A Review on Aspect-Based Sentiment Analysis Using Deep Learning Techniques
Author : Madhuri Saxena, Dr. Vaibhav Patel and Dr. Anurag Shrivastava
Abstract :
Aspect-Based Sentiment Analysis (ABSA) is a fine-grained approach to understanding sentiments by identifying specific aspects of an entity and the sentiments expressed towards them. This paper explores the application of deep learning techniques to ABSA, addressing the challenges of handling unstructured and ambiguous text from diverse domains such as social media, product reviews, and customer feedback. We propose a hybrid model integrating advanced neural architectures, such as Bidirectional LSTMs and Transformer-based models, to effectively capture contextual and semantic information. The model leverages pre-trained embeddings for language representation and incorporates attention mechanisms to focus on aspect- specific sentiments. Experiments are conducted on benchmark datasets to evaluate the performance in terms of accuracy, precision, recall, and F1-score. Results demonstrate significant improvements over traditional machine learning approaches. This research contributes to advancing sentiment analysis by providing a robust and scalable framework for real-world applications, enabling better decision-making in businesses and customer experience management.
Keywords :
Aspect-based sentiment analysis, deep learning, bidirectional LSTM, transformer models, sentiment analysis.
