Collective Intelligence Based Opinion Mining of Social Data

Authors(4) :-Hemavati, Lakshmi K V, Punyashree K, Sindhu Priya M A

The most critical factors in todayís world in formulating our views and influencing the success of a brand, product or service are opinions and reviews that are accessible to us. With the advent and growth of social media, stakeholders often express their opinions on popular social media, namely twitter where the data is extremely informative and it presents a challenge for analysis because of its immense volume, disorganized nature, difficulty in verifying the data authenticity and data security. In this system, the tweets are fetched from Twitter via Twitter API which uses open standard for authorization OAuth which also provides security for the users. This massive volume of data fetched are then stored into Hadoop Distributed File System (HDFS). The system can also take the input from local Excel file for processing. Efficient pre-processing techniques are applied to get sentiment for the userís opinion. The results are depicted in the form of graphs.

Authors and Affiliations

Hemavati
Assistant Professor, Department of ISE, SIT, Tumakuru, Karnataka, India
Lakshmi K V
Department of ISE, SIT, Tumakuru, Karnataka, India
Punyashree K
Department of ISE, SIT, Tumakuru, Karnataka, India
Sindhu Priya M A
Department of ISE, SIT, Tumakuru, Karnataka, India

Peltier Device, Thermoelectric Generator, Microcontroller (ATMEGA16), Temperature Sensor (LM35), Bluetooth Module

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 556-561
Manuscript Number : CSEIT1835125
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Hemavati, Lakshmi K V, Punyashree K, Sindhu Priya M A, "Collective Intelligence Based Opinion Mining of Social Data", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.556-561, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT1835125

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