Authors: Prof. Sachin Sambhaji Patil, Anthon Rodrigues, Rahul Telangi, Vishwajeet Chavan
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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.
Text classification, the task of assigning predefined categories or labels to textual data, is a fundamental problem in natural language processing (NLP). It plays a crucial role in various applications, including sentiment analysis, topic classification, spam detection, and document categorization. Convolutional Neural Networks (CNNs) have gained significant attention in recent years as powerful deep learning models for text classification tasks due to their ability to capture local patterns and hierarchical representations within the text. In this research paper, we focus on applying CNNs for text classification specifically in the context of Twitter data. Twitter has become a prominent platform for real-time information sharing, making it a valuable source for analyzing public sentiment, tracking trends, and monitoring social discussions. To explore the effectiveness of CNNs for Twitter text classification, we utilize a large dataset consisting of 1.6 million tweets.
In addition to employing CNNs, we incorporate the VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis tool into our framework. VADER is specifically designed for social media texts, including Twitter data, and provides a lexicon-based approach to sentiment analysis by considering contextual valence and sentiment intensities. By integrating VADER with CNNs, we aim to enhance the performance of sentiment classification on the Twitter dataset.
Our objectives in this research paper are twofold: first, to investigate the effectiveness of CNNs for text classification on a large-scale Twitter dataset, and second, to evaluate the impact of incorporating VADER into the CNN framework for sentiment analysis. We conduct extensive experiments to compare the performance of different CNN architectures and variations, as well as the performance of the CNN-VADER hybrid model, with respect to accuracy, precision, recall, and F1 score.
The findings of this research have implications for both academia and industry. Understanding the capabilities and limitations of CNNs for text classification on Twitter data can contribute to the development of more accurate sentiment analysis systems and better understanding of social media dynamics. Furthermore, the integration of VADER with CNNs can potentially improve sentiment classification in real-time applications that rely on Twitter data.
II. LITERATURE SURVEY
III. REQUIREMENTS SPECIFICATIONS
A. Software Requirements
B. Hardware Requirements
The flowchart represents the process of text classification using CNNs. It involves taking raw text as input, pre-processing and tokenizing the text, padding the sequences to make them equal length, feeding the sequences into a CNN model for feature extraction, and finally obtaining the predicted output class or label.
Convolutional Neural Networks (CNNs) have shown remarkable success in computer vision tasks, but they can also be effectively applied to text classification tasks. In the context of text classification, CNNs are designed to extract and capture local patterns and hierarchical representations from textual data.
The basic idea behind CNNs for text classification is to treat the text as a one-dimensional signal, where the words or characters form the sequential input. The CNN architecture consists of convolutional layers, pooling layers, and fully connected layers.
Incorporating VADER sentiment analysis into the CNN framework for text classification further enhances the model's performance. VADER is a lexicon-based approach that considers the contextual valence and sentiment intensities of words. By integrating VADER, the CNN model gains an additional understanding of sentiment-related features, aiding in sentiment classification tasks.
A. Dataset Collection
B. Text Pre-processing
C. Embedding Representation
D. Model Architecture
E. Sentiment Analysis with VADER
F. Model Training and Evaluation
G. Additional Analysis with YouTube Comments
H. Streamlit Application Development and Deployment
In this research paper, we proposed a methodology for text classification using a Convolutional Neural Network (CNN) with VADER sentiment analysis. We collected a Twitter dataset of 1.6 million tweets and pre-processed the data by cleaning, removing noise, and tokenizing. GloVe word embeddings were utilized to represent the text data. The CNN model architecture consisted of convolutional and max-pooling layers to extract features from the text. VADER sentiment analysis was integrated to enhance sentiment classification.
The model was trained, evaluated using various metrics, and visualized using a confusion matrix. Additionally, we conducted sentiment analysis on YouTube comments using the trained model. Finally, a Streamlit application was developed for interactive sentiment prediction.
The Classification report is a summary of the performance of a classification model, presenting metrics such as precision, recall, F1-score, and support for each class. It provides a detailed evaluation of how well the model performs in classifying instances from different classes.
The results of the text classification model for sentiment analysis, using the given dataset, are as follows:
Overall, the model demonstrates strong performance in classifying sentiment in the given dataset. The weighted average of precision, recall, and F1-score is also high, indicating a balanced performance across all sentiment classes.
These results can be discussed as evidence of the effectiveness of the proposed CNN model for sentiment analysis. Additionally, the macro average and weighted average scores highlight the model's overall performance in handling imbalanced classes.
In conclusion, our research paper presented a comprehensive methodology for text classification using a Convolutional Neural Network (CNN) with VADER sentiment analysis. We utilized a Twitter dataset of 1.6 million tweets and demonstrated the effectiveness of our approach in accurately classifying sentiment. The integration of VADER enhanced the sentiment analysis process by considering contextual valence and sentiment intensities. Our research has real-world implications and practical applications. Text classification can be immensely useful in various scenarios. For instance, in the context of social media monitoring, it enables organizations to analyze public sentiment towards their brand, products, or services. This information can guide decision-making, reputation management, and marketing strategies. In the field of customer support, text classification can automate the categorization of customer feedback, allowing for efficient analysis of customer sentiments and identification of potential issues. Furthermore, text classification can be applied to analyze sentiment in online reviews, helping businesses gain insights into customer satisfaction and make informed decisions for product improvements. It can also aid in monitoring news articles, identifying emerging trends or public opinion on specific topics. Our methodology\'s integration with YouTube comment sentiment analysis expands the scope of application to the realm of video content. Content creators can use it to assess audience sentiment and engagement, enabling them to refine their content strategy and improve viewer satisfaction. Overall, our research demonstrates the significance of text classification in extracting valuable insights from textual data, enhancing decision-making processes, and improving customer experiences. The real-time applications of our methodology have the potential to positively impact various domains, ranging from social media analytics to customer support and content creation.
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Copyright © 2023 Prof. Sachin Sambhaji Patil, Anthon Rodrigues, Rahul Telangi, Vishwajeet Chavan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.