Enhancing Opinion Mining of Twitter Data with a Deep Convolutional Spiking Neural Network and Balancing Composite Motion Optimization

Social media is the most popular platforms for opinion expression. Sentiment analysis is the process of acquiring information about things, events, and their characteristics out of people's views, assessments, and feelings. Opinion mining is an alternative term for sentiment analysis. In this paper, Enhancing Opinion Mining of Twitter Data with a Deep Convolutional Spiking Neural Network and Balancing Composite Motion Optimization (OMTD-DCSNN-BCMO) is proposed. Initially, the twitter data is obtained from Stanford Sentient Treebank (SST-2) dataset. Then, the data is fed to preprocessing. The pre-processing output is provided to extract the Radiomic features depending on Residual Exemplars Local Binary Pattern (RELBP). The extracted output is provided into the feature selection for choosing ideal features using Piranha foraging Optimization Algorithm. The selected features are provided into Deep Convolutional Spiking Neural Network (DCSNN) for classifying twitter data as negative, positive and neutral. Then, the DCSNN approach is optimized using Balancing Composite Motion Optimization (BCMO) for better performance. The efficacy of proposed technique is examined using performance metrics and method attains 23.32%, 26.07% and 28.51% higher accuracy and 21.92%, 15.03% and 19.15% lesser error rate are evaluated with existing approaches.