We use sentiment analysis techniques to identify opinion expressions.

2.1 Sentiment Analysis

Our work is related to a huge body of work on sentiment analysis .

A very detailed survey that covers techniques and approaches in sentiment analysis and opinion mining could be found in (Pang and Lee, 2008).

In the past few years, opinion mining and sentiment analysis have attracted much attention in Natural Language Processing (NLP) and Information Retrieval (IR) (Pang and Lee, 2008; Liu, 2010).

In summary, we have three main contributions: 1) We give a systematic study on cross-domain sentiment analysis in word level.

There are also lots of studies for cross-domain sentiment analysis (Blitzer et al., 2007; Tan et al., 2007; Li et al., 2009; Pan et al., 2010; Bollegala et al., 2011; He et al., 2011; Glorot et al., 2011).

In the future, we will work on leveraging parallel sentences and word alignments for other tasks in sentiment analysis , such as building multilingual sentiment lexicons.

Sentiment Analysis (also known as opinion mining), which aims to extract the sentiment information from text, has attracted extensive attention in recent years.

Sentiment classification, the task of determining the sentiment orientation (positive, negative or neutral) of text, has been the most extensively studied task in sentiment analysis .

Aspect extraction is a central problem in sentiment analysis .

Aspect-based sentiment analysis is one of the main frameworks for sentiment analysis (Hu and Liu, 2004; Pang and Lee, 2008; Liu, 2012).

Our models are related to topic models in general (Blei et al., 2003) and joint models of aspects and sentiments in sentiment analysis in specific (e.g., Zhao et al., 2010).