A Performance-Centric Evaluation of Machine Learning (ML) and Deep Learning (DL) Models for Sentiment and Opinion Analysis

Opinion mining, commonly known as sentiment analysis (SA), is crucial for the retrieval of useful knowledge from massive quantities of text that is written over various subjects as vast as web social media and customer opinions of products. In this paper, the authors assess performance for specific ML and DL architectures specifically adapted for sentiment and opinion mining tasks. This paper does an in-depth comparison of the standard models used in machine learning, such as SVM, Naive Bayes, and Decision Trees, with the more advanced deep-learning methodologies like LSTM networks, CNNs, and integrated architectures that merge both paradigms. The main goal is to evaluate these models' effectiveness in classifying sentiment into positive, negative, and neutral classes among diverse datasets that range from evaluating products to social media communications. This paper compares and evaluates the performance of the models based on key performance metrics such as accuracy, precision, recall, and $\mathbf{F 1}$-score. Based on a comparative analysis, this study identifies the strengths and weaknesses and the tradeoffs that the ML and DL techniques carry, especially while dealing with noisy, complex datasets coming from real-world scenarios on a large scale. This brings to light the appropriateness of models for certain sentiment and opinion analysis tasks besides giving a clearer view regarding their applicability and limitations. Findings such as these point the way forward in enhancing the robustness, scalability, and generalization across models for myriad formats of text. Future Research Directions. This could push the frontiers of the research by rectifying the present inadequacies in terms of insensitivity to noise, adaptability of models, and efficiency of algorithms for the models.