Selam Kanta


2023

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Selam@DravidianLangTech:Sentiment Analysis of Code-Mixed Dravidian Texts using SVM Classification
Selam Kanta | Grigori Sidorov
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages

Sentiment analysis in code-mixed text written in Dravidian languages. Specifically, Tamil- English and Tulu-English. This paper describes the system paper of the RANLP-2023 shared task. The goal of this shared task is to develop systems that accurately classify the sentiment polarity of code-mixed comments and posts. be provided with development, training, and test data sets containing code-mixed text in Tamil- English and Tulu-English. The task involves message-level polarity classification, to classify YouTube comments into positive, negative, neutral, or mixed emotions. This Code- Mix was compiled by RANLP-2023 organizers from posts on social media. We use classification techniques SVM and achieve an F1 score of 0.147 for Tamil-English and 0.518 for Tulu- English.

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Habesha@DravidianLangTech: Abusive Comment Detection using Deep Learning Approach
Mesay Gemeda Yigezu | Selam Kanta | Olga Kolesnikova | Grigori Sidorov | Alexander Gelbukh
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages

This research focuses on identifying abusive language in comments. The study utilizes deep learning models, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs), to analyze linguistic patterns. Specifically, the LSTM model, a type of RNN, is used to understand the context by capturing long-term dependencies and intricate patterns in the input sequences. The LSTM model achieves better accuracy and is enhanced through the addition of a dropout layer and early stopping. For detecting abusive language in Telugu and Tamil-English, an LSTM model is employed, while in Tamil abusive language detection, a word-level RNN is developed to identify abusive words. These models process text sequentially, considering overall content and capturing contextual dependencies.