A semi-supervised approach of short text topic modeling using embedded fuzzy clustering for Twitter hashtag recommendation

Abstract

AbstractSocial media stands as a crucial information source across various real-world challenges. Platforms like Twitter, extensively used by news outlets for real-time updates, categorize news via hashtags. These hashtags act as pivotal meta-information for linking tweets to underlying themes, yet many tweets lack them, posing challenges in topic searches. Our contribution addresses this by introducing a novel heuristic for hashtag recommendation. Extracting 20 thousand tweets, 5000 each from distinct categories health, sports, politics, and technology we applied fundamental data cleaning and tokenization techniques. Leveraging Word2Vec, we vectorized tokens, capturing nuanced semantic meanings and mitigating data sparsity issues. The proposed heuristic creates clusters of different topic by combining these embedded features and idea of fuzzy C-Means technique. Develop a rule-based approach that combines both supervised and unsupervised methods to label clusters, indicating their respective topic. The experimental outcomes shows that our proposed techniques achieve better performance metrics in precision, recall, and F1-score compared to specific baseline models.