Building a Sustainable Social Feedback Loop: A Machine Intelligence Approach for Twitter Opinion Mining

Abstract

This paper presents a sustainable machine intelligence approach for Twitter opinion mining, focusing on building a socially responsible feedback loop. We propose a methodology that combines advanced machine learning algorithms with eco-conscious practices to extract sentiment-related insights from Twitter data while minimizing environmental impact. The preprocessing steps involve removing special characters, tokenization, stop word removal, handling user handles and URLs, and lemmatization or stemming. Sentiment classification is performed using the Extra Tree Classifier, an ensemble learning algorithm that incorporates random feature selection and bagging techniques. Experimental results demonstrate the effectiveness of our approach in accurately classifying tweets into positive, negative, and neutral sentiment categories. The visualizations of class distribution, number of tokens per tweet, and word clouds provide further insights into the sentiment landscape on Twitter. Our research contributes to the development of sustainable and inclusive approaches for Twitter opinion mining, ensuring minimal environmental impact while capturing valuable sentimental information.