The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their
sentiments are analysed and sentiments are evolved over time, is getting much attention …

Sentiment analysis on public opinion expressed in social networks, such as Twitter or
Facebook, has been developed into a wide range of applications, but there are still many …

Online crowdsourcing provides a scalable and inexpensive means to collect knowledge (eg
labels) about various types of data items (eg text, audio, video). However, it is also known to …

With the rapid development of e-commerce, online consumption has become a mainstream
form of consumption in recent years. Text sentiment analysis for a large number of customer …

Traditional truth inference algorithms take multiple source labels as input and infer true
labels for objects. Besides source labels, object features have been introduced in inference …

Aggregating crowd wisdom infers true labels for objects, from multiple noisy labels provided
by various sources. Besides labels from sources, side information such as object features is …

In recent years, crowdsourcing has gained tremendous attention in the machine learning
community due to the increasing demand for labeled data. However, the labels collected by …

Crowdsourcing has become an economical and efficient way to obtain data, but the data
obtained by crowdsourcing is often noisy. Due to concerns about human errors in …

Microtask crowdsourcing has become an appealing approach to collecting large-scale high-
quality labeled data across a wide range of domains. As the crowd workers may be …

The the task of Aspect-based opinion mining (AbOM) is an emeraging research area, where
aspects are mined, the corresponding opinion are scrutinized and sentiments are …