Using support vector machine ensembles for target audience classification on Twitter
The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twi...
Saved in:
Main Authors: | LO, Siaw Ling, CHIONG, Raymond, CORNFORTH, David |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2015
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5906 https://ink.library.smu.edu.sg/context/sis_research/article/6904/viewcontent/LoSiawLing_2015_Using_SVM_ensembles_for_target_audience_classification_on_Twitter.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Effects of training datasets on both the extreme learning machine and support vector machine for target audience identification on twitter
by: LO, Siaw Ling, et al.
Published: (2014) -
Ranking of high-value social audiences on Twitter
by: LO, Siaw Ling, et al.
Published: (2016) -
Use of a high-value social audience index for target audience identification on Twitter
by: LO, Siaw Ling, et al.
Published: (2015) -
Identifying the high-value social audience from Twitter through text-mining methods
by: LO, Siaw Ling, et al.
Published: (2014) -
Multilingual sentiment analysis : From formal to informal and scarce resource languages
by: LO, Siaw Ling, et al.
Published: (2017)