Specialized Review Selection Using Topic Models

2016 ◽  
pp. 102-114 ◽  
Author(s):  
Anh Duc Nguyen ◽  
Nan Tian ◽  
Yue Xu ◽  
Yuefeng Li
Keyword(s):  
Topic Models

Using Topic Models to Extrapolate Subjective Ratings for Psycholinguistic Variables

2013 ◽  
Author(s):  
Pawel Mandera ◽  
Emmanuel Keuleers ◽  
Marc Brysbaert
Keyword(s):  
Topic Models ◽  
Subjective Ratings ◽  
Psycholinguistic Variables

Descending Kernel Track of Static and Dynamic Topic Models in Topic Tracking

2012 ◽  
Vol 23 (5) ◽  
pp. 1100-1119 ◽  
Author(s):  
Yu HONG ◽  
Yu CANG ◽  
Jian-Min YAO ◽  
Guo-Dong ZHOU ◽  
Qiao-Ming ZHU
Keyword(s):  
Topic Models ◽  
Topic Tracking

Supervising topic models with Gaussian processes

2018 ◽  
Vol 77 ◽  
pp. 226-236 ◽  
Author(s):  
Melih Kandemir ◽  
Taygun Kekeç ◽  
Reyyan Yeniterzi
Keyword(s):  
Gaussian Processes ◽  
Topic Models

scholarly journals Expert-Informed Topic Models for Document Set Discovery

2021 ◽  
pp. 1-20
Author(s):  
Eike Mark Rinke ◽  
Timo Dobbrick ◽  
Charlotte Löb ◽  
Cäcilia Zirn ◽  
Hartmut Wessler
Keyword(s):  
Topic Models

Identifying Physical Activity Profiles in COPD Patients Using Topic Models

2015 ◽  
Vol 19 (5) ◽  
pp. 1567-1576 ◽  
Author(s):  
Gabriele Spina ◽  
Pierluigi Casale ◽  
Paul S. Albert ◽  
Jennifer Alison ◽  
Judith Garcia-Aymerich ◽  
...  
Keyword(s):  
Physical Activity ◽  
Topic Models ◽  
Copd Patients

Classifying Sentiment Based on LDA Model

2013 ◽  
Vol 427-429 ◽  
pp. 2614-2617
Author(s):  
Qing Xi Peng
Keyword(s):  
Sentiment Analysis ◽  
Topic Models ◽  
Online Reviews ◽  
Simultaneous Analysis ◽  
Experimental Result ◽  
Proposed Model

Online reviews as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. Although supersized methods have obtained good results, a large amount of corpus should be trained beforehand. Recently, topic models have been introduced for the simultaneous analysis for sentiment in the document. However, the LDA model makes the assumption that, given the parameters the words in the document are all independent. It obviously isnt the case. The words in the document express the sentiment of the author. This paper proposes a model to solve the problem. We assume that the sentiments are related to the topic in the documents. A sentiment layer is added to the LDA model to improve it. Experimental result in the dataset demonstrates the advantage of the proposed model.

Learning routines over long-term sensor data using topic models

2013 ◽  
Vol 31 (4) ◽  
pp. 365-377 ◽  
Author(s):  
Federico Castanedo ◽  
Diego López- de-Ipiña ◽  
Hamid K. Aghajan ◽  
Richard Kleihorst
Keyword(s):  
Topic Models ◽  
Sensor Data
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