Estimation of 2017 Iran’s Presidential Election Using Sentiment Analysis on Social Media

2018 ◽  
Author(s):  
Sasan Salari ◽  
Navid Sedighpour ◽  
Vahid Vaezinia ◽  
Saeedeh Momtazi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Presidential Election

scholarly journals THE IMPLEMENTATION OF THE MACHINE LEARNING ALGORITHM FOR THE SENTIMENT ANALYSIS OF INDONESIA’S 2019 PRESIDENTIAL ELECTION

2021 ◽  
Vol 22 (1) ◽  
pp. 78-92
Author(s):  
GA Buntoro ◽  
R Arifin ◽  
GN Syaifuddiin ◽  
A Selamat ◽  
O Krejcar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithm ◽  
Presidential Candidates ◽  
N Gram

In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo." We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesian presidential election on Twitter using non-conventional processes resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%. ABSTRAK: Pada tahun 2019 rakyat Indonesia telah terlibat dalam proses demokrasi memilih presiden baru, wakil presiden, dan berbagai calon legislatif negara. Pemilihan presiden Indonesia 2019 sangat tegang dalam kempen calon di ruang siber, terutama di laman media sosial seperti Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, dll. Rakyat Indonesia menggunakan platfom media sosial bagi menyatakan pendapat positif, berkecuali, dan juga negatif terhadap calon presiden masing-masing. Kampen pencalonan menteri, gabenor, dan perundangan hingga pencalonan presiden dilakukan melalui media internet dan atas talian. Oleh itu, kajian ini dilakukan bagi menilai sentimen terhadap calon pemilihan presiden Indonesia 2019 berdasarkan kumpulan data Twitter. Kajian ini menggunakan kumpulan data yang diungkapkan oleh rakyat Indonesia yang terdapat di Twitter dengan hashtag (#) yang mengandungi "Jokowi dan Prabowo." Proses data dibuat menggunakan pilihan komentar, pembersihan data, penguraian teks, normalisasi kalimat, dan tokenisasi teks dalam bahasa Indonesia, penentuan atribut kelas, dan akhirnya, pengklasifikasian catatan Twitter dengan hashtag (#) menggunakan Klasifikasi Naïve Bayes (NBC) dan Mesin Vektor Sokongan (SVM) bagi mencapai ketepatan optimum dan maksimum. Kajian ini memberikan faedah dari segi membantu masyarakat meneliti pendapat di Twitter yang mengandungi sentimen positif, neutral, atau negatif. Analisis Sentimen terhadap calon dalam pemilihan presiden Indonesia 2019 di Twitter menggunakan proses bukan konvensional menghasilkan penjimatan kos, waktu, dan usaha. Penyelidikan ini membuktikan bahawa gabungan algoritma pembelajaran mesin SVM dan tokenisasi abjad menghasilkan nilai ketepatan tertinggi iaitu 79.02%. Manakala nilai ketepatan terendah dalam kajian ini diperoleh dengan kombinasi algoritma pembelajaran mesin NBC dan tokenisasi N-gram dengan nilai ketepatan 44.94%.

scholarly journals Analisis Sentimen Bahasa Indonesia pada Twitter Menggunakan Struktur Tree Berbasis Leksikon

2021 ◽  
Vol 8 (1) ◽  
pp. 135
Author(s):  
Feby Tri Saputra ◽  
Yani Nurhadryani ◽  
Sony Hartono Wijaya ◽  
Defina Defina
Keyword(s):  
Social Media ◽  
Standard Deviation ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Small Difference ◽  
Tree Structure ◽  
Positive Class ◽  
The Relationship ◽  
Tree Method ◽  
Bahasa Indonesia

