Depression is a severe mental health disorder characterized by persistent feelings of sadness and anxiety, a decline in cognitive functioning resulting in drastic changes in a human’s psychological and physical well-being. However, depression is curable completely when treated at a suitable time and treatment resulting in the rejuvenation of an individual. The objective of this paper is to devise a technique for detecting signs of depression from English social media comments as well as classifying them based on their intensity into severe, moderate, and not depressed categories. The paper illustrates three approaches that are developed when working toward the problem. Of these approaches, the BERT model proved to be the most suitable model with an F1 macro score of 0.407, which gave us the 11th rank overall.

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.

As world population increases, more people are living to the age when depression or Major Depressive Disorder (MDD) commonly occurs. Consequently, the number of those who suffer from such disorders is rising. There is a pressing need for faster and reliable diagnosis methods. This paper proposes the method to analyse text input from social media posts of subjects to determine the severity class of depression. We have used the DistilBERT transformer to process these texts and classify the individuals across three severity labels - ‘not depression’, ‘moderate’ and ‘severe’. The results showed the macro F1-score of 0.437 when the model was trained for 5 epochs with a comparative performance across the labels.The team acquired 6th rank while the top team scored macro F1-score as 0.470. We hope that this system will support further research into the early identification of depression in individuals to promote effective medical research and related treatments.

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.

The amount of digital material that is disseminated through various social media platforms has significantly increased in recent years. Online networks have gained popularity in recent years and have established themselves as goto resources for news, information, and entertainment. Nevertheless, despite the many advantages of using online networks, mounting evidence indicates that an increasing number of malicious actors are taking advantage of these networks to spread poison and hurt other people. This work aims to detect abusive content in youtube comments written in the languages like Tamil, Tamil-English (codemixed), Telugu-English (code-mixed). This work was undertaken as part of the “DravidianLangTech@ RANLP 2023” shared task. The Macro F1 values for the Tamil, Tamil-English, and Telugu-English datasets were 0.28, 0.37, and 0.6137 and secured 5th, 7th, 8th rank respectively.

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.

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.

This paper describes the system used for detecting humor in text. The system developed by the team TECHSSN uses binary classification techniques to classify the text. The data undergoes preprocessing and is given to ColBERT (Contextualized Late Interaction over BERT), a modification of Bidirectional Encoder Representations from Transformers (BERT). The model is re-trained and the weights are learned for the dataset. This system was developed for the task 7 of the competition, SemEval 2021.

The system developed by the SSN MLRG1 team for Semeval-2018 task 1 on affect in tweets uses rule based feature selection and one-hot encoding to generate the input feature vector. Multilayer Perceptron was used to build the model for emotion intensity ordinal classification, sentiment analysis ordinal classification and emotion classfication subtasks. Support Vector Machine was used to build the model for emotion intensity regression and sentiment intensity regression subtasks.

Sentiment analysis plays an important role in E-commerce. Identifying ironic and sarcastic content in text plays a vital role in inferring the actual intention of the user, and is necessary to increase the accuracy of sentiment analysis. This paper describes the work on identifying the irony level in twitter texts. The system developed by the SSN MLRG1 team in SemEval-2018 for task 3 (irony detection) uses rule based approach for feature selection and MultiLayer Perceptron (MLP) technique to build the model for multiclass irony classification subtask, which classifies the given text into one of the four class labels.

The SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with bag of words feature vectors and fixed rule multi-kernel learning, for sentiment analysis of tweets. Since tweets on the same topic, made at different times, may exhibit different emotions, their properties such as smoothness and periodicity also vary with time. Our experiments show that, compared to single kernel, multiple kernels are effective in learning the simultaneous presence of multiple properties.

The system developed by the SSN_MLRG1 team for Semeval-2017 task 5 on fine-grained sentiment analysis uses Multiple Kernel Gaussian Process for identifying the optimistic and pessimistic sentiments associated with companies and stocks. Since the comments made at different times about the same companies and stocks may display different emotions, their properties such as smoothness and periodicity may vary. Our experiments show that while single kernel Gaussian Process can learn certain properties well, Multiple Kernel Gaussian Process are effective in learning the presence of different properties simultaneously.