Revolutionizing Emotion Analysis with GRU-Enhanced Deep Learning

This is the authors' abstract. We don't add key points for this paper.

This study explores emotion analysis in natural language processing (NLP) using bidirectional gated recurrent units (GRUs). Traditional models, which only process data in a single direction, are less effective than bidirectional GRUs since they cannot capture dependencies in sequential data from both past and future contexts. For tasks like emotion analysis, where context is crucial, this bidirectional capability is vital. The proposed model is built using a sequential framework. The first step involves an embedding layer that converts word indices into dense vectors for handling textual data. To prevent overfitting, a dropout layer with a rate of 0.5 is included. This is followed by two bidirectional GRU layers, each set to return sequences to capture detailed temporal dependencies. A batch normalization layer is applied to stabilize and speed up training. An additional bidirectional GRU layer, with six units and a softmax activation function, further enhances the model’s ability to capture intricate dependencies from both directions, classifying emotions into six categories. The final layer is dense. The model is optimized using the Adam optimizer and employs sparse categorical cross-entropy as the loss function, with accuracy as the performance metric. This design demonstrates the effectiveness of bidirectional GRUs for emotion analysis, achieving a 94% accuracy rate. The model’s ability to uncover complex relationships in sequential data is evident in the results. The model summary details the components and parameter choices that contribute to its high performance.