International Journal of Computer Techniques Volume 12 Issue 4 | A Neural Network Approach to Sentiment Analysis
A Neural Network Approach to Sentiment Analysis
Authors:
Sandip Kumar Singh, Mohit Srivastava, Neeraj Kumar, Neha Singh
Department of Computer Science and Engineering, RRIMT Lucknow
Ankit Singh
Department of Computer Science and Engineering, BBDITM Lucknow
Journal: International Journal of Computer Techniques (IJCT)
Volume: 12 | Issue: 4 | Publication Date: July – August 2025
ISSN: 2394-2231 | Journal URL: https://ijctjournal.org/
Abstract
This paper presents a comprehensive overview of sentiment analysis using neural networks. It explores architectures including CNNs, RNNs, LSTMs, and Transformers, and discusses their implementation for sentiment classification. A case study on benchmark datasets demonstrates the superior performance of deep learning models over traditional approaches. The paper concludes with future directions such as attention mechanisms, aspect-based sentiment analysis, and interpretable neural models.
Keywords
Sentiment Analysis, Neural Networks, Deep Learning, Natural Language Processing, CNN, RNN, LSTM, Word Embeddings, Transformer
Conclusion
Neural networks have revolutionized sentiment analysis by capturing linguistic subtleties and context. Models like CNNs and LSTMs outperform classical methods, while Transformers offer state-of-the-art results. Future work should focus on larger pre-trained models, aspect-based sentiment, and explainability to enhance trust and applicability in real-world systems.
References
Includes 15+ references from EMNLP, NAACL, ICLR, Cambridge University Press, and IEEE covering neural architectures, embeddings, and sentiment classification benchmarks.
