Checking the Truthfulness of News Channels using NLP Techniques

Data analytics are difficult with unstructured data since it lacks pre-formatting, which makes up a large amount of information on social media. Research has looked into the potential of machine learning and natural language processing to interpret unstructured data and uncover significant information that could have gone unnoticed. We classify social media users using segmentation approaches on the basis of population, relationship status, and other criteria, allowing for the recognition of prominent people within particular categories. Behavior analysis assigns distinct roles to social media participants, such as users, recommenders, prospective users, and detractors, facilitating the tailoring of messages and reactions to address their concerns effectively. The study involves the accumulation of data from three news channels, data cleaning, and exploratory data analysis (EDA) to unearth patterns. Sentiment analysis is performed, and its results are compared for each of the news channels. Topic modeling is used to reveal different themes in the data. This study offers a solid basis for sentiment analysis and visualization, providing insightful information on the sentiment levels across various source kinds and features. It holds the potential to empower government agencies and businesses in harnessing the power of social media analytics for informed decision-making and improved communication strategies.