Enhancing Emotion Classification through Bi-LSTM and GloVe Embedding: A Computational Framework for Sentiment Analysis

In the realm of sentiment analysis, discerning and categorizing emotions encapsulated within textual data stands as a pivotal domain known as emotion classification. With the pervasive influence of online social media platforms such as Facebook, Twitter, and Instagram, the textual expressions of individuals' sentiments and thoughts regarding global issues have become increasingly prevalent. Recognizing and interpreting the spectrum of emotions conveyed through diverse textual expressions hold paramount importance. This paper introduces a model for emotion classification, aimed at discerning the underlying sentiments and emotions embedded within written text. Leveraging bidirectional long short-term memory (Bi-LSTM) layers in conjunction with GloVe embedding, our approach endeavors to accurately detect and classify the prevailing emotions within textual data. Through rigorous experimentation, our proposed methodology has achieved a commendable accuracy rate of 70%. Notably, our findings underscore the computational efficiency inherent in our Bi-LSTM-based architecture, thereby advocating its suitability for real-time applications within the domain of sentiment analysis.