Unlocking Emotions in Text: A Fusion of Word Embeddings and Lexical Knowledge for Emotion Classification (2023)

Abstract: This paper introduces an improved method for emotion classification through the integration of emotion lexicons and pre-trained word embeddings. The proposed method utilizes semantically similar features to reconcile the semantic gap between words and emotions. The proposed approach is compared against three baselines for predicting Ekman’s emotions at the document level on the GoEmotions dataset. The effectiveness of the proposed approach is assessed using standard evaluation metrics, which show at least a 5% gain in performance over baselines.

## introduction emotion classification, poses a intricate challenges involving the task of associating a given text with the best-fitting emotion(s) that encapsulate the author's state of mind @xcite . existing emotion classification models primarily rely on individual words and employ various architectural designs to extract semantic information from sequential inputs, thereby comprehending emotions at the sentence level. recognizing the domain-dependent nature of the emotion classification task, numerous efforts have been made to enrich the understanding of word-level emotional content by constructing and leveraging various manually crafted lexicons. these lexicons have exhibited significant performance of classifiers @xcite the main challenge addressed in the proposed work pertains to identifying the intricate connections between textual content and emotions. to tackle this challenge, we utilize emotion lexicons to bridge the semantic gap between words and emotions. given that a single emotion lexicon may not comprehensively cover all conceivable words or phrases associated with emotions, we employ a combination of four emotion lexicons. rooted in emotion theory, these commonly used lexicons can be categorized as discrete or dimensional lexicons @xcite . discrete lexicons, exemplified by nrc word-emotion association lexicon, also known as emolex @xcite and emosenticnet @xcite , attribute one or more tags from ekman's @xcite six basic emotions to each word. although these lexicons establish a direct link between words and emotions, they are limited range of emotions and provide no insight into emotion intensity. in contrast, dimensional lexicons, such as nrc-emotion intensity lexicon (nrc-eil) (mohammad, 2018b) and nrc valence, arousal, and dominance (nrc-vad) (mohammad, 2018a), assign intensity values based on 1-d, 2-d, or 3-d models. these dimensions include valence, arousal, and dominance (vad) and offer a more nuanced understanding of emotional content. the major contributions of the proposed work can be summed up as follows. (i) a features extraction technique to extract linguistic and semantically-similar features-based vector representation for the emotion classification task, (ii) a pre-trained word embeddings-based linguistic features enriched approach to enhance the performance of emotion classification task, and (iii) an empirical study on emotion classification using google's goemotion dataset. ## related work textual emotion classification has steadily emerged as a dynamic and evolving research domain, witnessing a proliferation of methodologies and techniques over the years @xcite @xcite . with the remarkable strides made in machine learning, deep learning, and word embeddings, the precision and efficacy of emotion classification have witnessed remarkable advancements @xcite . for instance, zahiri and choi (2018) laid the foundation for creating a multiparty dialogue corpus dedicated to text data to categorize emotions. several studies have pursued the enhancement of classifier performance by integrating external knowledge derived from emotion lexicons and syntactic structures. researchers have adeptly intertwined lexicon-based methodologies with machine learning models, yielding notable improvements in classification accuracy @xcite @xcite @xcite . li et al. (2020) introduced the emochannelattn network, which harnessed the underlying dependency relationships present within textual emotion constructs to enhance classification performance. their study delved into a spectrum of 151 finelygrained emotions, utilizing domain-specific knowledge and dimensional lexicon dictionaries. meanwhile, @xcite augmented existing word embeddings with lexical knowledge, increasing effective representation learning. in quest to acquire word representations infused with emotional nuances, @xcite employed a methodology wherein emotionally analogous words were mapped into close proximity within vector spaces. in contrast, emotionally dissimilar words were distanced from each other. however, their approach relied on a categorical lexicon, which constrained its applicability to a specific set of emotions. furthermore, @xcite embraced techniques to incorporate emotional words as salient features for text-based emotion prediction. additionally, @xcite pioneered a lexicon-based, unsupervised sentiment analysis approach, incorporating emotional signals within twitter datasets. @xcite undertook the generation of lexicon-based distributed contexts involving the selection of word vectors guided by a lexicon dictionary. ## proposed approach this section outlines the proposed approach, which capitalizes on a synergistic fusion of word embeddings and emotion lexicon-based features. the proposed approach is designed to elevate the performance of emotion classification by predicting emotion labels at the document-level. an illustrative overview of the proposed approach is presented in figure 1 . ## conclusion in this paper, we have presented an enhanced approach for emotion classification by leveraging semantic and linguistic-based feature representations. to transform each document for the required representation, we have used linguistic and semanticallysimilar features-based vector representations, i.e., linguistic-based features, semantically-similar features, and semantically-similar linguistic features. the empirical analysis conducted on the goemotions dataset demonstrates that classification models significantly outperform baseline approaches by a substantial margin of 5% when employing feature vectors derived from the fusion of pre-trained word embedding and lexicon knowledge.