper, we, team MUM, propose Machine Learning (ML) and Deep Learning (DL) models submitted to Multilingual Gender Biased and Communal Language Identification (ComMA@ICON) shared task at International Conference on Natural Language Processing (ICON) 2021. Word uni-grams, char n-grams, and emoji vectors are combined as features to train a ML Elastic-net regression model and multi-lingual Bidirectional Encoder Representations from Transformers (mBERT) is fine-tuned for a DL model. Out of the two, fine-tuned mBERT model performed better with an instance-F1 score of 0.326, 0.390, 0.343, 0.359 for Meitei, Bangla, Hindi, Multilingual texts respectively.

Dravidian language family is one of the largest language families in the world. In spite of its uniqueness, Dravidian languages have gained very less attention due to scarcity of resources to conduct language technology tasks such as translation, Parts-of-Speech tagging, Word Sense Disambiguation etc,. In this paper, we, team MUCS, describe sequence-to-sequence stacked Long Short Term Memory (LSTM) based Neural Machine Translation (NMT) model submitted to “Machine Translation in Dravidian languages”, a shared task organized by EACL-2021. The NMT model was applied on translation using English-Tamil, EnglishTelugu, English-Malayalam and Tamil-Telugu corpora provided by the organizers. Standard evaluation metrics namely Bilingual Evaluation Understudy (BLEU) and human evaluations are used to evaluate the model. Our models exhibited good accuracy for all the language pairs and obtained 2nd rank for TamilTelugu language pair.

Machine Translation (MT) is the task of automatically converting the text in source language to text in target language by preserving the meaning. MT usually require large corpus for training the translation models. Due to scarcity of resources very less attention is given to translating into low resource languages and in particular into Indic languages. In this direction, a shared task called “Adap-MT 2020: Low Resource Domain Adaptation for Indic Machine Translation” is organized to illustrate the capability of general domain MT when translating into Indic languages and low resource domain adaptation of MT systems. In this paper, we, team MUCS, describe a simple word extraction based domain adaptation approach applied to English-Hindi MT only. MT in the proposed model is carried out using Open-NMT - a popular Neural Machine Translation tool. A general domain corpus is built effectively combining the available English-Hindi corpora and removing the duplicate sentences. Further, domain specific corpus is updated by extracting the sentences from generic corpus that contains the words given in the domain specific corpus. The proposed model exhibited satisfactory results for small domain specific AI and CHE corpora provided by the organizers in terms of BLEU score with 1.25 and 2.72 respectively. Further, this methodology is quite generic and can easily be extended to other low resource language pairs as well.