VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Using Transformers and Stacked Embeddings (2023)

Abstract: Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles. Emotion detection from complex dialogues is challenging and often requires context/domain understanding. Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word embedding representations with tailored prepossessing strategies to capture the nuances of emotions expressed. Our experiments used static and contextual embeddings (individual and stacked) with Bidirectional Long short-term memory (BiLSTM) and Transformer based models. We occupied rank tenth in the emotion detection task by scoring a Macro F1-Score of 0.2717, validating the efficacy of our implemented approaches for small and imbalanced datasets with mixed categories of target emotions.

## introduction digitalization and ease of access to internet-based intelligent and interactive technologies have led to an unprecedented amount of textual data generation from social media, customer reviews, and online forums. therefore, the need to accurately understand and extract emotions and sentiments from text has become imperative for two reasons; first, due to their various crucial applications such as sentiment analysis @xcite , chatbots, mental health assessment @xcite , social media monitoring, market research, brand management, and customer feedback analysis and second to reduce the human efforts, time and resource requirements. the shared task on empathy detection, emotion classification and personality detection in interactions of wassa 2023 foot_0 aims to develop models to predict various targets, including emotion, empathy, personality, and interpersonal-index, from textual data @xcite . the shared task consists of five tracks, of which we participated in track 3: emotion classification (emo), which targets emotion classification at the essay level. this work presents two systems to capture the subtle notion of emotions expressed through texts: a) bilstm-based @xcite dl model using static, contextual, and the combination of static and contextual (stacked) embeddings and b) bidirectional encoder representations from transformers (bert) @xcite . stacked embeddings @xcite are fast-to-train, powerful but underutilized representations; therefore, to reckon their efficacy compared to the transformer model, we have used them in this work. our proposed systems have performed competitively and got the tenth rank foot_1 in the evaluation phase of the track 3: emo task. the remainder of the paper is structured as follows: section 2 presents the notable research works on emotion detection. section 3 presents the problem statement, dataset description, and the preprocessing strategies applied. in section 4, we present our different classification systems and the experimental setup. section 5 presents the evaluation results of our proposed systems and comparison with other participating teams of the shared task. finally, section 6 provides the conclusion and discusses the future research directions. ## literature survey the significance of accurate emotion detection and sentiment analysis extends beyond understanding textual data. recent research has brought the machines one step closer to mimicking humans' innate ability to understand emotional cues from and text and different modalities. works such as @xcite explore the emotion detection form texts; @xcite @xcite explores the variants of transformer models useful for emotion detection from texts. some notable works such as @xcite @xcite @xcite have created the novel datasets from textual and conversational settings to address the scarce data challenges in complex domains for emotion detection. ## problem statement, dataset description and data preprocessing in this section, we have mentioned the problem statement tackled, the dataset description, and the data-prepossessing techniques implemented for our experiments. ## methodology this section describes our different systems (classification models) based on the bilstm and transformer model implemented for the emotion classification task. ## results and analysis ## conclusion our system, visu, participated in the shared task track 3: emo of emotion classification tasks of the wassa 2023, and our bert base system scored tenth rank. our experiments conclude that although flair are powerful word representations built to capture out-of-vocabulary words, they are not as effective as contextual embeddings when used for small and imbalanced datasets. our future research aims to address the data imbalance and scarce data challenges @xcite by incorporating novel augmentation techniques of domain adaptation @xcite to interpret better the emotions expressed in text.