ABSTRACT
In this research paper, the authors have focused on predicting indicators of recession conditions using public opinion-based platforms and newspaper resources. A three-staged data science pipeline is created which involves data collection from various platforms, data filtering, and data cleaning process. In the last stage of the pipeline, we analyzed the data and generated insights from it. In the data collection process, we have collected real-time based data from public-opinionated social media platforms like Twitter and Reddit. Additionally, New York Times articles have been collected for the purpose of a newspaper-based platform We have performed natural language processing (NLP) methods like keyword analysis, word-frequency analysis, and sentiment analysis to compare the change in the attributes of data over time. The results suggest that NLP techniques tools can be used to prove the short- and long-term indicators of recession conditions and inflation reasons across the globe on public-opinionated platforms and newspaper articles.
