Language as a significant part of communication should be inclusive of equality and diversity. The internet user's language has a huge influence on peer users all over the world. People express their views through language on virtual platforms like F
acebook, Twitter, YouTube etc. People admire the success of others, pray for their well-being, and encourage on their failure. Such inspirational comments are hope speech comments. At the same time, a group of users promotes discrimination based on gender, racial, sexual orientation, persons with disability, and other minorities. The current paper aims to identify hope speech comments which are very important to move on in life. Various machine learning and deep learning based models (such as support vector machine, logistics regression, convolutional neural network, recurrent neural network) are employed to identify the hope speech in the given YouTube comments. The YouTube comments are available in English, Tamil and Malayalam languages and are part of the task EACL-2021:Hope Speech Detection for Equality, Diversity and Inclusion''.
Due to the development of modern computer technology and the increase in the number of online media users, we can see all kinds of posts and comments everywhere on the internet. Hope speech can not only inspire the creators but also make other viewer
s pleasant. It is necessary to effectively and automatically detect hope speech. This paper describes the approach of our team in the task of hope speech detection. We use the attention mechanism to adjust the weight of all the output layers of XLM-RoBERTa to make full use of the information extracted from each layer, and use the weighted sum of all the output layers to complete the classification task. And we use the Stratified-K-Fold method to enhance the training data set. We achieve a weighted average F1-score of 0.59, 0.84, and 0.92 for Tamil, Malayalam, and English language, ranked 3rd, 2nd, and 2nd.
