To combat this problem we first train the sentiment classifiers by assuming that pygm is equal for all the local topics, which effectively ignores the topic model.

The second problem is sentiment classification .

Sentiment classification is a well studied problem (Wiebe, 2000; Pang et a1., 2002; Tumey, 2002) and in many domains users explicitly

Therefore, the use of the aspect sentiment classifiers based only on the words assigned to the corresponding topics is problematic.

Most text-level sentiment classifiers use standard machine learning techniques to learn and select features from labeled corpora.

(2007) applied structural correspondence learning (Drezde et al., 2007) to the task of domain adaptation for sentiment classification of product reviews.

To our knowledge, the only work that describes the application of statistical classifiers (SVM) to sentence-level sentiment classification is (Gamon and Aue, 2005)1.