Sentiment analysis is an instrument in text data classification that offers valuable insights into the contextual refinement of written content. This research aims to conduct a thorough investigation into the enhancement of text data classification by synergistically incorporating sentiment analysis and recurrent neural networks (RNNs) within the domain of natural language processing (NLP). This study evaluates the effectiveness of sentiment analysis in enhancing text data classification by utilizing a sample size of 20, divided into two groups of 10. G-power parameters (0.05 alpha, 0.2 beta) were applied to determine sample adequacy. The methodology involved data collection, generation of artificial datasets, implementation of sentiment analysis, and the construction of a recurrent neural network (RNN). Results indicated that sentiment analysis achieved a higher accuracy of 78.80%, while the RNN reached 70.50%. A sample T-test showed no significant difference between the two methods. Overall, sentiment analysis demonstrated superior performance in capturing subtle distinctions within text data classification compared to RNN.