This paper presents the overview of the shared task on emotional analysis in Tamil. The result of the shared task is presented at the workshop. This paper presents the dataset used in the shared task, task description, and the methodology used by the participants and the evaluation results of the submission. This task is organized as two Tasks. Task A is carried with 11 emotions annotated data for social media comments in Tamil and Task B is organized with 31 fine-grained emotion annotated data for social media comments in Tamil. For conducting experiments, training and development datasets were provided to the participants and results are evaluated for the unseen data. Totally we have received around 24 submissions from 13 teams. For evaluating the models, Precision, Recall, micro average metrics are used.

/pdf/findings-of-the-shared-task-on-emotion-analysis-in-tamil-2vbzofvm.pdf

Findings of the Shared Task on Emotion Analysis in Tamil

This paper elaborates on our work in designing a system for SemEval 2023 Task 12: AfriSentiSemEval, which involves sentiment analysis for low-resource African languages using the Twitter dataset. We utilised a pre-trained model to perform sentiment classification in Hausa language tweets. We used a multilingual version of the roBERTa model, which is pretrained on 100 languages, to classify sentiments in Hausa. To tokenize the text, we used the AfriBERTa model, which is specifically pretrained on African languages.

TechSSN at SemEval-2023 Task 12: Monolingual Sentiment Classification in Hausa Tweets

This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.

Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages

Sentiment Analysis is the task of identifying the emotions underlying the subjective opinions or emotional responses pertaining to a given topic, be it positive, negative or neutral. Sentiment Analysis is done with the use of natural language processing. Homophobia speech is a type of hate speech directed towards LGBT+ people. This research work presents Sentiment Analysis and Homophobia detection in Youtube comments in Code-Mixed Dravidian Languages with different embeddings using machine learning algorithms. The goal of Task- A is to identify sentiment polarity of the code-mixed dataset of comments, posts in Tamil-English, Malayalam-English, and Kannada-English collected from social media. The goal of Task-B is to identify if the comment is homophobic/transphobic in nature. Our team srmnlp worked with code-mixed form of Tamil, Malayalam and Kannada text provided by the FIRE 2022 organizers. Pre-trained models such as bert, xlm, MPNet were used along with classifiers such as SVM,MLP, Random Forest under the feature extraction techniques like, Count Vectorizer, and TF-IDF. The rankings for sentiment analysis task are, rank 1 in Tamil dataset, rank 6 in Malayalam dataset, rank 7 in Kannada dataset. The highest F1-score of 0.63 was obtained for sentiment analysis in Malayalam dataset, similarly 0.95 was obtained for homophibia detection task in Malayalam dataset. The performance of the proposed system is compared with various machine learning algorithms.

Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages using machine learning and transformer models

Emotion Recognition in Conversation (ERC) in the context of code-mixed Hindi-English interactions is a subtask addressed in SemEval-2024 as Task 10. We made our maiden attempt to solve the problem using natural language processing, machine learning and deep learning techniques, that perform well in properly assigning emotions to individual utterances from a predefined collection. The use of well-proven classifier such as Long Short Term Memory networks improve the model’s efficacy than the BERT and Glove based models. How-ever, difficulties develop in the subtle arena of emotion-flip reasoning in multi-party discussions, emphasizing the importance of specialized methodologies. Our findings shed light on the intricacies of emotion dynamics in code-mixed languages, pointing to potential areas for further research and refinement in multilingual understanding.

pdf/techssn-at-semeval-2024-task-10-lstm-based-approach-for-3drt24evxi.pdf

TECHSSN at SemEval-2024 Task 10: LSTM-based Approach for Emotion Detection in Multilingual Code-Mixed Conversations

In this paper, we present our system for the task of Emotion analysis in Tamil. Over 3.96 million people use these platforms to send messages formed using texts, images, videos, audio or combinations of these to express their thoughts and feelings. Text communication on social media platforms is quite overwhelming due to its enormous quantity and simplicity. The data must be processed to understand the general feeling felt by the author. We present a lexicon-based approach for the extraction emotion in Tamil texts. We use dictionaries of words labelled with their respective emotions. The process of assigning an emotional label to each text, and then capture the main emotion expressed in it. Finally, the F1-score in the official test set is 0.0300 and our method ranks 5th.

Varsini_and_Kirthanna@DravidianLangTech-ACL2022-Emotional Analysis in Tamil

The ACL shared task of DravidianLangTech-2022 for Troll Meme classification is a binary classification task that involves identifying Tamil memes as troll or not-troll. Classification of memes is a challenging task since memes express humour and sarcasm in an implicit way. Team SSN_MLRG1 tested and compared results obtained by using three models namely BERT, ALBERT and XLNET. The XLNet model outperformed the other two models in terms of various performance metrics. The proposed XLNet model obtained the 3rd rank in the shared task with a weighted F1-score of 0.558.

SSN_MLRG1@DravidianLangTech-ACL2022: Troll Meme Classification in Tamil using Transformer Models

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Varsini_and_Kirthanna@DravidianLangTech-ACL2022-Emotional Analysis in Tamil

SSN_MLRG1@DravidianLangTech-ACL2022: Troll Meme Classification in Tamil using Transformer Models