PollVue: Public Opinion Lens Using Python and Streamlit

Forecasting the election results is a tedious task that tends to be stochastic. Analyzing the public sentiment is erratic. “PollVue” employs Python, NLTK, Firebase, Streamlit, and Scikit-learn. The exceptional features include Multimodal data fusion gathers information from multiple sources; Geospatial sentiment mapping identifies regional sentiment variations; and Temporal analysis traces varying public sentiments over some time. PollVue's unique selling proposition involves actionable insights that incorporate campaigning strategies, analyzing their winning possibilities, and choosing influencers based on location. Thereby PollVue strives to intensify the predictive capabilities for more accurate analysis.