Topic modeling is used in information retrieval to infer the hidden themes in a collection of
documents and thus provides an automatic means to organize, understand and summarize …

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic
modeling have been developed which consider many kinds of relationships and restrictions …

We illustrate the design and use of open-ended survey questions to elicit people's first-order
concerns on policies. Closed-ended questions are the backbone of surveys but may prime …

In present-day in consumers' homes, there are millions of Internet-connected devices that
are known to jointly represent the Internet of Things (IoT). The development of the IoT …

Firms apply strategic foresight in technology and innovation management to detect
discontinuous changes early, to assess their expected consequences, and to develop a …

Abstract Word Sense Disambiguation (WSD) has been a basic and on-going issue since its
introduction in natural language processing (NLP) community. Its application lies in many …

Topic modelling is a prominent task for automatic topic extraction in many applications such
as sentiment analysis and recommendation systems. The approach is vital for service …

Rapid economic development of any country will usually lead to negative environmental
impacts. A free market economy cannot fundamentally solve this issue, which requires the …

Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining
technique to extract human-readable semantic “topics”(ie, word clusters) from a corpus of …

Household waste segregation and recycling is ranked at a high priority of the waste
management hierarchy. Its management remains a great challenge due to the high …