Sentiment Analysis using Rapid Miner
Aravindasamy. R1, C. Nalini2, Sangeetha. S3, S. Theivasigamani4
1Aravindasamy R, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
2C Nalini, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
3Sangeetha.S, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
4S. Theivasigamani, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
Manuscript received on 10 July 2019 | Revised Manuscript received on 22 July 2019 | Manuscript Published on 23 August 2019 | PP: 1589-1594 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I33320789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3332.0789S319
Open Access | Editorial and Publishing 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 (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now a day the data grows day by day so data mining replaced by big data. Under data mining, Text mining is one of the processes of deriving structured or quality information or data from text document. It helps to business for finding valuable knowledge. Sentiment analysis is one of the applications in text mining. In sentiment analysis, determine the emotional tone under the text. It is the major task of natural language processing. The objective of this paper to categorize the document in sentence level and review level, and classification techniques applied on the dataset (electronic product data). There is an ensemble number of classification techniques applied on the dataset. Then compare each techniques, based on various parameters and find out which one is best. According to that give better suggestions to the company for improving the product.
Keywords: Analysis, Big data, information
Scope of the Article: Big Data Networking