Event-Driven Context-Aware Sentiment Analysis using BERT - Bi-LSTM for Emotion Insights
Keywords:
Classification, Sentiment Analysis, Context Awareness, Embedding, Event DrivenAbstract
In the digital era, vast amounts of user-generated content in the form of tweets, often comprising tweet opinion and feedbacks, are replete with valuable insights. Users frequently express their sentiments through a blend of text and emojis, providing rich context for understanding their emotions. Within this context, sentiment analysis is a critical task, with classification at its core. This research focuses on advancing sentiment analysis with a keen eye on context awareness, particularly during dynamic events. The proposed approach utilizes event-driven sentiment analysis to gain a nuanced understanding of user sentiments by considering the surrounding context in which content is created. Leveraging state-of-the-art techniques, such as Bidirectional Encoder Representations from Transformers (BERT) for word embeddings and Bi-directional Long Short-Term Memory (Bi-LSTM) classifiers, enhance sentiment analysis accuracy. The results of classifier reflect significant improvements, effectively capturing the context related elements and evolving event driven context aware sentiment landscape in response to dynamic contexts.
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