Back to Search
Start Over
WITHDRAWN: Clustering Yelp’s sentiment data through various approaches and estimating the error rate
- Source :
- Materials Today: Proceedings.
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Yelp is a platform that provides free access to use the data for educational and research purposes. The sentiment data which is used for this empirical research consists of two attributes namely sentiment text and sentiment count. The sentiment text contains the sentences and the sentiment counts contain their binary sentiment polarity as 0 and 1. 1 means the sentence is positive and 0 means negative. After performing clustering through various approaches like k-means, canopy, cobweb, etc. it is found that none of the approaches gave the exact results. Through this empirical research, we are putting forward the most appropriate approach among various approaches which gave better results for Yelp’s sentiment data and similar types of data. This research is concluded by recommending the clustering approach which is having less error rate and execution time as compared to others.
- Subjects :
- 010302 applied physics
Computer science
Polarity (physics)
Binary number
Word error rate
02 engineering and technology
021001 nanoscience & nanotechnology
computer.software_genre
01 natural sciences
Data type
Exact results
Empirical research
0103 physical sciences
Data mining
0210 nano-technology
Cluster analysis
computer
Sentence
Subjects
Details
- ISSN :
- 22147853
- Database :
- OpenAIRE
- Journal :
- Materials Today: Proceedings
- Accession number :
- edsair.doi...........1c449b2db7a35d61e823388b223d7bb4
- Full Text :
- https://doi.org/10.1016/j.matpr.2020.09.346