Statistical Challenges in Social Media Data Analysis Sentiment Tracking and Beyond - Diaa, Nabaa Muhammad; Ahmed, Saba Sabah; Salman, Hayder Mahmood; Sajid, Wafaa Adnan (Journal of Ecohumanism, 2024)
Background: Social media has emerged as an important forum for public discourse, generating a large amount of data for sentiment analysis and other insights. However, the enormous and unstructured nature of social media data presents substantial statistical hurdles, which can affect the quality and reliability of results. Objective: The article aims to investigate and address the statistical issues that arise while analyzing social media data, emphasizing sentiment tracking. By identifying and addressing these problems, the study aims to improve the accuracy and reliability of sentiment analysis and broaden its uses. Methods: The article conducts a complete literature review to identify typical statistical problems in social media data analysis. These issues are addressed by developing and implementing advanced statistical approaches, such as natural language processing (NLP) and machine learning algorithms. Data from several social media platforms (over 1 million posts and comments) is collected and evaluated to test these strategies. Results: The findings reveal several significant obstacles, including lack of data (with over 60% of posts containing limited sentiment indicators), excessive dimensionality (with an average of 200 features per post), noise (30% of data classified as irrelevant), and social media data bias (found in 25% of posts). Advanced statistical approaches result in a 15% increase in sentiment classification accuracy and a 20% decrease in noise. The findings also indicate the possibility of applying these strategies to other social media data analysis areas. Conclusion: Addressing statistical issues in social media data analysis is critical for improving the accuracy and reliability of sentiment tracking. Advanced statistical techniques, particularly those based on NLP and machine learning, provide intriguing possibilities. Future research should focus on improving these algorithms and expanding their uses beyond sentiment analysis.