Our paper explores a task involving the analysis of emotions and triggers within dialogues. We annotate each utterance with an emotion and identify triggers, focusing on binary labeling. We emphasize clear guidelines for replicability and conduct thorough analyses, including multiple system runs and experiments to highlight effective techniques. By simplifying the complexities and detailing clear methodologies, our study contributes to advancing emotion analysis and trigger identification within dialogue systems.

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.

In recent years, the growth of online platforms and social media has given rise to a concerning increase in the presence of abusive content. This poses significant challenges for maintaining a safe and inclusive digital environment. In order to resolve this issue, this paper experiments an approach for detecting abusive comments. We are using a combination of pipelining and vectorization techniques, along with algorithms such as the stochastic gradient descent (SGD) classifier and support vector machine (SVM) classifier. We conducted experiments on an Tamil-English code mixed dataset to evaluate the performance of this approach. Using the stochastic gradient descent classifier algorithm, we achieved a weighted F1 score of 0.76 and a macro score of 0.45 for development dataset. Furthermore, by using the support vector machine classifier algorithm, we obtained a weighted F1 score of 0.78 and a macro score of 0.42 for development dataset. With the test dataset, SGD approach secured 5th rank with 0.44 macro F1 score, while SVM scored 8th rank with 0.35 macro F1 score in the shared task. The top rank team secured 0.55 macro F1 score.

The abusive content on social media networks is causing destructive effects on the mental well-being of online users. Homophobia refers to the fear, negative attitudes and feeling towards homosexuality. Transphobia refer to negative attitudes, hatred and prejudice towards transsexual people. Even though, some parts of the society have started to accept homosexuality and transsexuality, there are still a large set of the population opposing it. Hate speech targeting LGBTQ+ individuals, known as homophobia/transphobia speech, has become a growing concern. This has led to a toxic and unwelcoming environment for LGBTQ+ people on online platforms. This poses a significant societal issue, hindering the progress of equality, diversity, and inclusion. The identification of homophobic and transphobic comments on social media platforms plays a crucial role in creating a safer environment for all social media users. In order to accomplish this, we built a machine learning model using SGD and SVM classifier. Our approach yielded promising results, with a weighted F1-score of 0.95 on the English dataset and we secured 4th rank in this task.

Social media platforms have revolutionized the landscape of communication, providing individuals with an outlet to express their thoughts, emotions, and experiences openly. This paper focuses on the development of a model to determine whether individuals exhibit signs of depression based on their social media texts. With the aim of optimizing performance and accuracy, a Naive Bayes approach was chosen for the detection task.The Naive Bayes algorithm, a probabilistic classifier, was applied to extract features and classify the texts. The model leveraged linguistic patterns, sentiment analysis, and other relevant features to capture indicators of depression within the texts. Preprocessing techniques, including tokenization, stemming, and stop-word removal, were employed to enhance the quality of the input data.The performance of the Naive Bayes model was evaluated using standard metrics such as accuracy, precision, recall, and F1-score, it acheived a macro- avergaed F1 score of 0.263.