Chinese Textual Sentiment Analysis: Datasets, Resources and Tools

AbstractThe 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 gi