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An International Multidisciplinary e-Journal
Opened Access & Peer Reviewed

SAI : 2016SCIA6DFEXXXX, SCIAID : 20166DFE ,  e-ISJN- A4372-3068

ISSN: 2456-0146
Impact Factor-3.071(IIJIF)
IF - 3.2 
Impact Factor - 5.936 (SJIF)

Application of Text Classification and Clustering of Twitter Data for Business Analytics



Sandhya Singh, Anamika Tiwari

In the ongoing years, interpersonal organization in business are increasing tremendous prevalence since it helps in the business development. Organizations become more acquainted with about their customers feelings of their item in market which encourages them to comprehend the market better and as needs be, they make their market and business technique. There is a colossal information accessible on interpersonal organization site like Twitter, Facebook and so on. Utilizing Machine Learning devices and strategies this information can be prepared into applicable informational collection so as to decide the example and patterns to increase significant bits of knowledge. This paper chosen a famous nourishment brand to assess a given stream of client remarks on Twitter. A few measurements were utilized to arrange and bunch information which was utilized for investigation. A Twitter API is utilized to gather information and channel it into a Binary tree classifier which will find the extremity of the tweets, regardless of whether positive or negative. A k-implies bunching strategy is utilized to aggregate comparable words so an important esteem can be given to the business. This paper endeavours to examine the specialized and business point of view of content order of Twitter information and features the future open doors this rising field.

Twitter, Sentiment Analysis, Decision Tree, k-means, Social media.