Automated classification of text sentiment-Reference-Cited by-同舟云学术

Automated classification of text sentiment

Published:2017 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the South African Institute of Computer Scientists and Information Technologists on - SAICSIT '17
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Author:

Dufourq Emmanuel¹,Bassett Bruce A.¹

Affiliation:

1. University of Cape Town, Cape Town, South Africa

Funder

National Research Foundation (NRF)

Publisher

ACM Press

Reference33 articles.

1. Ahmed Abbasi, Hsinchun Chen, and Arab Salem. 2008. Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums.ACM Transactions on Information Systems26, 3, Article 12 (June 2008), 34 pages.

2. G. Acampora and G. Cosma. 2014. A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews. InIntelligent Agents (IA), 2014 IEEE Symposium on.73--80.

3. Alias-i. 2008. LingPipe 4.1.0. http://alias-i.com/lingpipe. (2008).

4. Tobias Blickle and Lothar Thiele. 1996. A Comparison of Selection Schemes Used in Evolutionary Algorithms.Evolutionary Computation4, 4 (Dec. 1996), 361--394.

5. Jonnathan Carvalho, Adriana Prado, and Alexandre Plastino. 2014. A Statistical and Evolutionary Approach to Sentiment Analysis. InProceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 02 (WI-IAT '14).IEEE Computer Society, Washington, DC, USA, 110--117.

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