ML & AI _ IIITR anchi@ LT - EDI -2023: Identification of Hope Speech of Y ou T ube comments in Mixed Languages (2023)

Abstract: Hope speech analysis refers to the examination and evaluation of speeches or messages that aim to instill hope, inspire optimism, and motivate individuals or communities. It involves analyzing the content, language, rhetorical devices, and delivery techniques used in a speech to understand how it conveys hope and its potential impact on the audience. The objective of this study is to classify the given text comments as Hope Speech or Not Hope Speech. The provided dataset consists of YouTube comments in four languages: English, Hindi, Spanish, Bulgarian; with pre-defined classifications. Our approach involved pre-processing the dataset and using the TF-IDF (Term Frequency-Inverse Document Frequency) method.

## introduction in a world full with problems, uncertainties, and moments of despair, the power of hope becomes an urgent appeal for resilience and transformation. hope speeches, a distinct mode of communication, have evolved as a powerful tool for instilling optimism, inspiring action, and fostering a renewed feeling of possibilities in individuals and communities. these speeches capture the spirit of optimism by combining words, emotions, and ideas of a brighter future. hope talks overcome linguistic boundaries, reaching audiences of all cultures and languages. they have an extraordinary power to touch hearts, ignite emotions, and motivate people for positive change. this study work tries to uncover the universal characteristics that underlying these messages through the analysis of hope speeches in four different languages; english, hindi, spanish, and bulgarian. this study examines the tremendous impact of hope speeches on individuals and societies, drawing on studies undertaken by several research institutes. the institute of hope studies at the university of oklahoma has conducted research that demonstrates the transforming power of hope in improving mental well-being, increasing resilience, and improving outcomes in a variety of circumstances. furthermore, the university of oklahoma's centre for hope studies provides unique insights into the psychological and social implications of hope speeches in various cultural contexts. we faced some difficulties while analysing hope speech text snippets. the subjective nature of hope was one such obstacle. the distinctive emotional and psychological responses of individuals within a certain cultural and linguistic framework has to be considered when determining the effectiveness of a hope speech. another difficulty was translating and interpreting hope speeches across languages, as variations, cultural allusions, and rhetorical strategies may differ, potentially changing the impact on varied audiences. in this study we have attempted to provide a greater knowledge of hope speech analysis by looking into these difficulties. we aimed to develop a viable system that could detect and categorise hope speech in this language by leveraging several machine learning and deep learning approaches. our experiments involved training and fine-tuning models applying cutting-edge methodologies. by finetuning distilroberta, a language model trained on numerous languages, we hoped to improve the accuracy and effectiveness of our categorization system. our experiment produced encouraging results, with a precision-score of 0.66. this performance displayed the effectiveness of our strategy as well as the efforts put in in tackling the obstacles unique to the english language. we improved our system's capabilities and got commendable results in detecting and categorising hope speech by leveraging the power of transfer learning and combining it with domain-specific fine-tuning. in conclusion, hope speech analysis, particularly text analysis, provides vital insights regarding their universal significance. we can learn how these speeches inspire optimism and resonate with varied audiences by evaluating linguistic and rhetorical aspects such as metaphors, imagery, and narrative frameworks. text analysis allows us to delve into the complexities of language, revealing the tactics used by speakers to provoke emotions and transmit positive messages. however, difficulties arise in the subjective interpretation and translation of hope speeches, highlighting the importance of taking cultural settings and language variations into account. by negotiating these intricacies, we gain a better grasp of hope speech analysis, allowing us to use words to inspire good change in individuals and communities alike. our work has been organized in a step-by-step to represent our work in a much efficient way. section 2 describes work done in the related field, section 3 & 4 identifies the outlines of the task and dataset description, section 5 mentions the various methodologies adapted and their results on validation dataset. lastly, we have discussed the result and concluded in section 6 and 7 respectively. ## related works for the purpose of fostering hope and uplifting individuals, researchers and organizations involved in hope speech analysis have recognized the importance of exploring and developing effective methodologies and models. in order to inspire and empower individuals, studies have been conducted to examine various approaches to hope speech analysis across different fields. just as social media companies have been obligated to support sentiment analysis research for the protection of users from cyberbullying, researchers in hope speech analysis strive to enhance their understanding of the key components that instill hope and promote optimism. through the examination of different methodologies and models, researchers of @xcite seek to uncover the most impactful elements that contribute to the effectiveness of hope speeches. by investing in hope speech analysis research, scholars and organizations aim to unlock the potential for positive change, resilience, and motivation in individuals and communities. the authors of @xcite used youtube comments to do opinion mining and trend analysis. the researchers examined attitudes to determine trends, seasonality, and projections; user sentiments were discovered to be highly associated with the impact of real-world events. the research @xcite conducted a thorough study of opinion mining using youtube comments. the authors created a comment corpus with 35k hand labelled data in order to predict the opinion polarity of the comments using tree kernel models. the works @xcite @xcite used a social network analysis and mining approach to find hope speech in youtube comments. their study emphasises the role of hope within online networks and its potential impact on human well-being. they create a framework specifically designed to identify instances of hope speech using sentiment analysis and natural language processing techniques. they successfully extract relevant patterns and features for accurate detection by employing data mining techniques and taking into account linguistic, visual, and social elements. their study contributes to a better understanding of the role of hope in digital communities by providing significant insights into the detection of hope