Nowadays terrorism is one of the biggest problems in the world. In particular,
afterward the twin towers attack in the United States on September 11, 2001, this has become a
bigger problem for most of the countries in the world. Following in time the 9/11 terrorist attack,
many governments launched a war against global terrorism in different parts of the world. Even
the community psychology, which is not threatened by global terrorism, is negatively affected
by extremist groups’ coverage in media. Asian countries like Japan, Taiwan, and the Republic
of Korea are the best examples of that. For this cause, in this research two linear regression are
analyzed and Taiwanese newspaper text analyses are made. The main purpose and motivation
of this research is to analyze the importance of media on terrorism and terrorist acts and how
the media affects people’s thoughts and psychology.
RELATED LINK: https://dergipark.org.tr/tr/pub/insanveinsan/issue/55807/695346
Saturday, July 4, 2020
Wednesday, July 1, 2020
The Opinion of The Taiwanese Media on Pro-Independence and Pro-Unification with People Republic China During Tsai Ing-Wen Era
Abstract: Taiwan-People’s Republic of China (PRC) relations have attracted a lot of attention in recent years. These relationships are at the core of Taiwan's social and political life. The pro-independence and pro-unification social cleavage line affected by Taiwan-PRC relations divides Taiwan's political life into two separate camps. These camps are pan-blue coalition (pro-unification) and the pan-green coalition (pro-independence). In this study, the views of Taiwanese newspapers containing both camps on Taiwan-PRC relations are analyzed quantitatively. The study focuses specifically on the Tsai Ing-Wen period. Within the scope of the study, dictionary-based and sentiment analysis methods are used and the selected newspapers are examined with these principles. As a consequence of the quantitative analysis of these three English Taiwanese newspapers, it is shown that the media adopted a perspective similar to the views of the government and the DPP throughout the Tsai Ing-Wen period. Furthermore, sentiment analysis is done of the news from these newspapers relevant to PRC-Taiwan ties. It has been noted that the reporting regarding PRC-Taiwan ties in all three newspapers has a positive language.
Keywords: Taiwan, PRC, Text Analysis, Pro-Unification, Pro-Independence, KMT, DPP
RELATED LINK: http://daadtr.com/DAAD/ArchiveIssues/PDF/787eb20a-e1bb-ea11-810a-005056b0673e
Keywords: Taiwan, PRC, Text Analysis, Pro-Unification, Pro-Independence, KMT, DPP
RELATED LINK: http://daadtr.com/DAAD/ArchiveIssues/PDF/787eb20a-e1bb-ea11-810a-005056b0673e
Monday, January 27, 2020
2020 General Election in Taiwan
The results of the Taiwan general elections had been eagerly
awaited for a long time. Especially the results of the local elections in 2018
made these general elections more remarkable. In addition, the protests in Hong
Kong increased the importance of the Taiwan general elections.
In this article, I will first describe my observations in
general elections. Then I will briefly explain the results of Twitter analysis
about Taiwan general election, which I analyzed with the text analysis method.
OBSERVATION
In the morning, I started
following the general elections in Taiwan. At the same time, I also tried to
feel the political atmosphere inside country. Because of that reason, I
talked with some of my Taiwanese friends. Around 7 PM, the election results
began to be announced. Around 7.30pm, I first went to the KMT's
headquarter in Kaohsiung. However, the KMT headquarter was quite empty. The
most important reason for this was that DPP was ahead by far, according to the
first results. Another important reason was that pan-blue party sympathizers
did not trust their candidates.
Around 8 pm, the results began to
be clear and I decided to go to the DPP's rally. The rally area was quite
crowded. It was as crowded as the KMT's rally, which won last year's local
elections. The excitement and joy of people in the rally area was quite
interesting.
As a result of the local elections
in 2018, the Green Party(DPP) lost the local elections. As a result of the
defeat, the DPP leader and Taiwanese president Tsai Ing-Wen resigned from party
leadership. But Tsai Ing-Wen, who withdrew her resignation, won this
year's general elections.
