SSN _ ARMM @ LT - EDI - ACL 2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model (2022)

Abstract: In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a method to find and support helpful social media content. In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence. The task was to classify the sentences into ‘Hope speech’ and ‘Non-hope speech’. The dataset was provided by LT-EDI organizers with text from Youtube comments. Based on the task description, we developed a system using the pre-trained language model BERT to complete this task. Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.

## introduction social media has become an essential part of our lives. people tend to reflect on their inner selves through their online conversations. there is a huge increase in the number of individuals looking for support through the internet. in recent times, there has been a surge in these online support sources. @xcite online support groups help people going through similar disabilities, health problems, etc and overcome their difficulties together. recently researchers @xcite have found out that social media network and online support groups have a great impact on people's self-understanding. youtube is a social media platform which connects billions of users across the internet. it has gained outstanding popularity across the globe @xcite @xcite @xcite . with commenting options available, one can easily manipulate different people through this. as social media is used predominantly in day to day life, it is crucial, not only to protect users from harmful content but also to spread and encourage hope and optimism in this society @xcite @xcite @xcite . in recent days, nlp has gained many architectural advancements and gained better results than state of art methods @xcite . the task focuses on the classification of hope speech in multiple languages with each language having different class imbalances. hope speech detection can uplift the amount of positive content on social media and helps to build a peaceful world. in our task, we used hope speech dataset for equality, diversity, and inclusion in english, tamil, malayalam, and kannada @xcite @xcite . dravidian languages are a group of 250 million people who speak mostly in southern india, north-east sri lanka, and southwest pakistan. the dravidian languages are classified as south, south-central, central, and north, with each category subdivided into 24 subgroups. the indian constitution recognizes four main literary languages: telugu, tamil, malayalam, and kannada. tamil is one of the world's longestsurviving classical languages @xcite @xcite . tamil is a member of the southern branch of the dravidian languages, a group of about 26 languages indigenous to the indian subcontinent. it is also classed as a member of the tamil language family, which contains the languages of around 35 ethno-linguistic groups, including the irula and yerukula languages @xcite . the remainder of this paper is organized as follows: the next section includes related work followed by dataset description in section 3. section 4 contains the methodology. results are presented in section 5 and the conclusion is presented in section 6. ## related works hope speech detection has been one of the important areas of research in recent years. researchers have developed a wide range of tools, datasets, and models for text classification problems. in recent years many researchers have developed automatic methods for hope speech detection in social media. these methods rely on popular technologies like machine learning and natural language processing. @xcite did hate speech analysis for short text such as tweets. the proposed dnn method helps in identifying features useful for classification. they evaluated their model with the twitter dataset and obtained good results. @xcite characterized hate speech in online social networks with the help of n degroot's learning model. they found how hateful users are different from normal users using centrality measures and user activity patterns. @xcite carried out hope speech detection task with various models and found out that mbertcased model gave the best results. they employed zero short cross-lingual model transfer which is used to finetune the model evaluation. they found out that degradation of the model performance was due to freezing of base layers of transformer model . @xcite focused on youtube sentiment analysis. the researchers analyzed these data to find their trends and it was found that real-life events are influenced by user sentiments. hope speech can also be termed as the opposite of hate speech. hate speech includes offensive and bad comments on a particular work or a particular person. @xcite these offensive comments create a bad impact on this society. work done by @xcite includes analyzing the corpus of data collected from youtube comments. ## dataset description in this work, we made use of the datasets provided by the association for computational linguistics for hope speech detection for equality, diversity, and inclusion competition. these are multi-lingual datasets constructed by chakravarthi (2020). it consists of comments made by users from the social media platform youtube with 28,424, 17715, 9918, and 6176 comments in english, tamil, malayalam, and kannada respectively, manually labeled. in these datasets, the comments are classified into two different categories as hope-speech and non-hopespeech. the distribution of each language dataset is shown in the ## conclusion due to the pandemic there has been a sudden increase in active social media users which has led to abundant online content. there is a need to promote and motivate positive content to spread peace and knowledge in this society. in this paper, we proposed a transformer-based approach for hope speech detection in 4 different languages (english, tamil, malayalam, kannada). we used the al-bert model with adamw optimizer for classification. our model got an f1 score of 0.880, 0.390, 0.740, and 0.750 in english, tamil, malayalam, and kannada. in future work, techniques like data augmentation can be used to fine-tune the model on more data.