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In this paper, we briefly describe several experimental methods to solve MOAT at NTCIR-7. In the subtask of opinionated sentence detection, two methods aiming to extract the context information of each sentence are proposed. Maximum Entropy model is used to predict the polarity class. A rule-based pattern matching scheme is devised to find topic-relevant(More)
The amount of labeled sentiment data in En-glish is much larger than that in other languages. Such a disproportion arouse interest in cross-lingual sentiment classification, which aims to conduct sentiment classification in the target language (e.g. Chinese) using labeled data in the source language (e.g. English). Most existing work relies on machine(More)
Microblogging services, such as Twitter, have become popular channels for people to express their opinions towards a broad range of topics. Twitter generates a huge volume of instant messages (i.e. tweets) carrying users' sentiments and attitudes every minute, which both necessitates automatic opinion summarization and poses great challenges to the(More)
For sentiment analysis, lexicons play an important role in many related tasks. In this paper, aiming to build Chinese emotion lexicons for public use, we adopted a graph-based algorithm which ranks words according to a few seed emotion words. The ranking algorithm exploits the similarity between words, and uses multiple similarity metrics which can be(More)
Abbreviation is a common linguistic phenomenon with wide popularity and high rate of growth. Correctly linking full forms to their abbreviations will be helpful in many applications. For example, it can improve the recall of information retrieval systems. An intuition to solve this is to build an abbreviation dictionary in advance. This paper investigates(More)
In this paper, we propose a simple yet efective approach to automatically building sentiment lexicons from English sentiment lexicons using publicly available online machine translation services. The method does not rely on any semantic resources or bilingual dictionaries, and can be applied to many languages. We propose to overcome the low coverage problem(More)
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