Transfer Learning for Multiple-Domain Sentiment Analysis

Yasuhisa Yoshida (1051124)

Sentiment analysis is the task of determining the attitude (positive or negative) of authors expressed in documents. While the polarity of words in the documents is informative for this task, polarity of some words cannot be determined without domain knowledge. Detecting word polarity thus poses a challenge for multiple-domain sentiment analysis. Previous approaches tackle this problem with transfer learning techniques, but they cannot handle multiple source domains and multiple target domains. This thesis proposes a novel Bayesian probabilistic model to handle multiple source and multiple target domains. We derive an efficient algorithm using Gibbs sampling for inferring the parameters of the model, from both labeled and unlabeled texts. Using real data, we demonstrate the effectiveness of our model in a document polarity classification task compared with a method not considering the differences between domains. Moreover our method can also tell whether the polarity of a word is domain-dependent or domain-independent. This feature allows us to construct a word polarity dictionary for each domain.