scholarly journals Toward an Automatic Classification of Negotiation Styles Using Natural Language Processing

2017 ◽  
pp. 339-342 ◽  
Author(s):  
Daniela Pacella ◽  
Elena Dell’Aquila ◽  
Davide Marocco ◽  
Steven Furnell
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automatic Classification

scholarly journals Automatic classification of the emotional content of web documents

2021 ◽  
Author(s):  
Alaa Hussainalsaid
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Automatic Classification ◽  
Emotional Content ◽  
Web Pages ◽  
Web Documents ◽  
N Gram

This thesis proposes automatic classification of the emotional content of web documents using Natural Language Processing (NLP) algorithms. We used online articles and general documents to verify the performance of the algorithm, such as general web pages and news articles. The experiments used sentiment analysis that extracts sentiment of web documents. We used unigram and bigram approaches that are known as special types of N-gram, where N=1 and N=2, respectively. The unigram model analyses the probability to hit each word in the corpus independently; however, the bigram model analyses the probability of a word occurring depending on the previous word. Our results show that the unigram model has a better performance compared to the bigram model in terms of automatic classification of the emotional content of web documents.

scholarly journals Automatic classification of the emotional content of web documents

2021 ◽  
Author(s):  
Alaa Hussainalsaid
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Automatic Classification ◽  
Emotional Content ◽  
Web Pages ◽  
Web Documents ◽  
N Gram

This thesis proposes automatic classification of the emotional content of web documents using Natural Language Processing (NLP) algorithms. We used online articles and general documents to verify the performance of the algorithm, such as general web pages and news articles. The experiments used sentiment analysis that extracts sentiment of web documents. We used unigram and bigram approaches that are known as special types of N-gram, where N=1 and N=2, respectively. The unigram model analyses the probability to hit each word in the corpus independently; however, the bigram model analyses the probability of a word occurring depending on the previous word. Our results show that the unigram model has a better performance compared to the bigram model in terms of automatic classification of the emotional content of web documents.

Automatic Classification of Valve Diseases Through Natural Language Processing in Spanish and Active Learning

2021 ◽  
pp. 39-50
Author(s):  
Pablo Pérez-Sánchez ◽  
Víctor Vicente-Palacios ◽  
Manuel Barreiro-Pérez ◽  
Elena Díaz-Peláez ◽  
Antonio Sánchez-Puente ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Active Learning ◽  
Natural Language ◽  
Language Processing ◽  
Automatic Classification ◽  
Valve Diseases

A Comparison of Natural Language Processing Methods for the Classification of Lumbar Spine Imaging Findings Related to Lower Back Pain

2021 ◽  
Author(s):  
Chethan Jujjavarapu ◽  
Vikas Pejaver ◽  
Trevor A. Cohen ◽  
Sean D. Mooney ◽  
Patrick J. Heagerty ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Back Pain ◽  
Lumbar Spine ◽  
Natural Language ◽  
Language Processing ◽  
Lower Back Pain ◽  
Imaging Findings ◽  
Lower Back ◽  
Spine Imaging
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