With the prevalence of code-mixing among speakers of Dravidian languages, DravidianLangTech proposed the shared task on Sentiment Analysis in Tamil and Tulu at RANLP 2023. This paper presents the submission of LIDOMA, which proposes a methodology that combines lexical features and Convolutional Neural Networks (CNNs) to address the challenge. A fine-tuned 6-layered CNN model is employed, achieving macro F1 scores of 0.542 and 0.199 for Tulu and Tamil, respectively

Hate speech detection during times of war has become crucial in recent years, as evident with the recent Russo-Ukrainian war. In this paper, we present our submissions for both subtasks from the Multimodal Hate Speech Event Detec- tion contest at CASE 2023, RANLP 2023. We used pre-trained BERT models in both submis- sion, achieving a F1 score of 0.809 in subtask A, and F1 score of 0.567 in subtask B. In the first subtask, our result was not far from the first place, which led us to realize the lower impact of images in real-life memes about feel- ings, when compared with the impact of text. However, we observed a higher importance of images when targeting hateful feelings towards a specific entity. The source code to reproduce our results can be found at the github repository https://github.com/JesusASmx/OmeteotlAtCASE2023