# name
A tensor based approach for temporal topic modeling

# metadata.dc.description[].value
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Jordi Vitrià i Marca

# metadata.dc.description.abstract[].value
Latent Dirichlet Allocation (LDA) are a suite of algorithms that are often used for topic modeling. We study the statistical model behind LDA and review how tensor methods can be used for learning LDA, as well as implement a variation of an already existing method. Next, we present an innovative algorithm for temporal
topic modeling and provide a new dataset for learning topic models over time. Last, we create a visualization for the word-topic probabilities.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Oriol Julià Carrillo, 2016

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Matemàtiques

# metadata.dc.subject.classification[].value
Tractament del llenguatge natural (Informàtica)

# metadata.dc.subject.other[].value
Natural language processing (Computer science)

# metadata.dc.title[].value
A tensor based approach for temporal topic modeling