Most sentiment analysis studies address classification of a single source of data such as reviews or blog posts. However, the multitude of social media sources available for text analysis lends itself naturally to domain adaptation. In this study, we create a dataset spanning three social media sources ‐ blogs, reviews, and Twitter ‐ and a set of 37 common topics. We first examine sentiments expressed in these three sources while controlling for the change in topic. Then using this multidimensional data we show that when classifying documents in one source (a target source), models trained on other sources of data can be as good as or even better than those trained on the target data. That is, we show that models trained on some social media sources are generalizable to others. All source adaptation models we implement show reviews and Twitter to be the best sources of training data. It is especially useful to know that models trained on Twitter data are generalizable, since, unlike reviews, Twitter is more topically diverse.
['Yelena Mejova', 'Padmini Srinivasan']
Crossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter