This paper examines the effectiveness of various transformer models—BERT, RoBERTa, DistilBERT, and ALBERT—in conducting sentiment analysis or opinion mining on datasets derived from airline tweets, IMDB, and Yelp. By utilizing transfer learning, these models can effectively analyze the nuanced language and slang found in tweets, resulting in accurate and context-aware predictions. Furthermore, the explainability of these models is improved through the use of Local Interpretable Model-agnostic Explanations (LIME) and SentiWordNet. These tools enhance the interpretability of the transformer-based models by identifying the key features that influence predictions. Their ability to handle informal expressions significantly enhances sentiment-based text classification. Overall, the transformer models demonstrate strong performance in accurately analyzing and categorizing individual opinions across diverse datasets, showcasing their adaptability to new domains.