Mr. Vaibhav Gajananrao Ingole
Download PDF- Sentiment analysis has emerged as an important search area in Natural Language Processing (NLP), machine learning, computational linguistics, and artificial intelligence. The objective of sentiment analysis is to automatically identify opinions, emotions, attitudes, and polarity expressed in textual data. The rapid expansion of social media, e-commerce platforms, online reviews, blogs, discussion forums, and digital communication has generated enormous volumes of opinionated content, making automated sentiment analysis increasingly important for organizations and researchers. This paper presents a comprehensive review of sentiment analysis techniques developed up to 2020. The review covers lexicon-based methods, traditional machine-learning algorithms, feature-engineering techniques, deep-learning models, aspect-based sentiment analysis, social-media sentiment analysis, and transformer-based approaches. Particular attention is given to Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, attention mechanisms, contextual word representations, and Bidirectional Encoder Representations from Transformers (BERT). The paper also reviews commonly used datasets and evaluation measures and discusses challenges involving negation, sarcasm, ambiguity, domain adaptation, implicit aspects, multilingual text, code-mixed language, and noisy social-media content. The review demonstrates the transition from manually engineered features toward contextual representation learning and identifies important research directions for multilingual, explainable, robust, and aspect-aware sentiment analysis.
