Sentiment analysis usually refers to the analysis of human-generated content via a polarity
filter. Affective computing deals with the exact emotions conveyed through information …

Text has traditionally been used to train automated classifiers for a multitude of purposes,
such as: classification, topic modelling and sentiment analysis. State-of-the-art LSTM …

For short text classification, insufficient labeled data, data sparsity, and imbalanced
classification have become three major challenges. For this, we proposed multiple weak …

The idea of the wisdom of the crowd is that integrating multiple estimates of a group of
individuals provides an outcome that is often better than most of the underlying estimates or …

Labelling, or annotation, is the process by which we assign labels to an item with regards to
a task. In some Artificial Intelligence problems, such as Computer Vision tasks, the goal is to …

In recent years, significant progress has been made in developing supervised Machine
Learning (ML) systems like Convolutional Neural Networks. However, it's crucial to …

The extensive use of online social media has highlighted the importance of privacy in the
digital space. As more scientists analyse the data created in these platforms, privacy …

The extensive use of online social media has highlighted the importance of privacy in the
digital space. As more scientists analyse the data created in these platforms, privacy …

Market information plays an important role in the functioning of markets. The recurrence of
market information asymmetry and associated market failure has been a challenge in …

In crowdsourcing, requesters are companies that require external workers to execute
specific tasks, whereas a platform acts as a mediator to match and allocate the tasks to …