@InProceedings{ku-chen:2016:COLINGTuto,
  author    = {Ku, Lun-Wei  and  Chen, Wei-Fan},
  title     = {Chinese Textual Sentiment Analysis: Datasets, Resources and Tools},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {5--8},
  abstract  = {The rapid accumulation of data in social media (in million and billion scales)
	has imposed great challenges in information extraction, knowledge discovery,
	and data mining, and texts bearing sentiment and opinions are one of the major
	categories of user generated data in social media. Sentiment analysis is the
	main technology to quickly capture what people think from these text data, and
	is a research direction with immediate practical value in ‘big data’ era.
	Learning such techniques will allow data miners to perform advanced mining
	tasks considering real sentiment and opinions expressed by users in additional
	to the statistics calculated from the physical actions (such as viewing or
	purchasing records) user perform, which facilitates the development of
	real-world applications. However, the situation that most tools are limited to
	the English language might stop academic or industrial people from doing
	research or products which cover a wider scope of data, retrieving information
	from people who speak different languages, or developing applications for
	worldwide users. 
	More specifically, sentiment analysis determines the polarities and strength of
	the sentiment-bearing expressions, and it has been an important and attractive
	research area. In the past decade, resources and tools have been developed for
	sentiment analysis in order to provide subsequent vital applications, such as
	product reviews, reputation management, call center robots, automatic public
	survey, etc. However, most of these resources are for the English language.
	Being the key to the understanding of business and government issues, sentiment
	analysis resources and tools are required for other major languages, e.g.,
	Chinese. 
	In this tutorial, audience can learn the skills for retrieving sentiment from
	texts in another major language, Chinese, to overcome this obstacle. The goal
	of this tutorial is to introduce the proposed sentiment analysis technologies
	and datasets in the literature, and give the audience the opportunities to use
	resources and tools to process Chinese texts from the very basic preprocessing,
	i.e., word segmentation and part of speech tagging, to sentiment analysis,
	i.e., applying sentiment dictionaries and obtaining sentiment scores, through
	step-by-step instructions and a hand-on practice. The basic processing tools
	are from CKIP Participants can download these resources, use them and solve the
	problems they encounter in this tutorial.
	This tutorial will begin from some background knowledge of sentiment analysis,
	such as how sentiment are categorized, where to find available corpora and
	which models are commonly applied, especially for the Chinese language. Then a
	set of basic Chinese text processing tools for word segmentation, tagging and
	parsing will be introduced for the preparation of mining sentiment and
	opinions. After bringing the idea of how to pre-process the Chinese language to
	the audience, I will describe our work on compositional Chinese sentiment
	analysis from words to sentences, and an application on social media text
	(Facebook) as an example. All our involved and recently developed related
	resources, including Chinese Morphological Dataset, Augmented NTU Sentiment
	Dictionary (aug-NTUSD), E-hownet with sentiment information, Chinese Opinion
	Treebank, and the CopeOpi Sentiment Scorer, will also be introduced and
	distributed in this tutorial. The tutorial will end by a hands-on session of
	how to use these materials and tools to process Chinese sentiment.
	Content Details, Materials, and Program please refer to the tutorial URL:
	http://www.lunweiku.com/},
  url       = {http://aclweb.org/anthology/C16-3002}
}