Angel S


2022

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SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model
Praveenkumar Vijayakumar | Prathyush S | Aravind P | Angel S | Rajalakshmi Sivanaiah | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a method to find and support helpful social media content. In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence. The task was to classify the sentences into ‘Hope speech’ and ‘Non-hope speech’. The dataset was provided by LT-EDI organizers with text from Youtube comments. Based on the task description, we developed a system using the pre-trained language model BERT to complete this task. Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.

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SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models
Sarika Esackimuthu | Shruthi Hariprasad | Rajalakshmi Sivanaiah | Angel S | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.

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SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text
Karun Anantharaman | Angel S | Rajalakshmi Sivanaiah | Saritha Madhavan | Sakaya Milton Rajendram
Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion

DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the system should classify the signs of depres-sion into three labels namely “not depressed”,“moderately depressed”, and “severely de-pressed”. To achieve this objective, we haveadopted a fine-tuned BERT model. This solu-tion from team SSN_MLRG1 achieves 58.5%accuracy on the DepSign-LT-EDI@ACL-2022test set.

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SSN_MLRG1@DravidianLangTech-ACL2022: Troll Meme Classification in Tamil using Transformer Models
Shruthi Hariprasad | Sarika Esackimuthu | Saritha Madhavan | Rajalakshmi Sivanaiah | Angel S
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

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.

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Varsini_and_Kirthanna@DravidianLangTech-ACL2022-Emotional Analysis in Tamil
Varsini S | Kirthanna Rajan | Angel S | Rajalakshmi Sivanaiah | Sakaya Milton Rajendram | Mirnalinee T T
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

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.