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Bifet, Albert, and Eibe Frank. (2010) Sentiment Knowledge Discovery in Twitter Streaming Data. Proceedings of 13th International Conference on Discovery Science, Canberra, 6-8 October 2010, 1-15.

has been cited by the following article:

• TITLE: Sentiment Analysis on the Social Networks Using Stream Algorithms

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

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

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.2 No.2, May 30, 2014

  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.