Real Time Sentimental Analysis of Twitter using R language and Big Data tool RHIPE

Real Time Sentimental Analysis of Twitter using R language and Big Data tool RHIPE

Sentimental Analysis is one of the major germane to the area for data science, data analyst, researches and the academic people to imprint the attitude, views, and opinion towards any peculiar product, organization, celebrity, political etc. The basic idea is to arbitrate whether they are scrutinizing positively or negatively. A number of social networking sites like Facebook, Twitter, LinkedIn and e-commerce sites like Amazon, eBay, and Snapdeal are a big source of assessment of text data. So, there is a prerequisite to analyze and comprehend the data.

This research paper pronounces the sentimental analysis of twitter which is the microblogging and an extraordinary social communication medium for the users. Data used in this research is collected from twitter server and has implemented the sentimental analysis by using 500 posts and 2000 posts in which the response of people is scrutinized towards our Prime Minister Modi or Delhi Chief Minister Arvind Kejriwal. RStudio tool is made in use because of its flexibility towards the working and writing with R scripts. A big data tool RHIPE is also used. It is a combination of the R language and HIPE tool. It is applicable when the size of data is huge and the same cannot be analyzed by using the RStudio environment.

In this research, the dataset has been taken from the twitter server through the Application programming interface. Through APIs, firstly the 500 real-time posts were fetched and apply the R language algorithm on it. After that 2000 posts have been fetched. It has been a large dataset, therefore, data is stored into the HDFS (Hadoop Distributed File System) file system and by using RHIPE and R language analysis is accomplished. This paper tackles with the fundamental problem of sentimental analysis of twitter namely the sentiment polarity categorization that divides sentiments into three categories such as the positive, negative and neutral.

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