Introduction:
The proposed project is a Data analyzing application which can help us to assess public’s opinion on trending topics and issues discussed on Twitter, one the largest social networks. This is achieved with Sentimental analysis powered by Naïve Bayes Theorem, which enables the application to categorize the set of data (Tweets in our case) into one category or the other (positive impact or negative impact in our case). After categorizing the tweets, the result is outputted in form of graphs for simple understanding. The project can also be used to evaluate the popularity of an individual, or the trending things in a specific domain. This is done with gathering all tweets from a given dataset and scanning them. Then, this is used to produce a WordCloud, which is a cloud of words made up of most recurring words. In WordCloud the most-frequented word will constitute the majority of the cloud, surrounded by the words with lesser frequency.
Aim:
This project aims to develop an application which will help an individual to understand the notion of the public in trending topics and issues. This can be helpful to people in the fields of Cinema, Politics, etc. to see themselves from the public’s point of view and rectify flaws, if any.
Objectives:
The main objective of developing an application which will help you to extract public pulse and opinion on a particular trending issue. This application can also evaluate the popularity of an individual or topic. To provide graphical outputs for simple understanding.