Abstract:
With the increasing reliance on online reviews for decision-making, sentiment analysis has gained popularity. This project aims to develop a deep learning-based framework for categorizing customer feedback as positive or negative, using natural language processing (NLP) techniques. Sentiment analysis involves supervised learning, where a classifier is trained on labeled data and used to categorize new, unseen data. To demonstrate the concept, a prototype application is developed.
Deep learning heavily relies on the availability of extensive training data. In this project, an innovative approach called Deep Learning-based Sentiment Analysis (DLSA) is proposed and implemented. This approach utilizes publicly available ratings as weak supervision signals for training the sentiment classifier. A deep learning framework is suggested and implemented to accomplish this task. The prototype application serves as proof of concept.
Empirical research conducted in this project demonstrates that the proposed system outperforms the current state-of-the-art approaches. This project, based on an IEEE paper, offers an excellent opportunity for final-year engineering students in computer science. The components we provide include:
1.complete documentation support
2.complete working hardware/software implemented in students environment
3.classes will held accordingly.
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