Twitter iletileri duygu değerleri ve Bist 30 endeksi günlük değer değişimlerinin Pearson korelasyonu ve Granger nedensellik analizi

Güran, Aysun; Ateş, Emine Gül

doi:10.17341/gazimmfd.660018

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Duygu skorlarının oluşturulması için [9] çalışması baz alınmış ve VADER yazılımı ile olumlu ve olumsuz olarak etiketlenen haber sayıları kullanılmıştır. Bu kapsamda 𝑆 1 , 𝑆 2 , 𝑆 3 olmak üzere üç adet duygu skoru elde edilmiştir.…”

Section: Duygu Skorları (Sentiment Scores)unclassified

“…Atan ve Çınar çalışmasında [8], finansal piyasalar ile ilgili haber metinlerinin yansıttığı olumlu, olumsuz duygu değerleri ile finansal değerler arasında anlamlı bir korelasyonun olduğunu vurgulamışlardır. Ateş ve Güran çalışmaları ile [9], sosyal medyada paylaşılan kısa iletiler ile BİST30 endeksi arasında anlamlı korelasyon ilişkileri olduğunu belirtmiş ve ülkemiz adına önemli olayların olduğu bir dönemde duygu değerleri ve endeks arasında Granger nedensellik ilişkine rastlandığını ifade etmişlerdir. Pek çok çalışmanın deneysel olarak elde etmiş olduğu bulgular neticesinde günümüzde haber kaynaklarının beklenmedik olaylar ile ilgili yansıttığı olumlu ve/veya olumsuz algıların finansal piyasaları etkileyebildiği görülmektedir [10][11][12][13][14][15].…”

Section: Gi̇ri̇ş (Introduction)unclassified

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Predicting BIST 100 Index Movement by using Sentiment Scores and Technical Indicators during the COVID-19 Pandemic

ALACA

Güran

2022

Bilişim Teknolojileri Dergisi

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Sentiment analysis includes the stages of identifying the positive, negative or neutral emotions contained in the text data. Positive and/or negative emotions reflected by the text data can affect the decision-making processes of people, small or large-scale companies. Emotions reflected by documents can be vectorized with sentiment scores and these scores could be useful to forecast time series models. As it is known, the coronavirus, which emerged in Wuhan, China on December 1, 2019 caused a global pandemic all around the world. The Covid-19 pandemic has brought sharp and sudden declines in global and domestic stock markets. Within the scope of our study, it was analyzed whether the sentiment scores obtained from the Covid-19 related news documents were effective in forcasting the trend of the Bist100 index. Technical indicators that have great importance in estimating stock market indices, were also used in the analysis. Hence, the effect of sentiment scores and technical indicators in determining the trend of the stock market index during the pandemic could be observed. As a result of the study, it was observed that the sentiment scores were effective to predict the price trend of stock market index for some periods.

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Section: Duygu Skorları (Sentiment Scores)unclassified

Section: Gi̇ri̇ş (Introduction)unclassified

Predicting BIST 100 Index Movement by using Sentiment Scores and Technical Indicators during the COVID-19 Pandemic

ALACA

Güran

2022

Bilişim Teknolojileri Dergisi

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Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

Gao

Xiao

et al. 2023

Nat Hazards

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The exploding popularity of social networks, provides a new opportunity to study disasters and public emotion. Among the social networks, Weibo is one of the largest microblogging services in China. Taking Guangdong and Guangxi in the south of China as a case, Web Scraper was used to obtain Weibo texts related to oods in 2020. The spatial distribution of oods was analyzed using Kernel Density Estimation.Public emotion was analyzed using Natural Language Processing (NLP) tools. The association between oods and public emotion was explored through correlation analysis methods. The results indicated that:(1) Weibo texts could be utilized as an effective data to identify urban waterlogging risk in Guangdong and Guangxi.(2) More oods occurred in the southeast than in the central and northwest, and more in the south than in the north in Guangdong and Guangxi. The coastal cities and provincial capitals were severely affected.(3) The public emotion was mainly negative and varied signi cantly over time, generally showing stronger negative emotion during periods of heavy precipitation. ( 4) There was a strong correlation between public emotion and oods in spatial-temporal variation. The degree of negative public emotion was signi cantly in uenced by the number of waterlogging points. The presented results serve as the pre-liminary data for future planning and designing of emergency management. HighlightsWeibo texts can be used to study the ooding situation and public emotion.The distribution of waterlogging points was spatially heterogeneous.The type of public response was dominated by the description of the ood.The overall public emotion during the ood was negative.There was a strong correlation between public emotion and ooding.

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Sentiment analysis of public sensitivity to COVID-19 vaccines on twitter by majority voting classifier-based machine learning

ÇILGIN

Gökçen

Gökşen

2022

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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With the rise of social media platforms, which have billions of users around the World, the dissemination of information has become easier than ever. The COVID-19 pandemic has increased the use of social media to discuss many topics, including vaccines. The aim of this study is to analyze public sentiment with Machine Learning of vaccine-related tweets obtained on Twitter in order to better understand the attitudes and concerns of social media users, especially regarding COVID-19 vaccines in Turkey. For this purpose, the majority voting method, which is an ensemble learning method, was developed by comparing the machine learning algorithm used in six different classification tasks and then via Support Vector Machine, XGBoost and Random Forest having the highest accuracy, in the study. Soft Voting method, which is one of the majority voting methods, has reached a success rate of 90.5%, with a higher success rate than both the Hard Voting approach and the other six individual machine learning approaches. With the Soft Voting method, which has the highest accuracy rate, 412,588 daily tweets from 153 days obtained from Twitter were analyzed and the results were reported. The findings of the study are very striking and differ from studies on other countries. As far as we know, this study is the first study to perform sentiment analysis on COVID-19 vaccines in Turkey, and it shows that it is a valuable and easily applied tool to monitor sensitivity to COVID-19 vaccines with sentiment analysis approach over social media.

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Twitter iletileri duygu değerleri ve Bist 30 endeksi günlük değer değişimlerinin Pearson korelasyonu ve Granger nedensellik analizi

Cited by 5 publications

References 24 publications

Predicting BIST 100 Index Movement by using Sentiment Scores and Technical Indicators during the COVID-19 Pandemic

Predicting BIST 100 Index Movement by using Sentiment Scores and Technical Indicators during the COVID-19 Pandemic

Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

Sentiment analysis of public sensitivity to COVID-19 vaccines on twitter by majority voting classifier-based machine learning

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