Social media is the most popular platform 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 are obtained from the Stanford Sentiment Treebank (SST-2) dataset. Then, the data is fed to the preprocessing. The pre-processing output is provided to extract the Radiomic features depending on the Residual Exemplars Local Binary Pattern (RELBP). The extracted output is provided to the feature selection for choosing ideal features using the Piranha foraging Optimization Algorithm. The selected features are provided to a 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 the proposed technique is examined using performance metrics and the 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.