We now have a mountain of data due to the constant flow of information from various social media sites. It is crucial to digest data and extract emotions or important elements from it. An approach that may help with this is sentiment analysis. There has to be an English-like system that can decipher regional languages like Hindi for sentiment analysis. Machine translation is one of various emotion identification methods; nonetheless, it incurs the cost of translating across languages. This research presents a Deep Belief Network–based method for sentiment analysis of Hindi data. When it comes to Hindi data, this neural network model outperforms the machine translation method. An improvement in performance may be achieved by combining sentiment analysis with deep learning [4]. This sentiment categorization is best handled by a deep belief network, one of many deep learning neural network models [5]. To categorise Hindi reviews as either favourable, bad, or neutral, this s

Because it contains all sorts of information, including breaking news, a daily newspaper is an essential component of every human being's daily routine. Nowadays, social media is the most important thing for any generation. Everything is more accessible, less priced, and at your fingertips than ever before. The influence of social media has made modern living more easier [1]. Anyone, at any moment, may use social media as they see fit. The use of social media is ubiquitous in modern society. Social media connects you to the globe and keeps every generation informed about what's occurring. Social media allows individuals to have an impact. Twitter and Facebook are the most well-known social media platforms among different generations, but there are many others. In the run-up to an election, people tend to dwell on any news that mentions a certain candidate or political party. Commenters on social media platforms express their views and opinions on the upcoming elections

In the age of big data, social media has emerged as a prime example of a representative and relevant data source. The rapid expansion of the Internet has given rise to a new breed of individuals—or maybe a whole new social order—that is enthralled by the prospect of connecting with others via shared experiences and information. Facebook, Instagram, Google, Tumblr, Flickr, Twitter, and LinkedIn are just a few of the many prominent online programmes and websites that contribute to social media data. This data has expanded to several domains, including e-commerce, e-business, e-tourism, hobbies, friendship, education, health, and day-to-day employment. New and intriguing problems have emerged in fields such as social media and networks as a result of the merging of big data technology with conventional machine learning algorithms. Issues with data processing, storage, and representation, as well as its usage for pattern mining, user behaviour analysis, data visualisation,