Unless the number of keywords is increased drastically, it will be difficult to precisely characterize a two-topic segment, but significantly increasing the number of keywords will result in more noise and errors in the description.
This lets anyone with domain expertise define the different concepts and tags which would characterize a page about a particular topic, such as a recipe or a baseball player or a car.
pStevienna de Saille (University of Sheffield), in her study of all documents pertaining to RRI (from the European Commission and others), concluded (personal communication) that the first occurrence of the term was in December 2007, to characterize the topic of a workshop with nanotechnologists and stakeholders, organized by Robinson and Rip [2007] (Robinson and Rip [2007]).
Although the above models were mainly developed for subject classification, they have also been used to investigate burstiness since bursty words can characterize the topic of a document [27], [40].
Our Bio-LDA model used the bio-terms, journal information and the word information to characterize the topic providing a better representation of topics than the simple LDA model, which only can provide the word representation Table 3 shows a table with the most associated topics for 3 of 13,338 possible bio-terms.
This is the first study to characterize the topic in a comprehensive manner, and to attempt to interrelate the contributing factors within a coherent framework.
Further research is warranted to better characterize this topic.
Most of the systematized scientific activity on hormesis, organized to rationalize and characterize the topic per se, has been done by the University of Massachusetts-Amherst (UMass-Amherst) hormesis program led by Edward J. Calabrese.
LDA is a probabilistic model that hypothesizes that each document (e.g., individual tweets) in a given corpus (e.g., all the tweets) has been generated as a mixture of unobserved, or latent, topics, where a topic is characterized by a categorical distribution over words.
In particular, we analyze the students' behavior patterns in the forums of a distance subject, and characterize the relevant topics and subtopics from the forums' messages belonging to two academic years.
