AI-Powered Sentiment Analysis of Social Media: Trends, Challenges, and Insights

6 May

AI-Powered Sentiment Analysis of Social Media: Trends, Challenges, and Insights

Authors- Koniki Ganesh, Y Sai Pallavi, Dr.Swapna M, P Srinivas, B Saikumar

Abstract-– The proliferation of social media sites has generated an unprecedented amount of user-created content, rendering sentiment analysis a vital tool to gauge public opinion, brand reputation, and social trends. Methods of sentiment analysis like supervised and unsupervised learning, as well as lexicon-based models, and hybrid models have been proposed to categorize social media posts as positive, negative, or neutral. Sophisticated AI techniques like Random Forest, Decision Tree, and XGBoost quite highly boost sentiment classification accuracy. In spite of all these developments, difficulty exists in situations like sarcasm detection, multilingual text analysis, and contextual ambiguity, making sentiment analysis in real-time difficult with changing slang and noisy data. The ubiquitous use of sentiment analysis in marketing, politics, healthcare, finance, and crisis management underscores its increasing significance. Companies utilize it for learning consumer sentiments, governments make use of it to analyze policy issues, and academics use it for research purposes. The ongoing research in the area of deep learning, explainable AI, and cross-lingual analysis will further enrich techniques for sentiment analysis so that it can monitor and analyze social media even better. Real-time sentiment monitoring and more sophisticated analytical techniques will improve decision-making, customer interaction, and trend identification, enabling organizations to lead in a more digital age. With the development of sentiment analysis, it will prove to be an integral tool for companies, researchers, and policymakers alike, offering meaningful insights into public opinion and allowing data-driven approaches to achieve improved results in various fields.

DOI: /10.61463/ijset.vol.13.issue2.393