scholarly journals Possibilities of automatic text analysis in the task of determining the psychological characteristics of the author

2020 ◽  
Vol 13 (1) ◽  
pp. 149-158
Author(s):  
A.K. Kovalev ◽  
Y.M. Kuznetsova ◽  
M.Y. Penkina ◽  
M.A. Stankevich ◽  
N.V. Chudova

Using a tool for automatic text analysis and machine learning methods developed at the Federal Research Center ‘Computer Science and Control’ of the Russian Academy of Sciences, the first results are obtained in the task of identifying text parameters specific to people with certain psychological characteristics. The tool of corpus linguistic and statistical research, based on the use of relational-situational analysis, psycholinguistic indicators and dictionaries covering the vocabulary of emotional and rational assessment, allowed us to obtain values for 177 textual attributes of the essay written by 486 subjects. To obtain data on the severity of characterological and personality characteristics of the subjects, a number of psychological questionnaires were used. When processing the data, binary classification algorithms were used — the support vector method (SVM) and the Random Forest method. The results allow us to draw conclusions about the prospects of using some textual parameters in problems of population psychodiagnostics and the adequacy of the applied classification algorithms.

2014 ◽  
Vol 989-994 ◽  
pp. 1913-1917
Author(s):  
Min Li ◽  
Meng Dong Chen ◽  
Xiang Bin Li

With the rapid development of network, texts which contain position, views and opinions of events are exploding. Texts of review contain author’s feelings, views and tendencies the author wants to express. People need to analyze these texts automatically to acquire sentiment tendency of the author. This paper presents a model for automatic text analysis about sentiment tendency on comment text. The model combines algorithms based on emotional dictionary and Support Vector Machine learning algorithm together, which takes advantage of both algorithms.


2012 ◽  
Vol 56 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Yair Neuman ◽  
Yohai Cohen ◽  
Dan Assaf ◽  
Gabbi Kedma

Author(s):  
Wouter van Atteveldt ◽  
Kasper Welbers ◽  
Mariken van der Velden

Analyzing political text can answer many pressing questions in political science, from understanding political ideology to mapping the effects of censorship in authoritarian states. This makes the study of political text and speech an important part of the political science methodological toolbox. The confluence of increasing availability of large digital text collections, plentiful computational power, and methodological innovations has led to many researchers adopting techniques of automatic text analysis for coding and analyzing textual data. In what is sometimes termed the “text as data” approach, texts are converted to a numerical representation, and various techniques such as dictionary analysis, automatic scaling, topic modeling, and machine learning are used to find patterns in and test hypotheses on these data. These methods all make certain assumptions and need to be validated to assess their fitness for any particular task and domain.


Science ◽  
1970 ◽  
Vol 168 (3929) ◽  
pp. 335-343 ◽  
Author(s):  
G. Salton

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