speech in online platforms. the researchers of @xcite investigate the application of computational approaches to analyse peace discourse in the context of kashmir in their research. through computer analysis, their research provides unique insights into the dynamics of peace-related communication. they use powerful computational approaches to shed light on the voices advocating for peace in the region. the work @xcite explored using deep learning, specifically convolutional neural networks (cnns), for sentiment analysis. they investigate various word vector representations' effectiveness and advances understanding of using neural networks for discerning sentiment in text data. the works @xcite employed deep neural network models based on transformers to detect offensive remarks in bangla social media. bert (bidirectional encoder representations from transformers) and electra (efficiency learning an encoder that classifies token replacements accurately) pretraining language architectures are used in tandem. the authors constructed a one-of-a-kind dataset that includes 44,001 comments from a wide range of bangla-language facebook postings. hence, the existing literature on hope speech analysis includes a diverse set of ideas and methodologies. the evaluated works in this subject stress the importance of experimenting with various data mining techniques, using benchmark datasets, leveraging deep learning models, and conducting comparative studies. these findings highlight the necessity of building thorough frameworks, using credible datasets, and using lessons from prior research in order to progress the area of hope speech analysis. the proposed approaches, benchmark datasets, and findings from these studies are useful tools for scholars and practitioners interested in developing successful computational strategies for instilling hope and optimism. building on these references, our work in hope speech analysis intends to be inspired by proven methodology and to perform a comparative research of our dataset in order to uncover and develop effective ways. we intend to contribute to the development of strong approaches in hope speech analysis that can empower individuals and communities, create resilience, and inspire positive change by incorporating insights from prior studies and utilising our own comparative analysis. ## task description the primary objective of this task is to perform hope speech analysis on youtube comments in four different languages, which are english, hindi, spanish and bulgarian. health professionals believe that hope is important for human well-being, healing, and repair. hope speech represents the notion that one may uncover and get motivated to employ pathways to one's desired goals. our approach strives to shift popular thinking away from preoccupations with prejudice, loneliness, or the negative aspects of life and towards fostering confidence, support, and positive traits based on individual comments. hope speech analysis refers to the examination and evaluation of speeches or messages that aim to instill hope, inspire optimism, and motivate individuals or communities.it entails analysing a speech's content, language, rhetorical devices, and delivery tactics to determine how it transmits hope and its potential impact on the listener. participants are presented with training, devel-opment, and test datasets in four languages (hindi, english, spanish, bulgarian), some of them being code-mixed. the datasets are annotated at the comment/post level. a comment or post may contain more than one sentence, although the corpus's average sentence length is one. participants can opt to classify one or more code-mixed languages. each language's leaderboard results were released. we address this problem using a variety of machine learning approaches and methodologies. these include typical machine learning algorithms, deep learning models, or a combination of the two. by training and fine-tuning such models on the available information, we hope to develop a robust classifier capable of accurately predicting the level of depression displayed in unseen social media posts. ## dataset description the dataset utilized for the hope speech analysis task in multiple languages comprises a diverse collection of youtube comments. it is segmented into three distinct subsets: a training dataset, a development dataset, and a test dataset. the dataset consists of 2 labels, which differ for the languages. for english, "hope speech" is used for hope speech and "non hope speech" for not hope speech. for hindi, "hope" is used for hope speech and "not-hope" for not hope speech. for spanish, "hs" is used for hope speech and "nhs" for not hope speech. for bulgarian, "true" is used for hope speech and "false" for not hope speech. the dataset was separated into three parts for the competition: the training set, the development set, and the test set. the labels for the test set were hidden by the competition's administrators because this section was exclusively utilised to evaluate the competitors' solutions. the training dataset provided to us contains a large number of youtube comments composed in many languages and code-mixed format. each post in the training dataset is labelled with a label indicating whether it is a hope or non-hope comment. these labelled annotations served as the basis for training classification models, helping the construction and refining of machine learning or deep learning models. the dev dataset, also known as the validation dataset, supplements the labelled data provided. it enabled us to analyse model performance and fine-tune hyperparameters during the development period. the labelled information in the development dataset aids in determining whether the algorithms are accurate in categorising social media text as original or fake news. ## methodology in our study, we used a variety of approaches on the development dataset to determine the most successful approach for making predictions on the test dataset. numerous methodologies were used in the experiments carried out during the inquiry. these included data preprocessing, tf-idf based classification and bag of words classification. the raw english social media postings were processed through a range of text cleansing processes during the initial data pre-processing step. to standardise the textual data, these include the removal of punctuation, stop words, and special characters, as well as tokenization and stemming procedures. this stage of pre-processing ensures that the text is properly prepared for further analysis. following that, tf-idf-based classification is implemented. each document, i.e., youtube comment, is represented as a numerical feature vector using the tf-idf (term frequency-inverse document frequency) approach. the methodology involves bag of words classification after tf-idf-based classification. the text data is represented using a bag of words model, which creates a vocabulary comprised of unique words extracted from the corpus. following this methodology, which includes data pre-processing, tf-idf and bag of words classification, a comprehensive and effective approach for accurately categorising code-mixed youtube comments was developed.