Enhancing Graph-Based Sentiment Analysis Models with Hill Climbing

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

In the current study, sentiment graphs were constructed in which the nodes represented emotion-laden words, and the edges depicted their weighted semantic associations. To improve the model, the hill climbing method was employed, which iteratively adjusted parameters to achieve increasingly higher classification accuracy. The developed system employed a combination of graph neural networks (GNNs) and hill climb-based optimisation to improve the efficiency of sentiment categorisation. The experiment's outcomes reveal that the suggested model reached a maximum accuracy of 96.95%, which is higher than traditional sentiment analysis methods and thus proves its appropriateness for emotion-aware text representation. The experimental findings confirm that GNN-based sentiment representation and hill climbing optimisation effectively leverage the intricate emotional relationships, resulting in better sentiment classification. The graphs illustrating optimisation progress and the structure of the sentiment graph further demonstrate the effectiveness of our method.