The lack of Chinese sentiment corpora limits the research progress on Chinese sentiment classification .

This paper focuses on the problem of cross-lingual sentiment classification, which leverages an available English corpus for Chinese sentiment classification by using the English corpus as training data.

Sentiment classification is the task of identifying the sentiment polarity of a given text.

In recent years, sentiment classification has drawn much attention in the NLP field and it has many useful applications, such as opinion mining and summarization (Liu et al., 2005; Ku et al., 2006; Titov and McDonald, 2008).

To date, a variety of corpus-based methods have been developed for sentiment classification .

Our experiments consist of an opinion retrieval task and a sentiment classification task.

(2002) to test various ML—based methods for sentiment classification .

We present the sentiment classification performances in Table 3.

(2002) presents empirical results indicating that using term presence over term frequency is more effective in a data-driven sentiment classification task.

In this section, we describe the characteristics of terms that are useful in sentiment analysis, and present our sentiment analysis model as part of an opinion retrieval system and an ML sentiment classifier .

To address this problem, we propose a semi-supervised approach to sentiment classification where we first mine the unambiguous reviews using spectral techniques and then exploit them to classify the ambiguous reviews via a novel combination of active learning, transductive learning, and ensemble learning.

First, none of the steps in our approach is designed specifically for sentiment classification .

For evaluation, we use five sentiment classification datasets, including the widely-used movie review dataset [MOV] (Pang et al., 2002) as well as four datasets that contain reviews of four different types of product from Amazon [books (BOO), DVDs (DVD), electronics (ELE), and kitchen appliances (KIT)] (Blitzer et al., 2007).

perimental results on five sentiment classification datasets demonstrate that our system can generate high-quality labeled data from unambiguous reviews, which, together with a small number of manually labeled reviews selected by the active learner, can be used to effectively classify ambiguous reviews in a discriminative fashion.

Section 2 gives an overview of spectral clustering, which will facilitate the presentation of our approach to unsupervised sentiment classification in Section 3.

Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand.

In particular, the use of SVMs in (Pang et al., 2002) initially sparked interest in using machine learning methods for sentiment classification .

A two-tier scheme (Pang and Lee, 2004) where sentences are first classified as subjective versus objective, and then applying the sentiment classifier on only the subjective sentences further improves performance.