With the results, the KMT candidate Han Kuo-yu made his
speech first. About an hour later, Tsai Ing wen, the winner of the election,
made her speech. However, the first thing that caught my attention during this
speech was that Tsai Ing Wen spoke quite calmly and seriously. Unlike Ing Wen,
DPP sympathizers were extremely happy.
In her speech, Ing wen praised Taiwan's democracy and stated
that relations with China will continue in the same way.
TWITTER ANALYSIS
2000 English tweets about Taiwanese general elections
between the January 1st and January 20th were examined with R computer program.
First, the most commonly used words were found in these 2000 tweets. The words
Economy, Growth, Hong Kong Protests, independence and victory are the most used
words in 2000 tweets.
Most of the tweets are positive about Ing-Wen and DPP's
victory.
Words like freedom, peaceful, democracy, growth and hope are
the most commonly used positive words. Words such as infrared, protest, fight
and Xi Jinping are the most commonly used negative words.
Secondly, sentiment
analysis of these 2000 tweets was analyzed. The highest sentiment in these tweets is trust. Then comes
anticipation and fear.
Words like president, democracy, freedom, integrity, alliance
and brilliant are the words with the highest trust sentiment. Words like young,
vote, result and prevention are the words with the highest sense of
anticipation. Words like Fight, Interfere, and Xi Jinping are the words with
the highest sense of fear.
CONCLUSION
In conclusion, DPP sympathizers were very happy and hopeful
at the DPP rally. Twitter analysis results gave similar results.
However, despite all this happiness and anticipation, it is
a matter of curiosity how China-Taiwan relations and Ing-Wen's attitude towards
China will change and shape.
Labels:
Hong Kong,
Kaohsiung,
news,
polarity analysis,
PRC,
R language,
R programing,
R.O.C.,
sentiment analysis,
social media,
Taiwan,
text analysis,
tweets,
twitter,
word cloud,
world
Monday, September 30, 2019
Lijphart's Democracy Idea and Turkey
This paper tries to apply Lijphart’s way of description with ten factors for the two models of democracy to the Republic of Turkey. Turkey, with her important location, which is a country with a different political structure and social structure, can be added to Lijphart example. This research mainly analysis Turkish political system with Lijphart’s majoritarian and consensus democracy idea. In this study, while examining the case of Turkey, the World Bank, IMF, and IV project, Freedom House reports and data were used.
URL: https://transformative.ub.ac.id/index.php/jtr/article/view/46
URL: https://transformative.ub.ac.id/index.php/jtr/article/view/46
Tuesday, September 24, 2019
Trend Topic Analysis with R
Twitter is one of the most popular social media applications
in recent years. With the R program, it is possible to analyze many data from
Twitter. Two of these analyzes are sentiment and polarity analysis.
These analyzes were applied to one of the trend topics on
Twitter. For this purpose, the first trend topics in the world were found with
"getTrend ()" function. As a results of this research, the most
popular trend topic is #gretathunberg.
Afterwards, tweets were cleaned and sentiment and polarity
analysis were performed.
First, polarity analysis was performed. As a results of this
analysis, the majority of tweets related to #gretathumberg are positive.
Figure 1: polarity analysis
Secondly, sentiment analysis was performed. According to
this analysis, anticipation and trust are the most dominant feelings among
tweets.
Figure 2: Sentiment analysis (second column is anticipation.)
Finally, the most popular devices, which are used to post on
twitter, were analyzed. Twitter web app and twitter android are the most used
software.
Figure 3: Most used software and devices
Tuesday, September 17, 2019
Terrorism Dilemma
Terrorism has been one of the most
remarkable issues in recent years. Many scholars argue about terrorism. However,
the lack of a definition of terrorism complicates the issue.
The most acceptable definition of terror
in the world is the CIA's definition of terror. This study uses the CIA's
definition of terrorism. According to Central Intelligence Agency (2007),
terrorism means premeditated, politically motivated violence perpetrated
against noncombatant targets by subnational groups or clandestine agents,
usually intended to influence an audience.
This definition is quite clear. Based on
this definition, the YPG in Syria is a terrorist organization. However, many
Western countries and the United States do not recognize YPG as terrorists. But
this organization has caused the deaths of many innocent people in Turkey.
