Explainable Artificial Intelligence (AI) In Sentiment Analysis
TL;DR
Experimental results demonstrate that XAI techniques significantly enhance the interpretability of sentiment prediction without substantially compromising classification performance, and explainable sentiment analysis supports fairness assessment, bias detection, regulatory compliance, and informed decision-making in critical domains such as healthcare, finance, education, and social media analytics.
Abstract
Sentiment analysis has become an essential Natural Language Processing (NLP) technique for extracting opinions and emotions from textual data generated through social media, online reviews, blogs, and customer feedback. Although deep learning models such as Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and other transformer-based architectures have achieved remarkable accuracy in sentiment classification, their black-box nature limits interpretability and user trust. Explainable Artificial Intelligence (XAI) addresses this limitation by providing transparent and understandable explanations for model predictions, enabling users to identify the key words, phrases, and contextual features that influence sentiment classification. This paper presents a comprehensive study of Explainable AI techniques applied to sentiment analysis, focusing on both model-agnostic methods, including Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and attention-based explanation mechanisms. The proposed framework integrates text preprocessing, feature extraction using TF-IDF and contextual embeddings, sentiment classification through machine learning and deep learning models, and explanation generation to improve model transparency. Performance is evaluated using publicly available sentiment datasets based on accuracy, precision, recall, F1-score, and explanation quality. Experimental results demonstrate that XAI techniques significantly enhance the interpretability of sentiment prediction without substantially compromising classification performance. Furthermore, explainable sentiment analysis supports fairness assessment, bias detection, regulatory compliance, and informed decision-making in critical domains such as healthcare, finance, education, and social media analytics. The findings highlight that integrating explainability with sentiment analysis not only increases model reliability but also promotes greater user confidence and responsible deployment of artificial intelligence systems.