• Vaishnavi S. Manthalkar
• Sudhakar R. Barbade

This Twitter (Now X) is a major source of modern-day connectedness to social, political and entertainment waves of events and reactions from the global community. It also is a unique source of personal opinions and interests which in the form of digital information acts as an opportunity to extend the understanding of impact of freedom of digital speech. The aim of this work is to develop capacity to work with live data from this source to analyze the sentiment of people’s reactions and incorporating the subtle and at times strong or complex change in sentiment by inclusion of emojis. This work was conducted in India and in recent times Twitter has gained a wider user base in the country which is a host to people who react in multilingual phrases and tweets. This work acknowledges the popular languages in use and segregate content based on their content similarity using K-means clustering. The use of unsupervised learning led to an average silhouette score of 0.78 on a sample live data sourced from Twitter which indicated that the quality of the clusters is good and clusters are clearly distinguished. The paper also talks about challenges and opportunities to take this work forward

Sentiment analysis, clustering, multi-lingual data analysis, silhouette analysis, emoji interpretation, elbow curve.

"Sentiment analysis and clustering of trending tweets from Twitter including emojis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.g95-g101, December-2023, Available :http://www.jetir.org/papers/JETIR2312615.pdf

"Sentiment analysis and clustering of trending tweets from Twitter including emojis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. ppg95-g101, December-2023, Available at : http://www.jetir.org/papers/JETIR2312615.pdf