Social networks play a key role in providing a new approach to collecting information
regarding mobility and transportation services. To study this information, sentiment analysis …

Text from social media provides a set of challenges that can cause traditional NLP
approaches to fail. Informal language, spelling errors, abbreviations, and special characters …

The combination of the topic model and the semantic method can help to discover the
semantic distributions of topics and the changing characteristics of the semantic …

We present a feature vector formation technique for documents-Sparse Composite
Document Vector (SCDV)-which overcomes several shortcomings of the current …

This paper analyses the capabilities of different techniques to build a semantic
representation of educational digital resources. Educational digital resources are modeled …

In spite of the growing opportunities and demands for using social media to assist
government decision-making, few studies have investigated social media sentiments toward …

It has become more challenging for the security analysts to identify cyber threat related
content on the Internet because of the vast amount of publicly available digital texts. In this …

The abundance of multimodal data (eg social media posts) has inspired interest in cross-
modal retrieval methods. Popular approaches rely on a variety of metric learning losses …

Latent Dirichlet Allocation (LDA) is a probabilistic topic model to discover latent topics from
documents and describe each document with a probability distribution over the discovered …

Social media platforms like Twitter enable policymakers to communicate their policy
preferences directly and provide a bird's‐eye view of their diverse policy agendas. In this …