Microblogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. However, this short length coupled with their noisy nature can pose difficulties for standard machine learning document representations. In this work we examine the hypothesis that it is easier to classify the sentiment in these short form documents than in longer form documents. Surprisingly, we find classifying sentiment in microblogs easier than in blogs and make a number of observations pertaining to the challenge of supervised learning for sentiment analysis in microblogs.
Although many researchers point out that the properties of short and noisy present new challenges to the sentiment analysis for microblog message, Bermingham and Smeaton REF find classifying sentiment in microblogs easier than in blogs.
Classifying sentiment in microblogs: is brevity an advantage?
CIKM '10
Computer Science