Context-Aware Sentiment Analysis on Product Reviews Using Hybrid Model
TL;DR
The system integrates DistilBERT and CNN-LSTM hybrid model where CNN used to extract the local features, and LSTM learns sequential and long-term dependencies in the text, and DistilBERT is used to capture contextual embeddings in the text.
Abstract
Sentiment analysis and sarcasm detection as become an important area in natural language processing (NLP) due to growth of e-commerce and social media platforms. Customers give feedback through reviews which helps to understand the contextual meaning and sentiment present in the text.The system integrates DistilBERT and CNN-LSTM hybrid model where CNN (Convolutional Neural Network) used to extract the local features, and LSTM (Long Short-Term Memory) learns sequential and long-term dependencies in the text, and DistilBERT is used to capture contextual embeddings in the text. The proposed system processes the reviews through preprocessing techniques including text cleaning, negation handling, and tokenization. Experimental results shows that the hybrid model achieves high accuracy compared to standalone and architecture architectures. The proposed model is applied in e-commerce platforms, recommendation systems and customer feedback analysis for better decision making.