Text Analysis On Twitter Data Using LSA and LDA

The news ecosystem has changed in the modern years from outdated print media to social media sites. Because social media platforms enable us to absorb news much more quickly and with less restrictive editing, fake news is disseminated at an astonishing rate and scale. More and increasingly individuals are using social media as the world becomes more digital since it makes connecting with others relatively simple. But false information is misguiding people. Although fake news is simple to propagate, its effects can be disastrous. Bogus news has frequently resulted in uncontrolled circumstances that killed numerous people. People with limited education might quickly become acclimated to bogus news. Instead of checking the accuracy of the information, they accept what is provided to them. This can be overcome by using Text mining, Statics concepts which can detect fake news more precisely when compared to several machine learning methods. This proposed system is on the analysis of fake news and detection of hatred news using t-distributed Stochastic Neighbor Embedding(t-SNE) to check dimensionality reduction and topic modeling using Latent Dirichlet Allocation(LDA) and data pre-processing using Latent Semantic Analysis (LSA).