This study presents a controlled empirical benchmark of Python and R for sentiment classification and topic modeling, addressing the ongoing debate regarding the relative suitability of both programming environments for natural language processing tasks. To ensure a fair comparison, identical experimental conditions were established for both platforms. Using the Pang-Lee corpus of 10,662 labeled review snippets, mirrored multinomial Naive Bayes implementations were evaluated by stratified five-fold cross-validation, while matched six-topic Latent Dirichlet Allocation (LDA) models employed identical preprocessing procedures, tokenization, vocabulary construction, hyperparameter settings, initialization strategy, and Gibbs-sampling order. Classification performance was assessed using Accuracy, Precision, Recall, F1-score, and ROC-AUC, whereas topic-model quality was evaluated using UMass coherence and perplexity. Execution performance was measured under identical WebAssembly-based environments to eliminate hardware-related bias. The experimental results demonstrate that Python and R produced identical predictive performance, achieving a mean Accuracy of 0.7459, F1-score of 0.7450, ROC-AUC of 0.8248, UMass coherence of −2.9997, and perplexity of 318.7501. Although model quality remained unchanged across programming languages, substantial differences were observed in execution efficiency, with the median complete-pipeline runtime equal to 0.833 s for Python and 17.220 s for R under the same execution conditions. Based on these findings, a seven-criterion decision framework is proposed that distinguishes predictive performance, topic-model quality, execution efficiency, statistical workflow, visualization capabilities, reproducibility, and deployment considerations. The proposed framework provides practical guidance for researchers and practitioners selecting between Python, R, or hybrid analytical workflows for text mining and natural language processing applications.