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.