An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model’s parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO’s computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis.