Convolutional Neural Networks for Text Classification: A Comprehensive Analysis

Abstract

Abstract: This research paper explores the integration of Convolutional Neural Networks (CNNs) with the VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis tool for text classification tasks. CNNs have shown promising results in text classification, while VADER is a well-established lexicon and rule-based sentiment analysis tool. By combining the strengths of both approaches, we aim to enhance the accuracy and effectiveness of text classification models. The proposed approach leverages the local context capture capabilities of CNNs and the sentiment analysis capabilities of VADER to classify text into predefined categories. We evaluate the performance of the CNN with VADER model on benchmark datasets, comparing it with other state-of-the-art text classification models. The results demonstrate that the integration of CNNs with VADER significantly improves classification accuracy and provides a more nuanced understanding of sentiment in textual data. This research contributes to the field of text classification by highlighting the benefits of combining deep learning models with sentiment analysis tools for more accurate and informative classification.