Sentiment Analysis on Predicting Presidential Election: Twitter Used Case

Barghuthi, Nedaa Baker Al; Said, Huwida

doi:10.1007/978-3-030-43364-2_10

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…People also express their opinions on social media regarding elections. For example, this was seen in the U.S. regarding the presidential election in 2020 [35,36], in India [12,13,37], in Australia [38], in the 2013 Pakistani elections and 2014 Indian elections [39], in Nigeria [40], in the Punjab Legislative Assembly [41], in Indonesia [42], in the 2013 Pakistan elections 2013 [43], in Indonesia [44], in Iran [14], in the Colombian election in 2014 [16] and in the Singaporean election in 2011 [15]. Researchers use several techniques and approaches for text classification task in sentiment analysis [45].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

et al. 2021

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U.S. President Joe Biden took his oath after being victorious in the controversial U.S. elections of 2020. The polls were conducted over postal ballot due to the coronavirus pandemic following delays of the announcement of the election’s results. Donald J. Trump claimed that there was potential rigging against him and refused to accept the results of the polls. The sentiment analysis captures the opinions of the masses over social media for global events. In this work, we analyzed Twitter sentiment to determine public views before, during, and after elections and compared them with actual election results. We also compared opinions from the 2016 election in which Donald J. Trump was victorious with the 2020 election. We created a dataset using tweets’ API, pre-processed the data, extracted the right features using TF-IDF, and applied the Naive Bayes Classifier to obtain public opinions. As a result, we identified outliers, analyzed controversial and swing states, and cross-validated election results against sentiments expressed over social media. The results reveal that the election outcomes coincide with the sentiment expressed on social media in most cases. The pre and post-election sentiment analysis results demonstrate the sentimental drift in outliers. Our sentiment classifier shows an accuracy of 94.58% and a precision of 93.19%.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

et al. 2021

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“…Although there are many studies aiming to analyze the election results through the posts shared on Twitter, most of these studies cannot provide appropriate and consistent results about the election results (Tumasjan et al, 2010;Gayo-Avello, 2013;Ramteke et al, 2016;Salunkhe and Deshmukh, 2017;Budiharto and Meiliana, 2018;Nausheen and Begum, 2018;Batra et al, 2020;Baker Al Barghuthi and Said, 2020). Moreover, as stated in Gayo-Avello (2012), it will not be possible to make an election prediction on social media unless appropriate regulations and sample selections are made.…”

Section: Literaturementioning

confidence: 99%

“…Unlike YouTube, most of the posts on Twitter are for information sharing rather than comments, so it does not create a suitable infrastructure to measure voter attitudes.As mentioned above, although there are many studies on the US elections, there are also studies specific to many countries. Turkey(Uysal et al, 2017;Baker Al Barghuthi and Said, 2020), England (Rita et al, 2023), Indonesia(Budiharto and Meiliana, 2018), Netherlands(Sang and Bos, 2012; Sanders and van Den Bosch, 2013), Singapore(Choy et al, 2011), India(Sharma and Moh, 2016;Chauhan et al, 2023), Nigeria(Oyebode and Orji, 2019), Colombia (Cerón-Guzmán and León-Guzmán) are just a few of these countries.This study is the first study to perform emotion analysis on YouTube comments and elections in Turkey. In addition, unlike other studies on Turkish elections, a broader emotional polarity was obtained in this study instead of only positive or negative emotional polarity.…”

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confidence: 99%

Emotion Analysis on Youtube Comments for 2023 Turkish Presidential Elections

ÇILGIN

2023

Yeni Medya Dergisi

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The 13th Presidential election has created a wide agenda in many countries as well as in Turkey. In this election period, along with traditional media tools, social media tools were also used frequently in the execution of election campaigns. Interactions received through social media platforms once again proved the effective power of social media tools to reach large masses of all parties and party leaders. For this reason, the Open Microphone program organized by Oğuzhan Uğur, in which many politicians participated, was followed with interest not only in Turkey's agenda, but also in the world's agenda. In this context, this study aims to reveal various analysis findings with Emotion Analysis methods, especially from the comments made within the scope of this program. For this purpose, in this study, a total of 261.728 user comments, specific to 7 different politicians, were analyzed using the NRC emotion dictionary. With the NRC emotion dictionary, a broader emotional polarity was obtained, including the emotions of anger, fear, trust, anticipation, surprise, sadness, joy, and disgust, in addition to positive or negative emotion polarity. As a result of the findings, this study reveals that the emotion analysis of the masses through Youtube comments or different platforms can be a critical source of information for political campaigns.

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Generative adversarial network for sentiment‐based stock prediction

Asgarian

Ghasemi

Momtazi

2022

Concurrency and Computation

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Financial markets received more attention due to technological advancements, such as Artificial Intelligence (AI). In addition to the price index, traders and investors constantly monitor stock news on social media. Therefore, predicting the market by analyzing public opinions is an important issue. In this research, we propose three models based on Generative Adversarial Network (GAN), namely Price-GAN, Price-Sentiment-GAN, and Price-Sentiment-WGAN. The first model uses only optimized price features, and the two other models use sentiment features collected from social media as well as optimized price features. All the proposed GAN models include Long Short-Term Memory (LSTM) as generators and Convolution Neural Networks (CNN) as discriminators. To evaluate the proposed models, two different social media datasets in English and Persian are used. Our proposed models predict the close stock price for 15 English and 5 Persian stocks. All of the proposed GAN models outperform the state-of-the-art models by enhancing the performance of the English dataset by 2.44% and the Persian dataset by 12.11%.

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Sentiment Analysis on Predicting Presidential Election: Twitter Used Case

Cited by 4 publications

References 10 publications

Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

Emotion Analysis on Youtube Comments for 2023 Turkish Presidential Elections

Generative adversarial network for sentiment‐based stock prediction

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