@article{3811,
  author = {Shilpa Patil, V Lokesha},
  title = {Live Twitter Sentiment Analysis Using Streamlit Framework},
  journal = {Journal of Information & Systems Management},
  year = {2023},
  volume = {13},
  number = {3},
  doi = {https://doi.org/10.6025/jism/2023/13/3/78-89},
  url = {https://www.dline.info/jism/fulltext/v13n3/jismv13n3_3.pdf},
  abstract = {Sentiment analysis as per the textual description suggests that, it provides the sentiments or emotions on any form of given data in real life. As the social media information, now-a-days is flooded with various kinds of data from Facebook, Instagram, Twitter, and Whatsapp and so on. It is the need to handle data intelligently and classify as malicious and genuine data. The resultant growth in the area of social media entices tremendous challenges to the researchers in the field of social media analytics and deep learning. This paper contributes a user-friendly web application on ‘sentiment analysis of live twitter data’ using the keyword or handle, built on TextBlob library available in Python and Streamlit framework. The input data on the web application is firstly preprocessed with data cleaning, feature extraction and unstructured dataset view is filtered. Pre-processed data is further analyzed to collect the sentiments from a given twitter posts and predict into three classes viz positive, negative and neutral. The results presented in the work highlights the analysis of sentiments extracted from live tweets using keyword “Russian Ukraine World War” and classify the opinions as true or false tweets with positive, negative or neutral opinion by the use of Cat Boost Classifier.},
}