Sentiment Analysis- A Tool for Data Mining in Big Data Analytics
Girisha Moorjani1, Lipsa Sadath2

1Girisha Moorjani, School of Management Studies, Amity University, Dubai, UAE
2Lipsa Sadath, Software Engineering, Amity University, Dubai, UAE.

Manuscript received on 26 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2125-2131 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8005078919/19©BEIESP | DOI: 10.35940/ijitee.I8005.078919

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: With the current business environment and rapid changes in technology, the amount of data produced is increasing as each day passes. This huge collection of data is what can make or break such institutions, so it is vital for such a sector to efficiently utilize the data generated. Effective tools and analyses are required to make sure that this data is comprehended and organized in such a manner that it can be used for the tasks at hand. The challenge faced here is knowing how to extract and use the data to the benefit of the business world. The objective of understanding the underlying emotion displayed in each opinion that is voiced out is a huge exercise. Through this paper an attempt has been made to understand how the gap between consumers and providers can be bridged by analyzing secondary data through Sentiment Analysis tool. This research proposes a framework CSA (Continuous Sentiment Analysis) to repeatedly analyze the sentiments from customers highlighting the purpose of one such attempt to capture the tone of the message. This method of “Sentiment Analysis”- a fairly new field uses Natural Language Processing (NLP) in order to give meaning to the abundant data available at hand.
Keywords: Business Analysis, CSA, Data Analysis, Data Mining, Sentiment Analysis, NLP.

Scope of the Article: Data Mining