Sentiment Analysis on the Social Networks Using Stream Algorithms

Nathan Aston; Timothy Munson; Jacob Liddle; Garrett Hartshaw; Dane Livingston; Wei Hu

doi:10.4236/jdaip.2014.22008

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Journal of Data Analysis and Information... > Vol.2 No.2, May 2014

Sentiment Analysis on the Social Networks Using Stream Algorithms ()

Nathan Aston, Timothy Munson, Jacob Liddle, Garrett Hartshaw, Dane Livingston, Wei Hu

Department of Computer Science, Houghton College, Houghton, USA.

DOI: 10.4236/jdaip.2014.22008 PDF HTML XML 3,852 Downloads 6,275 Views Citations

Abstract

The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.

Keywords

Modified Balanced Winnow, Sentiment Analysis, Twitter, Online Social Networks, Feature Selection, Data Streams

Share and Cite:

Aston, N. , Munson, T. , Liddle, J. , Hartshaw, G. , Livingston, D. and Hu, W. (2014) Sentiment Analysis on the Social Networks Using Stream Algorithms. Journal of Data Analysis and Information Processing, 2, 60-66. doi: 10.4236/jdaip.2014.22008.

Conflicts of Interest

The authors declare no conflicts of interest.

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