ITDS | SPMS

E-COMMERCE SENTIMENT ANALYSIS USING DEEP LEARNING APPROACH WITH LONG SHORT-TERM MEMORY NETWORKS

Project Description
Supervisor

The present study proposes Long Short-Term Memory (LSTM)-based sentiment analysis model and its performance on e-commerce reviews. The proposed LSTM model outperformed baseline models like SVM, Naive Bayes, and CNN with high accuracy, precision, recall, and F1-score on the test dataset. Qualitative case studies also showed the effectiveness of the LSTM model in capturing the exact sentiment of reviews related to e-commerce. These findings represent important contributions to the state of the art in LSTM-based sentiment analysis, guidance for an e-commerce business strategy, and promotion of responsible deployment of the technologies associated with sentiment analysis. The authors suggest that future studies expand datasets, consider other LSTM architectures, and make use of multimodal sources of data together with their contextual information. In this regard, the current study proved that LSTM networks were efficient in enhancing sentiment analysis related to e-commerce reviews. Armed with the power of the LSTM model, e-commerce organizations can develop much more profound knowledge about customer sentiments and preferences, thus offering better customer experiences, informing product development, and making strategic decisions informed by data.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.