TERCET @ LT - EDI -2023: Hope Speech Detection for Equality, Diversity, and Inclusion (2023)

Abstract: 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.

## introduction with the world around us developing at an incomprehensible speed and the advent of the 'digital age,' the way we communicate has fundamentally evolved. technology has become a pivotal role in ensuring connectivity among humans in the form of calls, messages and most importantly, social media. social media is a platform for people to create, share and collaborate their ideas, information and opinions with each other virtually. the number of users only keeps increasing and these platforms show no signs of slowing down. @xcite social media is a platform of the masses, for the masses. while social media is a great tool to enable, entertain and equip people, it has its own drawbacks. not every user has positive intentions while using popular platforms like facebook and twitter. this may lead to presence of unwanted content and comments that can range from discouragement to targeted hate speech. detrimental speech on such platforms have an extended psychological impact on the victim of such comments @xcite . to ensure the cleanliness and positive environment of such platforms, it is quintessential to identify such texts and moderate them. applying sentiment analysis using data from social media platforms is a great tool to analyse and understand the feelings of the general population @xcite . sentiment analysis is a process where multiple people's opinions, views, feelings and emotions are analysed based on different projects, topics and general discourse. it is also known as opinion mining @xcite . sentiment analysis is an incredibly useful tool to automate the detection of negative comments on social media by analysing the sentiments from the user-generated text. both supervised and unsupervised learning can be applied to perform sentiment analysis. the only drawback of performing supervised learning is that high-quality training data with proper labels are required for the model to predict accurately. due to the non-requirement of a labelled training data, unsupervised learning is more robust and can be used more widely to perform sentiment analysis @xcite . the given task is related to detecting hope speech and non-hope speech from user-generated text on the popular video-sharing platform, youtube. hope being a positive sentiment, caters to increasing the general positivity of the social me-dia platform and thus improving the mood of users alike.(chakravarthi, 2020) hope speech therefore contributes the same and helps motivate, encourage and inspire individuals on the platform. non-hope speech being a negative sentiment has an adverse impact on the social media environment by propagating negative feelings through the users. detecting and moderating these comments is imperative to create an inclusive and safe space for millions of users to share a part of themselves. @xcite the text in the given text is in the form of codemixed data. usually, on social media, most data is not grammatical in nature and has some words and phrases from the native language but in non-native scripts. @xcite in our data set, the text is code-mixed in english-tamil. this can be attributed to the ease of typing in the roman script on social media while conveying the same intended sentiment. @xcite being able to write code-mixed text on social media provides users with a wider choice to express themselves freely and more accurately. the paper is organized as follows: section 2 pertains to related works as per the literature survey; section 3 is related to the task and data description; section 4 pertains to the methodology used to perform this task; section 5 shows the results and analysis of the results and section 6 entails the conclusion. ## related works opinion mining or sentiment analysis is a growing field with increasing applications on social media and e-commerce platforms. to cater to the same, extensive research is going on in this field to build the most efficient and robust models which range from multi-layer models (mlms) to natural language processing(nlp) models. research conducted by @xcite used the transformer model, albert for doing hope speech detection in the english, tamil, malayalam and kannada language. a new convolutional neural network(cnn) based model was proposed by @xcite that outperformed other traditional models for detecting hope-speech. both binary hope speech classification as well as multi-class hope speech classification was performed by @xcite . the binary task involved only two labels whereas the multi-class task involved three labels. multiple traditional, transformer and deep learning models were applied on the dataset. a logistic regression classifier was applied by @xcite with a l2 regularization whose results indicated that a hope-speech classifier with good precision and recall can be constructed. models based on long short term memory (lstm) network, deep learning and hybrid learning on the tamil and malayalam language dataset were used by @xcite . the best performing model on the english dataset was the 2-parallel cnn-lstm that used glove and word2vec embeddings. the best performing model on the malayalam dataset was the 3-parallel bi-lstm. multiple transformer models for hope speech detection in the english, tamil and malayalam languages were applied by @xcite . the models applied were the multilingual m-bertcased, xlm-roberta(xlmr), and indicbert. among these models, the m-bert-cased model gave the best f1-score. hope detection was done by @xcite for three language data sets in tamil, english and malayalam. they applied language models that include compact language detector 2, compact language detector 3, langid, textblob language detector, and langdetect. the experimental results from the same showed that detecting hope detection from text is a difficult task especially with codemixed data. these papers exhibit the versatility of the models that can be applied to perform sentiment analysis on comments from social media. based on literature survey, we decided to apply traditional models with the application of a simple transformer model for a more accurate classification of text. ## task and data description the shared task on hope speech detection for equality, diversity, and inclusion at lt-edi -ranlp 2023 @xcite is intended to determine whether the text was hope speech or non-hope speech. the data set was available for four languages, english, hindi, bulgarian and spanish, but we submitted the english data set only. the data set consisted of two fields: text and labels which were gathered from youtube comments. the training data set consisted of around 18191 texts out of which 16630 were labelled as non-hope speech and 1561 were labelled as hope speech. the development data set for english consisted of 4547 comments out of which 4148 were labelled as non-hope speech and 399 were labelled as hope speech. the test data had 4805 texts out of which 4783 were classified as non-hope speech and 22 were classified as hope speech. figure 2: description of data ## methodology multiple traditional models were employed to identify which texts were hope speech from the youtube comments given in the data set. ## conclusion through the scope of this paper, we have explored and presented a traditional model coupled with a simple sentence transformer model (labse) to perform classification of hope-speech and non-hope speech on the given data by dravidianlangtech in the english language. it was noted that svm gave the best performance metrics against other traditional models with a macro f1 score of 0.50. it is our belief that the classification can be improved by applying deep learning models on the given data set to obtain a higher accuracy.