Advancing Natural Language Processing with a Combined Approach: Sentiment Analysis and Transformation Using Graph Convolutional LSTM

Sentiment analysis is a key component of Natural Language Processing (NLP), taking into account the extraction of emotional cues from text. However, traditional strategies often fail to capture diffused feelings embedded in language. To deal with this, we advocate a novel hybrid model that complements sentiment analysis by way of combining Graph Convolutional Networks (GCNs) with Long Short-Term Memory (LSTM) networks. This fusion leverages LSTM’s sequential reminiscence abilities and GCN’s ability to model contextual relationships, allowing the detection of nuanced feelings regularly overlooked with the aid of conventional techniques. The hybrid technique demonstrates superior generalization overall performance and resilience, making it mainly powerful in complicated sentiment detection responsibilities that require a deeper knowledge of text. These results emphasize the capacity of combining sequential memory architectures with graph-based contextual facts to revolutionize sentiment analysis in NLP. This study not only introduces an innovative approach to sentiment analysis but also underscores the importance of integrating advanced techniques to push the boundaries of NLP research. This cutting-edge hybrid model surpasses the performance of previous techniques like CNN, CNN-LSTM, and RNN-LSTM with an amazing accuracy of 99.33%, creating a new benchmark in sentiment analysis. The results demonstrate how more precise sentiment analysis made possible by fusing sequential memory architectures with graph-based contextual information might revolutionise NLP. The findings provide a new benchmark, advancing the sphere by way of enabling greater specific and nuanced sentiment evaluation for a wide range of programs, inclusive of purchaser remarks analysis, social media monitoring, and emotional intelligence in AI structures.