Real-time Sentiment Analysis on Social Networks using Meta-model and Machine Learning Techniques

Abstract

Sentiment analysis is a critical task in social media analysis, enabling the understanding of user attitudes and opinions towards various topics. This paper proposes a real- time sentiment analysis system for social networks that utilizes a meta-model and machine learning techniques to accurately classify user sentiment. The proposed system integrates textual and visual data from social media posts to improve sentiment classification accuracy. The methodology includes data collection and preprocessing, feature extraction and selection, and the proposed meta-model for sentiment analysis. The system utilizes several machine learning techniques, including SVM, CNN, and LSTM networks. We evaluated the proposed system on a large-scale dataset and compared its performance with several state- of-the-art methods. The evaluation metrics, including accuracy, precision, recall, and F1-score, showed that our proposed system outperforms existing methods. The proposed system’s ability to handle multimodal data and achieve high accuracy in real- time makes it suitable for various applications, including social media monitoring and marketing analysis. The proposed system’s limitations provide opportunities for further research, such as developing more efficient algorithms and models that require less training data, and improving techniques for handling noisy and ambiguous data, such as sarcasm and irony. In conclusion, the proposed real-time sentiment analysis system using a meta-model and machine learning techniques provides a robust and efficient solution for sentiment analysis on social networks. The proposed system's performance and potential applications demonstrate its importance in the field of social media analysis.