BE mo L ex BERT : A Hybrid Model for Multilabel Textual Emotion Classification in B angla by Combining Transformers with Lexicon Features (2023)

Abstract: Multilevel textual emotion classification involves the extraction of emotions from text data, a task that has seen significant progress in high resource languages. However, resource-constrained languages like Bangla have received comparatively less attention in the field of emotion classification. Furthermore, the availability of a comprehensive and accurate emotion lexiconspecifically designed for the Bangla language is limited. In this paper, we present a hybrid model that combines lexicon features with transformers for multilabel emotion classification in the Bangla language. We have developed a comprehensive Bangla emotion lexicon consisting of 5336 carefully curated lexicons across nine emotion categories. We experimented with pre-trained transformers including mBERT, XLM-R, BanglishBERT, and BanglaBERT on the EmoNaBa (Islam et al.,2022) dataset. By integrating lexicon features from our emotion lexicon, we evaluate the performance of these transformers in emotion detection tasks. The results demonstrate that incorporating lexicon features significantly improves the performance of transformers. Among the evaluated models, our hybrid approach achieves the highest performance using BanglaBERT(large) (Bhattacharjee et al., 2022) as the pre-trained transformer along with our emotion lexicon, achieving an impressive weighted F1 score of 82.73%. The emotion lexicon is publicly available at https://github.com/Ahasannn/BEmoLex-Bangla_Emotion_Lexicon

## introduction multilabel emotion classification involves the assignment of several emotion labels to a provided text. this allows for a more comprehensive representation to understand underlying emotional content. however, achieving accurate multilabel emotion classification in resource-constrained lan-guages presents a unique challenge. limited availability of annotated data, linguistic diversity, and cultural variations pose significant hurdles. additionally, the lack of comprehensive emotion lexicons specific to these languages further complicates the task. to tackle these challenges, we present an innovative approach that combines the power of transformers with emotion lexicon features for multilabel emotion classification in bangla. by leveraging pretrained transformer models and developing an extensive bangla emotion lexicon, we aim to enhance the accuracy and effectiveness of emotion classification. ## related work emotion detections from textual content have gained considerable attention in recent years. while extensive research has been carried out in high-resource languages such as english, chinese, and arabic, there remains a scarcity of studies specifically targeting emotion detection and classification in the bangla language. irtiza tripto and eunus ali (2018) presented an lstm-based method for emotion classification in bangla youtube comments by achieving 59.23% accuracy. @xcite used logistic regression for detecting emotions (joy, anger, sorrow, suspense) from bangla short stories. rayhan et al. (2020) predicted six emotions from 7214 bangla texts with the cnn-bilstm model outperforming bigru, achieving 66.62% accuracy. @xcite developed s corpus of 6,523 texts for classifying six emotion categories employing various transformer models, among which xlm-r showed the best results with an f 1 -score of 69.73%. @xcite developed an emotion corpus comprising 9,000 bangla texts in six emotion categories and proposed a weighted ensemble of @xcite introduced a manually annotated bangla noisy dataset comprising of 22,698 bangla texts from various domains, labeled for six fine-grained emotion categories. researchers found that combining transformers with handcrafted features has enhanced performance in various tasks. for abusive language detection @xcite al. (2020 ) combined lexicon features with bert and found improved results in four different datasets. similarly, de bruyne et al. (2021) combined dutch transformer-based bert models named bertje and robbert with lexicon-based methods, resulting in a marginal performance improvement. there are many emotion lexicons have been developed in english but not that much available in bangla. mohammad and turney (2013a) has developed a crowdsourced english lexicon named nrc emolex consists of 14,000 lemmas labeled in plutchik (1980) eight basic emotions and two sentiments. abdaoui et al. (2017) used a semi-automatic translation method to construct french expanded emotion lexicon named feel, from the nrc emolex. ## results and analysis we conducted a total of 24 experiments to evaluate the model we advocate. we have experimented with six pre-trained transformers as shown in the outcomes of our proposed hybrid model are presented in across all transformers, the hybrid model demonstrated improved performance compared to the plain model. in the case of banglabert, we observed a notable increase of 2.42% in the f 1 score for the hybrid model. for banglabert ## conclusion in this study, we have introduced bemolexbert, a novel hybrid model that integrates transformers with lexicon features to enhance multilabel emotion detection in bangla texts. a critical component of this work is the development of be-molex, a specialized emotion lexicon tailored to the nuances of the bangla language. this lexicon encompasses a comprehensive repository of 5,336 unique lexicons, thoughtfully categorized into nine distinct emotion classes. our comprehensive evaluation, involving a comparative analysis between our proposed hybrid model and the plain model, underscores the efficacy of our approach in significantly enhancing emotion detection performance. notably, the monolingual models outperformed their multilingual counterparts, while the examination of preprocessing steps revealed their marginal benefits, suggesting that large language models are proficient in managing noisy data, thereby reducing the necessity for extensive preprocessing. furthermore, we conducted a comparative assessment with other existing models, and the results underscored the state-of-the-art performance achieved by banglabert(large) in conjunction with the bemolex lexicon. these findings collectively highlight the potential and significance of our approach in advancing multilabel emotion classification in the context of the bangla language.