Text categorization plays a crucial role in various NLP applications, including sentiment analysis, subject labeling, and question answering. Machine-learning-based text classification is a key research subject with numerous applications, including spam detection, hate speech identification, reviews, rating summarization, sentiment analysis, and topic modeling. Machine learning algorithms for text classification have proven to be quite reliable and are widely employed. We introduce SentCNN, based on convolutional neural network (CNN) for text classification that addresses NLP challenges. SentCNN's simplified architecture ensures outstanding performance while lowering processing costs. In this work, Stanford Sentiment Treebank dataset is used for text classification. The proposed SentCNN achieved remarkable results with an accuracy of 95.2% with 95.6% recall, and an F1 score of 95.2% and compared with sophisticated models like DPCNN, RCNN, and TextCNN. The comparative results shows that the proposed SentCNN outperformed existing models and effective for text classification.
Keywords
Text Classification, Natural Language Processing, Convolutional Neural Networks and Stanford Sentiment Treebank.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: IJ, AR;
Methodology: IJ, AR;
Software: AR;
Data Curation: IJ;
Writing- Original Draft Preparation: IJ, AR;
Visualization: IJ;
Supervision: AR;
Validation: IJ, AR;
Writing- Reviewing and Editing: IJ, AR;
Writing- Original Draft: IJ, AR; All authors reviewed the results and approved the final version of the manuscript.
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Cite this article
Iram Javed and Arulmurugan Ramu, "SentCNN - A Lightweight and Efficient Deep Learning Model for Accurate Text Classification", Journal of Future Networks and Communications, vol.1, no.1, pp. 033-040, January 2025. doi: XXXX/XXXX/JFNC202501004.
