Journal of Environmental Treatment Techniques
2020, Volume 8, Issue 4, Pages: 1606-1610
dissatisfaction with the policies of Angela Merkel and her party
was shown by liberal, left-wing sources. At the same time, other
European countries presented the refugee problem as a threat to
the security and preservation of the cultural code to a much
greater extent than Germany. However, this does not mean that
the attitude towards refugees was positive. M. Berry, I. Garcia-
Blanco and K. Moore emphasize that the German newspapers
publish few materials that would focus on the benefits of hosting
refugees for the economic and cultural situation in the country [7].
B. Holzberg, K. Kolbe and R. Zaborowski come to some
different conclusions: their analysis of the German mass media
regarding the refugee problem showed more negative public
sentiments. In particular, B. Holzberg and colleagues found out
that the image of a refugee in mass media is the image of a person
who is considered in terms of disadvantages or advantages for the
host country. Accordingly, the German newspapers articles
ignore the reasons for the crisis and pay attention to the presence
of refugees, which is considered a heavy burden [8, p. 534-550].
However, it is worth mentioning that such generalizations do
not take into account the political pluralism of the German
newspapers. Nowadays, the German press freedom is guaranteed
by the state and the Constitution, which is explained by the
diversity and wide range of mass media in the country. The
importance of journalists and editorial staff is so great that the
press in Germany is called the “fourth power”, which performs a
monitoring and educational function. Each of the political content
has its sources through which it expresses its views and opinions.
G. Nordheim, H. Müller and M. Scheppe believe that there are
obvious differences in how traditional and right-wing mass media
cover the refugee problem. Using the 2015-2016 newspaper
articles as an example, the authors of the research show that right-
wing newspapers such as Junge Freiheit raise negative sentiment
and ignore more detailed aspects of the problem [9, p. 38-56]. In
on the tonality of the expressed opinions [13, p. 2-10]. The
novelty of the research is in the attempt to use the method of
dictionary-based sentiment analysis applied to the newspapers
articles.
This research was aimed at identifying the tonality of the
concept “Refugee” in the publicist discourse, using the example
of a newspaper article genre.
1.2. Research Hypothesis
There are concepts in German linguistic culture, that are
implemented in the discourse process of the language system,
have a certain specificity and lend themselves to linguistic
research. The concept “Refugee” considered in publicist
discourse is such a concept. This concept cannot be analysed in
isolation from the context of newspapers, namely, from the
political situation in Germany and the significant difference in the
attitude to migration and refugees among representatives of
various political factions. The null hypothesis of the study is the
absence of differences between the representation of the concept
“Refugee” in sources of different political content. The alternative
hypothesis suggests the presence of these differences.
2
Methods
2
.1 Aims of the research
The following objectives were pursued in the research: 1)
machine analysis of the concept “Refugee” considering articles
from online versions of the conservative (Deutschland Kurier),
liberal (Die Tageszeitung (Taz)) and social-democratic
newspapers (Stern); 2) determination of the tonality of the studied
concept “Refugee” and its classification into three categories:
negative, positive or neutral; 3) finding the interconnections
between the concept “Refugee” and emotional concepts.
2
017, M. Haller criticized the review of the refugee crisis and said
2
.2 Theoretical and Empirical Methods
The following methods were used in the research:
Theoretical: analysis of text lexical units collected from
that the events of 2015-2017 deepened the split between the mass
media of different political content, making each of the positions
more radical [10].
online newspapers, their study and generalization, synthesis;
empirical: dictionary-based sentiment analysis. Nowadays,
there are several sentiment dictionaries of the German language.
We used SentiWortschatz (SentiWS) [14], which contains 1,650
negative and 1,818 positive words in the range (-1; 1) for the
analysis. Another reason to choose SentiWS, in addition to its rich
content, was its focus on political and social vocabulary, which
makes it suitable for analysing the political newspapers articles.
The second sentiment dictionary, German Polarity Lexicon
As for the Germans themselves, recent studies show that they
do not have a pronounced, unambiguous attitude to the problem.
5
9% of the Germans are concerned about the influx of refugees,
but at the same time, they are aware of the economic benefits
associated with their presence in the country [11]. Moreover,
many of them believe that helping people who are forced to leave
their countries because of a life-threatening situation is the moral
duty of a prosperous European country. As for conceptology,
according to the study by O.G. Palutina, the presence of
controversial elements in the structure of concept is not
uncommon. The concept’s associations may contain both positive
and negative elements [12, p. 251-255].
(
“PolArt” -Lexicon) [15] contains 3,424 positive, 5,294 negative
and 662 neutral nouns, verbs and adjectives. The third dictionary,
German Emotion Dictionary [16], differs from the previously
mentioned, as it does not determine the tonality of the text
In its turn, the analysis of the text's tonality as a special class
of natural language processing is gaining popularity in
computational linguistics. Machine texts analysis conducted to
highlight the emotional component shows the fast pace of the
development as well as promising prospects. In particular,
machine methods allow one to process great amounts of texts,
while its manual analysis would be time-consuming. Foreign
studies have already dealt with the tonality analysis of political
texts, but they have mostly focused on analysing the social
networks discourse and short materials such as posts on Twitter.
For example, A. Bermingham and A. Smeaton used data from
Twitter to predict the results of the US presidential race, basing
(
negative or positive). Its purpose is to identify emotions in the
text, such as fear, joy, anger and others. To calculate tonality, an
algorithm was written in the Python programming language. The
general tonality of each text is understood as the difference
between the sum of all individual negative indicators and the sum
of all individual positive indicators. The model was validated
using an error matrix that showed a classification accuracy (f-1)
of 83% for a liberal source, 80% for a social-democratic source
and 70% for a conservative source.
2
.3 Research base
The study was based on sources that differ in political content.
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