SentiStrength

 

SentiStrength is a software program that analyzes sentiment. SentiStrength gives sentiment results in three types. First, it gives us the results in binary form. In other words, it gives the sentiment results as positive or negative. Second, it gives it as a trinary. That is, the results are presented as positive, negative or neutral. Finally, it gives sentiment results as a single scale. In other words, sentiment results are given on a scale between -5 and +5. SentiStrength supports many languages such as German, English, Finnish and Turkish and is capable of sentiment analysis of texts written in these languages. The SentiStrength program is completely free.

10108 tweets about online food ordering services are analyzed with the SentiStrength program to explain this software in depth. The main problem we encounter at this stage is that the SentiStrength program only accepts documents with the ".txt" extension. If the text you want to analyze is ".xlsx" extended, you should deal with this issue first. Unfortunately, the file I would to analyse has the ".xlsx" extension. For this reason, I will first convert the section I want to examine in the excel file to .txt format. In this context, delete the sections except the section we want to analyze, and only the column we want to analyze remains in our file. In the next step, we need to remove the duplicates to avoid recounting the same tweets. In Excel, we can remove duplicates using the “Data-> Remove Duplicates” function. The logo you see marked is the logo that belongs to the "remove duplicates" function.

Then save our excel file, which we cleaned from repetitions, as unicode text (.txt). In the next step, open the SentiStrength program and select the "Analyze ALL Texts in File (each line separately)" function in the "Sentiment Strength Analysis" section.

Next, let's select the .txt file we want to analyze. Before the analysis starts, SentiStrength will ask if you want to add a header line to the resulting file, and we need to select "Yes". SentiStrength will ask us which column to use for analysis and we can directly write "1" here.

If this image occurs, it means that our analysis is complete. However, at this stage, the problem we encountered at the beginning reoccurs. The results are saved on our computer with the .txt extension.

It will be useful to copy and paste the results with .txt extension to the excel file so that we can see the results more clearly.

At the last stage, we will have an excel file with 5 columns such as positive, negative and EmotionRationale. When we look at the results, it is seen that there are many -1 and +1. -1 means not negative or neutral. +1 means not positive or neutral. Between 2 and 4 is positive and 5 is extremly positive. On the other hand, between -2 and -4 is negative and -5 is extremely negative.

Finally, we can add another column named "score" next to the negative and positive columns in the excel file and see the general sentiment result. For this process, we need to use the following formula; "=C2+D2". The results we get with this formula will give the overall result of each tweet we analyzed.

MULTICULTURALISM, NATION-STATE AND BERBER MOVEMENT IN ALGERIA

After decolonisation process, many African and Asian multicultural states designed their countries based on the nation-state model after their independence. One of the countries established based on the nation-state model was Algeria. Algeria was designed in accordance with the nation-state model due to its multicultural nature. In addition to the nation-state model, Algeria's official state ideology was determined as socialism based on Arab nationalism. The aim of the official ideo-logy was to Arabize Barbers and other cultures and ethnicities in Algeria. The Algerian regime ignored the Berber language and identity. This led to problems between the regime and the Berbers in Algeria. Because the Algerian new regime forced Berber community to adopt the Arab identity. The identity and ethnicity problems between the regime and the Berbers in Algeria still continue today.

KEYWORDS: Algeria, Berber movement, Nation-state, Multiculturalism, Algerian Army, Algerian Regime

LINK: https://dergipark.org.tr/tr/pub/ijshs/issue/59252/853209

The Effect Of Ethnic Participation And Ethnic Fractionalization On Terrorism in Sri Lanka

Terrorism is one of the biggest problems in the world. This problem has disturbed the people of Sri Lanka for many years. Sinhalese and Tamil, the two biggest ethnic groups of this island country, have been in conflict for many years. This study tries to analyze the Tamil issue in Sri Lanka. Besides, this study tries to reveal the causes and consequences of conflict. The main purpose of this paper is to look directly at and examine the association between ethnic participation in political life and fractionalization in Sri Lanka, and the possibility of an act of terrorism from that nation. 

Keywords: Sri Lanka, LTTE, Ethnic Participation, Ethnic Fractionalization, Terrorism

LINK: https://dergipark.org.tr/tr/pub/bader/issue/59113/850715

 ISIS Religious and Extremist Propaganda on Social Media: Dictionary-Based Study of Twitter

Abstract

The world had faced with many terrorist organizations until 2014. However, after 2014, the world faced with the most complicated terrorist organization. This terrorist organization is ISIS or Islamic State of Iraq and Syria. ISIS has been running a more different propaganda campaign, emphasizing the state-building and welfare schemes run by this organization and these elements make ISIS more complicated. ISIS has been very well integrated into the new technology such as social media and smartphone and ISIS has been using them very effectively. Especially Twitter has become a major component of ISIS social media movement. Twitter was used to spread sensationalistic ISIS photos and videos across the Twitter users. While ISIS spread fear and messages on twitter, at the same time it also gained supporters. However, it is seen that ISIS’ sympathizer uses different jargons in terms of their number of followers in twitter. As a result of my research, I found that users with more followers used a stronger violence jargon on Twitter, while users with fewer followers using a softer and more religious language. Users with less followers were an emphasis on unity and religion, while users with more followers encouraging physical violence such as lone wolf attacks and killing enemy appeared more often on Twitter. Dictionary-based analysis of ISIS' and its sympathizers' tweets were performed. This dictionary-based research creates a typology to explain and categorize tweets from ISIS and its followers. For reliability, "Split-half test" was applied to the results and similar results were reached.

Keywords: ISIS, Twitter, Dictionary-Based Analysis, Propaganda

RELATED LINK: https://smallwarsjournal.com/jrnl/art/isis-religious-and-extremist-propaganda-social-media-dictionary-based-study-twitter

The Role of Mass Media in Terrorism and Its Effect on Individuals

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

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

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.