Social media has become a vital platform for personal communication. Its widespread use as a primary means of public communication offers an exciting opportunity for early detection and management of mental health issues. People often share their emotions on social media, but understanding the true depth of their feelings can be challenging. Depression, a prevalent problem among young people, is of particular concern due to its link with rising suicide rates. Identifying depression levels in social media texts is crucial for timely support and prevention of negative outcomes. However, it’s a complex task because human emotions are dynamic and can change significantly over time. The DepSign-LT-EDI@RANLP 2023 shared task aims to classify social media text into three depression levels: “Not Depressed,” “Moderately Depressed,” and “Severely Depressed.” This overview covers task details, dataset, methodologies used, and results analysis. Roberta-based models emerged as top performers, with the best result achieving an impressive macro F1-score of 0.584 among 31 participating teams.

Depression is a mental fitness disorder from persistent reactions of unhappiness, void, and a deficit of interest in activities. It can influence differing facets of one’s life, containing their hopes, sympathy, and nature. Depression can stem from a sort of determinant, in the way that ancestral willingness, life occurrences, and social circumstances. In current years, the influence of social media on mental fitness has become an increasing concern. Excessive use of social media and the negative facets that guide it, can exacerbate or cause impressions of distress. The nonstop exposure to cautiously curated lives, social comparison, cyberbullying, and the pressure to meet unreal standards can impact an individual’s pride, social connections, and overall well-being. We participated in the shared task at DepSignLT-EDI@RANLP 2023 and have proposed a model that identifies the levels of depression from social media text using the data set shared for the task. Different transformer models like ALBERT and RoBERTa are used by the proposed model for implementing the task. The macro F1 score obtained by ALBERT model and RoBERTa model are 0.258 and 0.143 respectively.

Hope is a cheerful and optimistic state of mind which has its basis in the expectation of positive outcomes. Hope speech reflects the same as they are positive words that can motivate and encourage a person to do better. Non-hope speech reflects the exact opposite. They are meant to ridicule or put down someone and affect the person negatively. The shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI - RANLP 2023 was created with data sets in English, Spanish, Bulgarian and Hindi. The purpose of this task is to classify human-generated comments on the platform, YouTube, as Hope speech or non-Hope speech. We employed multiple traditional models such as SVM (support vector machine), Random Forest classifier, Naive Bayes and Logistic Regression. Support Vector Machine gave the highest macro average F1 score of 0.49 for the training data set and a macro average F1 score of 0.50 for the test data set.

The advent of social media platforms has revo- lutionized the way we interact, share, learn , ex- press and build our views and ideas. One major challenge of social media is hate speech. Homo- phobia and transphobia encompasses a range of negative attitudes and feelings towards people based on their sexual orientation or gender iden- tity. Homophobia refers to the fear, hatred, or prejudice against homosexuality, while trans- phobia involves discrimination against trans- gender individuals. Natural Language Process- ing can be used to identify homophobic and transphobic texts and help make social media a safer place. In this paper, we explore us- ing Support Vector Machine , Random Forest Classifier and Bert Model for homophobia and transphobia detection. The best model was a combination of LaBSE and SVM that achieved a weighted F1 score of 0.95.

In recent years, there has been a growing focus on Sentiment Analysis (SA) of code-mixed Dravidian languages. However, the majority of social media text in these languages is code-mixed, presenting a unique challenge. Despite this, there is currently lack of research on SA specifically tailored for code-mixed Dravidian languages, highlighting the need for further exploration and development in this domain. In this view, “Sentiment Analysis in Tamil and Tulu- DravidianLangTech” shared task at Recent Advances in Natural Language Processing (RANLP)- 2023 is organized. This shred consists two language tracks: code-mixed Tamil and Tulu and Tulu text is first ever explored in public domain for SA. We describe the task, its organization, and the submitted systems followed by the results. 57 research teams registered for the shared task and We received 27 systems each for code-mixed Tamil and Tulu texts. The performance of the systems (developed by participants) has been evaluated in terms of macro average F1 score. The top system for code-mixed Tamil and Tulu texts scored macro average F1 score of 0.32, and 0.542 respectively. The high quality and substantial quantity of submissions demonstrate a significant interest and attention in the analysis of code-mixed Dravidian languages. However, the current state of the art in this domain indicates the need for further advancements and improvements to effectively address the challenges posed by code-mixed Dravidian language SA.

In this research paper, we propose a multimodal approach to hate speech detection, directed towards the identification of hate speech and its related targets. Our method uses logistic regression and support vector machines (SVMs) to analyse textual content extracted from social media platforms. We exploit natural language processing techniques to preprocess and extract relevant features from textual content, capturing linguistic patterns, sentiment, and contextual information.

In recent times, there exists an abundance of research to classify abusive and offensive texts focusing on negative comments but only minimal research using the positive reinforcement approach. The task was aimed at classifying texts into ‘Hope_speech’, ‘Non_hope_speech’, and ‘Not in language’. The datasets were provided by the LT-EDI organisers in English, Tamil, and Malayalam language with texts sourced from YouTube comments. We trained our data using transformer models, specifically mBERT for Tamil and Malayalam and BERT for English, and achieved weighted average F1-scores of 0.46, 0.81, 0.92 for Tamil, Malayalam, and English respectively.

