G. Preslav Nakov, Zornitsa Kozareva, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, Theresa Wilson, “SemEval-2013 Task 2: Sentiment Analysis in Twitter,” Second Joint Conference on Lexical and Computational Semantics (*SEM), Seventh International Workshop on Semantic, Atlanta, 14-15 June 2013, Vol. 2, pp. 312-320.

has been cited by the following article:

• TITLE: Twitter Sentiment in Data Streams with Perceptron

  AUTHORS: Nathan Aston, Jacob Liddle, Wei Hu

  KEYWORDS: Sentiment Analysis; Twitter; Grams; Perceptron; Data Stream

  JOURNAL NAME: Journal of Computer and Communications, Vol.2 No.3, February 14, 2014

  ABSTRACT: With the huge increase in popularity of Twitter in recent years, the ability to draw information regarding public sentiment from Twitter data has become an area of immense interest. Numerous methods of determining the sentiment of tweets, both in general and in regard to a specific topic, have been developed, however most of these functions are in a batch learning environment where instances may be passed over multiple times. Since Twitter data in real world situations are far similar to a stream environment, we proposed several algorithms which classify the sentiment of tweets in a data stream. We were able to determine whether a tweet was subjective or objective with an error rate as low as 0.24 and an F-score as high as 0.85. For the determination of positive or negative sentiment in subjective tweets, an error rate as low as 0.23 and an F-score as high as 0.78 were achieved.