Nevertheless, the media of many countries continue to have positive news about
this organization. In addition, there are also positive news about the
terrorist organization on Twitter.
For this purpose, sentiment and polarity
analysis related with YPG in CNN, The New York Times and Twitter was performed.
Figure 1: Polarity result of CNN
Firstly, CNN's latest news about ypg is
examined. The analysis revealed that the CNN's news was very positive about
YPG. The most commonly used sentiment in the news about YPG is trust.
Figure 2: Sentiment result of CNN
Secondly, the last news of
the new york times about ypg was analyzed. Similar results were obtained with
CNN. The new york times ypg news is also very positive. The most commonly used
sentiment in the New York Times’ news about YPG is trust.
Figure 3: Polarity result of New York Times
Figure 4: Sentiment result of New York Times
Finally, the last 100 tweets about YPG on Twitter
were analyzed. Twitter also has positive tweets about YPG, like CNN and New
York Time. The most commonly used sentiment in twitter about YPG is fear. Tweets about
ISIS raise the fear rate. That’s why fear was the most common sentiment in this
anaysis.
Figure 5: Polarity result of Tweets
Figure 6: Sentiment result of Tweets
As seen above, there are positive views on YPG, which many countries recognize as terrorist organizations, in the media and social media.
Labels:
cnn,
ISIS,
new york times,
news,
polarity analysis,
R programing,
sentiment analysis,
terrorism,
terrorist,
text analysis,
text mining,
the new york times,
tweets,
twitter,
YPG
Thursday, August 15, 2019
Cryptocurrency(1)
In recent years, cryptocurrencies have become quite popular in the world. However, this issue of cryptocurrency reliability is highly controversial.
In addition cryptocurrency is used in many illegal trade on the deep web. These cryptocurrencies are used for illegal slave trade and drug.
In addition to all these problems, one of the most important problems is why the cryptocurrency rises and falls. Many scholars have different opinions on this issue. However, the common view is that the ups and downs in bitcoin or cryptocurrencies do not depend on any physical factors.
However, I think that this idea is not correct. I think that real economic activities play an important role in the rise and fall of bitcoin and other cryptocurrencies. I prepared a data set to test my idea. This data set includes data such as ripple and bitcoin. In addition, this data set includes data such as oil, dollar and gold.
This data set covers June 2014 to August 2019. The values in this dataset were first translated into Turkish Lira and then normalized in R program. My results were different from the literature.
Firstly, correlation analysis was performed. According to the results of the correlation analysis, there is a strong correlation between Bitcoin and USD and gold. In addition, there is also a correlation between oil and bitcoin. But this correlation is not strong like USD and gold.
In addition to correlation analysis, linear regression analysis was performed. The results of linear regression analysis confirmed the results of correlation analysis. In linear regression analysis, USD was chosen as the dependent variable. Bitcoin was chosen as an independent variable.
Linear regression analysis also shows that there is a significant relationship between Bitcoin and USD.
In addition cryptocurrency is used in many illegal trade on the deep web. These cryptocurrencies are used for illegal slave trade and drug.
In addition to all these problems, one of the most important problems is why the cryptocurrency rises and falls. Many scholars have different opinions on this issue. However, the common view is that the ups and downs in bitcoin or cryptocurrencies do not depend on any physical factors.
However, I think that this idea is not correct. I think that real economic activities play an important role in the rise and fall of bitcoin and other cryptocurrencies. I prepared a data set to test my idea. This data set includes data such as ripple and bitcoin. In addition, this data set includes data such as oil, dollar and gold.
This data set covers June 2014 to August 2019. The values in this dataset were first translated into Turkish Lira and then normalized in R program. My results were different from the literature.
Firstly, correlation analysis was performed. According to the results of the correlation analysis, there is a strong correlation between Bitcoin and USD and gold. In addition, there is also a correlation between oil and bitcoin. But this correlation is not strong like USD and gold.
In addition to correlation analysis, linear regression analysis was performed. The results of linear regression analysis confirmed the results of correlation analysis. In linear regression analysis, USD was chosen as the dependent variable. Bitcoin was chosen as an independent variable.
Linear regression analysis also shows that there is a significant relationship between Bitcoin and USD.
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