Recent advances in language technology, especially in sentiment analysis , promise to (partially) automate this task.

Sentiment analysis is often considered in the context of the following two tasks:

How can technology developed for sentiment analysis be applied to media analysis?

Much work has been done in sentiment analysis .

We discuss related work in four parts: sentiment analysis in general, domain- and target-specific sentiment analysis , product review mining and sentiment retrieval.

2.1 Sentiment analysis

Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierarchical relationships of products attributes is not fully utilized.

While this paper is mainly on sentiment analysis on reviews of one product, our proposed HL-SOT approach is easily generalized to labeling a mix of reviews of more than one products.

Faced with this problem, research works, e.g., (Hu and Liu, 2004; Liu et al., 2005; Lu et al., 2009), of sentiment analysis on product reviews were proposed and have become a popular research topic at the crossroads of information retrieval and computational linguistics.

Carrying out sentiment analysis on product reviews is not a trivial task.

We believe that labeling existing product reviews with attributes and corresponding sentiment forms an effective training resource to perform sentiment analysis .

The translation of sentiment information is a task from which sentiment analysis systems can benefit.

The automatic translation of this information could be beneficial for sentiment analysis in other languages.

Another important problem in sentiment analysis is the treatment of ambiguity.

Sentiment analysis is an important topic in computational linguistics that is of theoretical interest but also implies many real-world applications.

Usually, two aspects are of importance in sentiment analysis .

Work on sentiment analysis most often covers resources or analysis methods in a single language, usually English.

(2008) use machine translation for multilingual sentiment analysis .

Although unsupervised methods for the design of sentiment analysis systems exist, any approach can benefit from using resources that have been established in other languages.

For future work, we aim extend this work to constructing a multilingual sentiment analysis system and evaluate it with multilingual datasets such as product reviews collected from different countries.

There are multilingual subjectivity analysis systems available that have been built to monitor and analyze various concerns and opinions on the Internet; among the better known are OASYS from the University of Maryland that analyzes opinions on topics from news article searches in multiple languages (Cesarano et al., 2007)1 and TextMap, an entity search engine developed by Stony Brook University for sentiment analysis along with other functionalities (Bautin et al., 2008).2 Though these systems currently rely on English analysis tools and a machine translation (MT) technology to

Given sentiment analysis systems in different languages, there are many situations when the analysis outcomes need to be multilanguage-comparable.

To overcome the shortcomings of available resources and to take advantage of ensemble systems, Wan (2008) and Wan (2009) explored methods for developing a hybrid system for Chinese using English and Chinese sentiment analyzers .