Sarcasm analysis in user conversion text is automatic detection of any irony, insult, hurting, painful, caustic, humour, vulgarity that degrades an individual. It is helpful in the field of sentimental analysis and cyberbullying. As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone. In this paper, we present traditional machine learning approaches, deep learning approach (LSTM -RNN) and BERT (Bidirectional Encoder Representations from Transformers) for identifying sarcasm. We have used the approaches to build the model, to identify and categorize how much conversion context or response is needed for sarcasm detection and evaluated on the two social media forums that is twitter conversation dataset and reddit conversion dataset. We compare the performance based on the approaches and obtained the best F1 scores as 0.722, 0.679 for the twitter forums and reddit forums respectively.

Common sense validation deals with testing whether a system can differentiate natural language statements that make sense from those that do not make sense. This paper describes the our approach to solve this challenge. For common sense validation with multi choice, we propose a stacking based approach to classify sentences that are more favourable in terms of common sense to the particular statement. We have used majority voting classifier methodology amongst three models such as Bidirectional Encoder Representations from Transformers (BERT), Micro Text Classification (Micro TC) and XLNet. For sentence generation, we used Neural Machine Translation (NMT) model to generate explanatory sentences.

Assessing the funniness of edited news headlines task deals with estimating the humorness in the headlines edited with micro-edits. This task has two sub-tasks in which one has to calculate the mean predicted score of humor level and other deals with predicting the best funnier sentence among given two sentences. We have calculated the humorness level using microtc and predicted the funnier sentence using microtc, universal sentence encoder classifier, many other traditional classifiers that use the vectors formed with universal sentence encoder embeddings, sentence embeddings and majority algorithm within these approaches. Among these approaches, microtc with 6 folds, 24 processes and 3 folds, 36 processes achieve the least Root Mean Square Error for development and test set respectively for subtask 1. For subtask 2, Universal sentence encoder classifier achieves the highest accuracy for development set and Multi-Layer Perceptron applied on vectors vectorized using universal sentence encoder embeddings for the test set.

Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that is addressed towards an individual or a group. Due to immense growth and usage of social media, it has an extensive reach and impact on the society. OLI is helpful for hate speech detection, flame detection and cyber bullying, hence it is used to avoid abuse and hurts. In this paper, we present state of the art machine learning approaches for OLI. We follow several approaches which include classifiers like Naive Bayes, Support Vector Machine(SVM) and deep learning approaches like Recurrent Neural Network(RNN) and Masked LM (MLM). The approaches are evaluated on the OffensEval@SemEval2020 dataset and our team ssn_nlp submitted runs for the third task of OffensEval shared task. The best run of ssn_nlp that uses BERT (Bidirectional Encoder Representations from Transformers) for the purpose of training the OLI model obtained F1 score as 0.61. The model performs with an accuracy of 0.80 and an evaluation loss of 1.0828. The model has a precision rate of 0.72 and a recall rate of 0.58.

Offensive language identification is to detect the hurtful tweets, derogatory comments, swear words on social media. As an emerging growth of social media communication, offensive language detection has received more attention in the last years; we focus to perform the task on English, Danish and Greek. We have investigated which can be effect more on pre-trained models BERT (Bidirectional Encoder Representation from Transformer) and Machine Learning Approaches. Our investigation shows the difference performance between the three languages and to identify the best performance is evaluated by the classification algorithms. In the shared task SemEval-2020, our team SSN_NLP_MLRG submitted for three languages that are Subtasks A, B, C in English, Subtask A in Danish and Subtask A in Greek. Our team SSN_NLP_MLRG obtained the F1 Scores as 0.90, 0.61, 0.52 for the Subtasks A, B, C in English, 0.56 for the Subtask A in Danish and 0.67 for the Subtask A in Greek respectively.

Emotion identification is a process of identifying the emotions automatically from text, speech or images. Emotion identification from textual conversations is a challenging problem due to absence of gestures, vocal intonation and facial expressions. It enables conversational agents, chat bots and messengers to detect and report the emotions to the user instantly for a healthy conversation by avoiding emotional cues and miscommunications. We have adopted a Seq2Seq deep neural network to identify the emotions present in the text sequences. Several layers namely embedding layer, encoding-decoding layer, softmax layer and a loss layer are used to map the sequences from textual conversations to the emotions namely Angry, Happy, Sad and Others. We have evaluated our approach on the EmoContext@SemEval2019 dataset and we have obtained the micro-averaged F1 scores as 0.595 and 0.6568 for the pre-evaluation dataset and final evaluation test set respectively. Our approach improved the base line score by 7% for final evaluation test set.

Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that degrades an individual or a group. It is helpful for hate speech detection, flame detection and cyber bullying. Due to immense growth of accessibility to social media, OLI helps to avoid abuse and hurts. In this paper, we present deep and traditional machine learning approaches for OLI. In deep learning approach, we have used bi-directional LSTM with different attention mechanisms to build the models and in traditional machine learning, TF-IDF weighting schemes with classifiers namely Multinomial Naive Bayes and Support Vector Machines with Stochastic Gradient Descent optimizer are used for model building. The approaches are evaluated on the OffensEval@SemEval2019 dataset and our team SSN_NLP submitted runs for three tasks of OffensEval shared task. The best runs of SSN_NLP obtained the F1 scores as 0.53, 0.48, 0.3 and the accuracies as 0.63, 0.84 and 0.42 for the tasks A, B and C respectively. Our approaches improved the base line F1 scores by 12%, 26% and 14% for Task A, B and C respectively.