Finding the Relationship Between News and Social Media Users’ Emotions in the COVID-19 Process

Müngen, Ahmet Anıl; Aygün, İrfan; Kaya, Mehmet

doi:10.35377/saucis.03.03.830867

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…It can learn long term dependencies. This model was first proposed in the mid-90s [36] and is widely used today [37]. As mentioned in the above sections, RNNs aim to store and transfer the state information of the artificial neural network while working on arrays.…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images

Bayram

Özer

Temurtaş

2021

Sakarya University Journal of Computer and Information Sciences

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Brain injuries are significant disorders affecting human life. Some of these damages can be completely eliminated by methods such as drug therapy. On the other hand, there is no known permanent treatment for damages caused by diseases such as Alzheimer, Autism spectrum disorder (ASD), Multiple sclerosis and Parkinson. Treatments aimed at slowing the progression of the disease are generally applied in these types of disorders. For this reason, essential to diagnose the disease at an early phase before behavioral disorders occur. In this study, a study is presented to detect ASD through resting-state functional magnetic resonance imaging rs-fMRI. However, fMRI data are highly complex data. Within the study's scope, ASD and healthy individuals were distinguished on 871 samples obtained from the ABIDE I data set. The long short-term memory network (LSTM), convolutional neural network (CNN) , and hybrid models are used together for the classification process. The results obtained are promising for the detection of ASD on fMRI.

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Section: Long Short-term Memory Networkmentioning

confidence: 99%

Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images

Bayram

Özer

Temurtaş

2021

Sakarya University Journal of Computer and Information Sciences

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“…The method uses semantic relationships between words in tweets and statistical features that occur together. Concerning the sentiment analysis, recently, Müngen et al conducted a study on finding relationships between news and people's emotions in the Covid-19 pandemic [18]. Samuel et al [19], using Tweets and R statistics software specific to coronavirus, determined the public sensitivity associated with the pandemic by emotion analysis method.…”

Section: Related Workmentioning

confidence: 99%

Twitter Sentiment Analysis Based on Daily Covid-19 Table in Turkey

Kaya

Günay

2021

Sakarya University Journal of Computer and Information Sciences

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The coronavirus pandemic, which began to affect the whole world in early 2020, has become the most talked about agenda item by individuals. Individuals announce their feelings and thoughts through various communication channels and receive news from what is happening around them. One of the most important channels of communication is Twitter. Individuals express their feelings and thoughts by interacting with the tweets posted. This study aims to analyze the emotions of the comments made under the "daily coronavirus table" shared by the Republic of Turkey Ministry of Health and to measure their relationship with the daily number of cases and deaths. In the study, emotional classification of tweets was implemented using LSTM, GRU and BERT methods from deep learning algorithms. The results of all three algorithms were compared with the daily number of cases and deaths.

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“…Modern society relies heavily on these platforms for sharing complete information and expressing opinions and feelings freely. Today, people spend a lot of time on social media websites [1] since the internet is so popular. Likewise, the popularity of social media platforms makes it very likely that people can positively or negatively impact society by sharing their opinions.…”

Section: Introductionmentioning

confidence: 99%

Aspect based Sentiment & Emotion Analysis with ROBERTa, LSTM

Sirisha¹,

Chandana²

2022

IJACSA

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Internet usage has increased social media over the past few years, significantly impacting public opinion on online social networks. Nowadays, these websites are considered the most appropriate place to express feelings and opinions. The popular social media site Twitter offers valuable insight into people's thoughts. Throughout the conflict between Russia and Ukraine, people from all over the world have expressed their opinions. In this study, "machine-learning" and "deep-learning" techniques are used to understand people's emotions and their views about this war are revealed. This study unveils a novel deeplearning approach that merges the best features of the sequence and transformer models while fixing their respective flaws. The model combines Roberta with ABSA (Aspect based sentiment analysis) and Long Short-Term Memory for sentiment analysis. A large dataset of geographically tagged tweets related to the Ukraine-Russia war was collected from Twitter. We analyzed this dataset using the Roberta-based sentiment model. In contrast, the Long Short-Term Memory model can effectively capture longdistance contextual semantics. The Robustly optimized BERT with ABSA approach maps words into a compact, meaningful word embedding space. The accuracy of the suggested hybrid model is 94.7%, which is higher than the accuracy of the stateof-the-art techniques.

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Finding the Relationship Between News and Social Media Users’ Emotions in the COVID-19 Process

Cited by 5 publications

References 31 publications

Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images

Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images

Twitter Sentiment Analysis Based on Daily Covid-19 Table in Turkey

Aspect based Sentiment & Emotion Analysis with ROBERTa, LSTM

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