<p class="Body">Jumlah opini di media sosial seperti Twitter tersebar luas sehingga tidak mungkin membaca semua opini untuk mendapatkan seluruh sentimen. Analisis sentimen merupakan salah satu metode untuk mengatasi masalah tersebut. Salah satu pendekatan dalam analisis sentimen adalah berbasis leksikon. Pendekatan berbasis leksikon dapat menghasilkan performa yang baik pada lintas topik pembicaraan tanpa memerlukan pelatihan data. Namun, pendekatan berbasis leksikon sangat bergantung pada kelengkapan dan keragaman sentimen leksikon. Selain itu, hubungan antarkata sangat penting untuk diperhatikan karena dapat mengubah polaritas sentimen pada teks. Hubungan antarkata dapat direpresentasikan dengan baik menggunakan struktur <em>tree</em>. Penelitian ini menggunakan struktur <em>tree</em> sebagai interpretasi hubungan antarkata dalam pembentukan kalimat dengan menambahan kata ke dalam sentimen leksikon. Metode berbasis <em>tree</em> diujikan pada data dengan lintas topik seperti data twit Pilgub Jabar 2018, Pilpres 2019, dan pandemik COVID-19. Ketiga data uji memiliki proporsi kelas yang tidak seimbang, dengan kelas terbanyak merupakan kelas positif. Metode berbasis <em>tree</em> menghasilkan akurasi sebesar 64,97% (meningkat 1,26%) pada data Pilgub Jabar 2018, 64,33% (meningkat 11,41%) pada data Pilpres 2019, dan 66,24% (meningkat 7,61%) pada data pandemik COVID-19. Metode berbasis <em>tree</em> dapat menghasilkan akurasi yang stabil pada beberapa lintas topik dibuktikan dengan standar deviasi akurasi yang kecil (0,97%) bahkan lebih kecil dari metode tanpa <em>tree </em>(5,4%). Metode berbasis <em>tree </em>dapat meningkatkan <em>weighted f1-measure</em> pada data Pilpres 2019 sebesar 10,45% dan data pandemik COVID-19 sebesar 8,1%, sedangkan hasil pada data Pilgub 2018 tidak berbeda secara signifikan. Hasil akurasi dan <em>weighted f1-measure</em> memiliki selisih yang kecil sehingga pengukuran akurasi valid dan tidak bias terhadap data tidak seimbang.</p><p class="Body"> </p><p class="Body"><em><strong>Abstract</strong></em></p><p class="Judul2"><em>The number of opinions on social media like Twitter is so widespread that it's impossible to read all those opinions to get all the sentiments. Sentiment analysis is one of the methods that could overcome this problem. The lexicon-based approach is one of the sentiment analysis approaches which perform well across data topics without training. However, the lexicon-based approach relies heavily on the completeness and diversity of sentiment lexicons. The relationship between words is important because it could change the sentiment polarity in the text. The tree structure could represent the relationship between words well. This study uses a tree structure as an interpretation of the relationship between words in a sentence. The tree structure is constructed by adding words to the lexicon sentiment. The tree-based method is tested on cross-topic data such as the tweet data of the 2018 West Java Governor Election, the 2019 Presidential Election, and the COVID-19 pandemic. All data used has an unbalanced class proportion, with the positive class being dominant. The accuracy results of the tree-based method on all data consecutively are 64.97% (increased by 1.26%), 64.33% (increased by 11.41%), and 66.24% (increased by 7.61%). The tree-based method produce stable accuracy on several topics proved by the small accuracies standard deviation (0.97%) that even smaller than the non-tree method (5.4%). The weighted f1-measure increases of the tree-based method on all data consecutively are 0% (equal), 10.45%, and 8.1%. The small difference between the weighted f1-measure and accuracy concludes that the accuracy resulted is valid.</em></p><p class="Body"><em><strong><br /></strong></em></p>

scholarly journals Mapping the political landscape of Persian Twitter: The case of 2013 presidential election

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171983523 ◽  
Author(s):  
Emad Khazraee
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Network Analysis ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
The Political ◽  
Topic Detection ◽  
Political Landscape ◽  
English Speaking

The fallacy of premature designations such as “Iran's Twitter Revolution” can be attributed to the empirical gap in our knowledge about such sociotechnical phenomena in non-Western societies. To fill this gap, we need in-depth analyses of social media use in those contexts and to create detailed maps of online public environments in such societies. This paper aims to present such cartography of the political landscape of Persian Twitter by studying the case of Iran's 2013 presidential election. The objective of this study is twofold: first, to fill the empirical gap in our knowledge about Twitter use in Iran, and second, to develop computational methods for studying Persian Twitter (e.g., effective methods for analyzing Persian text) and identify the best methods for addressing different issues (e.g., topic detection and sentiment analysis). During Iran's 2013 presidential election, three million tweets were collected and analyzed using social network analysis and machine learning. The findings provide a more nuanced view of the political landscape of Persian Twitter and identify patterns in accordance with or in contrast to those identified in the English-speaking Twittersphere around the 2013 presidential election. Persian Twitter was dominated by micro-celebrities, whereas institutional elites dominated English discourse about Iran on Twitter. The results also illustrate that Persian Twitter in 2013 was predominantly in favor of reformists. Finally, this study demonstrates that sentiment analysis toward political name entities can be used efficiently for mapping the political landscape of conversation on Twitter.

Fake It “Till You Make It”: Debunking Fake News in a Post-Truth America

2019 ◽  
Vol 8 (1) ◽  
pp. 114-133
Keyword(s):  
Social Media ◽  
First Amendment ◽  
Presidential Election ◽  
Political Discourse ◽  
Cutting Edge ◽  
American People ◽  
Fake News ◽  
News Sources ◽  
The Media ◽  
The U.S

Since the 2016 U.S. presidential election, attacks on the media have been relentless. “Fake news” has become a household term, and repeated attempts to break the trust between reporters and the American people have threatened the validity of the First Amendment to the U.S. Constitution. In this article, the authors trace the development of fake news and its impact on contemporary political discourse. They also outline cutting-edge pedagogies designed to assist students in critically evaluating the veracity of various news sources and social media sites.

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