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loveb

Love Börjeson, Ph.D.

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Treemap, category based
Topic model, clustered and represented in a treemap
Topic Model Tree Map
Tre map representation of a topic model based on jokes from reddit
circlepack of topic model
circlepack representation of topic model based on jokes from reddit
Radial representation of a topic model
Pretty. Useless. The model is based on a corpus of jokes from Reddit.
Collapsable tree
representation of a topic model of jokes from reddit
Circlize it!
Mobility between different forms of tenure on the Swedish housing market.
Survival analysis
Script to survival analysis, including Kaplan-Meier survival estimates and Cox Proportional-Hazards Models
Min(d)ing Digitalization
We will look into how “DIGITALIZATION” is written about in social sciences. The data is gathered from WoS SSCI 1992-2018, using “digital*" to harvest English article abstracts, in total a little more than 25 000. The idea is to get an idea of what scholars in social sciences write about digitalization.
Embedding IRIS-neighbors
A test-drive of t-Distributed Stochastic Neighbor Embedding
Self Organizing IRIS
A test-drive of Self Organizing Maps
Hierarchical Linear Mixed Model
Does CEO tenure have an impact on Agencies spending on MCS? The plot thickens...
Mixed model
results from a project under progress published here for reference.
Unga vuxnas situation på bostadsmarknaden
En modell över sambandet mellan utsatthet, möjlighet och tveksamhet
Topic modeling and correspondence analysis lab (incl. results)
This lab explores latent categorization of texts in two steps. First, we compile abstracts from 53,709 American sociology dissertations (yrs 1990-2013) into a corpus. Using topic modeling, a natural language processing technique, we then try to detect latent topics based on the manifest abstracts. The results are communicated via so called loadings that shows the prevalence of various topics in each abstract. Second, we will try to explore the latent topical structure in an x-dimensional space, using correspondence analysis. We do this by accumulating the topical loadings to topics and to the pre-given labels of the abstracts. The accumulated loadings are aggregated in a two-by-two table, where the loadings in each cell can be thought of as (probable) frequencies of the intersection between a topic and a label.
Topic modeling and correspondence analysis lab
This lab explores latent categorization of texts in two steps. First, we compile abstracts from 53,709 American sociology dissertations (yrs 1990-2013) into a corpus. Using topic modeling, a natural language processing technique, we then try to detect latent topics based on the manifest abstracts. The results are communicated via so called loadings that shows the prevalence of various topics in each abstract. Second, we will try to explore the latent topical structure in an x-dimensional space, using correspondence analysis. We do this by accumulating the topical loadings to topics and to the pre-given labels of the abstracts. The accumulated loadings are aggregated in a two-by-two table, where the loadings in each cell can be thought of as (probable) frequencies of the intersection between a topic and a label.
Sociology 3D_Interactive
Topics modelled on 53,709 dissertation abstracts 1990-2013. Latent structure determined using correspondence analysis and topics/labels are clustered using hierarchal clustering. Size of data-points represent accumulated loading of topics and labels. Accumulated loadings per institutional affiliation added as supplementary categories.
Topical Structure of Sociology
Topics modelled on 53,709 dissertation abstracts 1990-2013. Latent structure determined using correspondence analysis and topics/labels are clustered using hierarchal clustering. Size of data-points represent accumulated loading of topics and labels. Accumulated loadings per institutional affiliation added as supplementary categories.
Sociology 3D
Sociological dissertations, labels and topics in three dimensions
Topics and labels of Sociology
Topics and labels of Sociology