Online social networks (OSNs) are part of daily life of human beings. Millions of users are
connected through online social networks. Due to very large number of users and huge …

When a recommender system suggests items to the end-users, it gives a certain exposure to
the providers behind the recommended items. Indeed, the system offers a possibility to the …

This paper presents a state-of-the-art approach for sentiment polarity classification. Our
approach relies on an ensemble of Bidirectional Long Short-Term Memory networks …

Student opinions for a course are important to educators and administrators, regardless of
the type of the course or the institution. Reading and manually analyzing open-ended …

Today, we are seeing an ever-increasing number of clinical notes that contain clinical
results, images, and textual descriptions of patient's health state. All these data can be …

Today, increasing numbers of people are interacting online and a lot of textual comments
are being produced due to the explosion of online communication. However, a paramount …

Accurate analysis of targeted sentiment in online course reviews helps in understanding
emotional changes of learners and improving the course quality. In this paper, we propose a …

Context-aware recommender systems (CARS) are a vital module of many corporate,
especially within the online commerce domain, where consumers are provided with …

Online platforms are nurturing social interactions, yet regrettably, they have also led to the
proliferation of antisocial behaviors such as cyberbullying, trolling, and hate speech on a …

Recommender systems are key tools to push items' consumption. Imbalances in the data
distribution can affect the exposure given to providers, thus affecting their experience in …