RANGANAYAKI @ LT - EDI : Hope Speech Detection using Capsule Networks (2023)

Abstract: HOPE speeches convey uplifting and motivating messages that help enhance mental health and general well-being. Hope speech detection has gained popularity in the field of natural language processing as it gives people the motivation they need to face challenges in life. The momentum behind this technology has been fueled by the demand for encouraging reinforcement online. In this paper, a deep learning approach is proposed in which four different word embedding techniques are used in combination with capsule networks, and a comparative analysis is performed to obtain results. Oversampling is used to address class imbalance problem. The dataset used in this paper is a part of the LT-EDI RANLP 2023 Hope Speech Detection shared task. The approach proposed in this paper achieved a Macro Average F1 score of 0.49 and 0.62 in English and Hindi-English code mix test data, which secured 2nd and 3rd rank respectively in the above mentioned share task.

## introduction in human life, hope plays a very vital role in healing, betterment and repairing oneself inside out. hope speech reflects the belief that one can find ways to one's desired objectives and become motivated to utilize those ways. social media platforms like facebook and instagram provide users with the opportunity to establish online communities comprising individuals who share common interests, values, or goals. these communities foster a profound sense of acceptance and belonging among their members @xcite . this work aims to encourage a positive way of thinking by moving away from discrimination, loneliness, or other worst things in life to building confidence, support, and good qualities based on comments by individuals. the concept of hope typically encompasses promises, potential, support, comfort, recommendations, and inspiration, all of which are offered to individuals by their peers during challenging moments of illness, stress, loneliness, and sadness (chakravarthi, 2020). nevertheless, conventional approaches in natural language processing such as machine learning algorithms often fall short when it comes to accurately identifying hope speeches. hence, there exists a critical need for innovative techniques that harness the power of deep learning, incorporating the latest advancements in the field. by leveraging these advanced techniques, we can strive to enhance the precision and effectiveness of hope speech detection, ultimately contributing to the improvement of mental health and overall well-being. this progress can be achieved through the dissemination of positive content across various online platforms, aiming to uplift individuals and promote a more optimistic outlook on life @xcite . by embracing these advancements, we can foster a society where hope thrives, enabling individuals to overcome challenges and embrace a brighter future. ## methodology the overall architecture for hope speech detection in english and hindi-english code-mix is given in figure 1 . ## result and analysis after the implementation of the various modules, the results obtained detect the hope texts in the comments. the evaluation metrics taken into consideration are accuracy, macro-precision, macrorecall, and macro-f1-score. fasttext word embedding combined with the capsule network produced better results than the other three embedding techniques -elmo, bert, and xlnet. it was also observed that over-sampling has increased the performance metrics along with preprocessing and spelling correction. tables 4 and 5 show the performance of hope speech detection with respect to various embedding techniques used in english and hindi-english code-mix validation data.