scholarly journals Analisis Sentimen Terhadap Kandidat Presiden Republik Indonesia Pada Pemilu 2019 di Media Sosial Twitter

2019 ◽  
Vol 3 (4) ◽  
pp. 405
Author(s):  
Cahyo Prianto ◽  
Nisa Hanum Harani ◽  
Indra Firmansyah
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Negative Polarity ◽  
Internet Technology ◽  
Behavior Patterns ◽  
Positive Polarity ◽  
Presidential Candidates ◽  
Average Value ◽  
A Value ◽  
Tweet Classification

The development of technology today has been growing rapidly and has an impact on the behavior patterns of people who feel it. The Ministry of Communication and Information (KOMINFO) released a data that of 265 million people of Indonesia, there are around 54% have used internet technology or about 143 million people. In one survey IDN Research Institute said that there are three Social Media that are widely used in Indonesia, namely Facebook, Instagram and Twitter. This study focuses on extracting data in the form of text produced from social media twitter that responds to the account of the RI presidential candidates in the 2019 elections. Sentiment analysis is obtained through tweet classification using sentiment analysis tools such as NRC Lexicon and Bing Lexicon so that information is obtained in the form of positive polarity and negative polarity from community tweets towards the Presidential candidates in the 2019 elections. Using March data before the 2019 election, for candidate 01 Joko Widodo, the NRC Lexicon analysis gave a value of 249 and bing lexicon of 267 with an average value of 0.11, while for candidate 02 Prabowo Subianto the NRC Lexicon analysis gave a value of 195 and bing lexicon of 204 with an average value of 0.085. Using april data after the 2019 election. Candidate 01 Joko Widodo still received a lot of responses from netizens but the sentiment value shifted more negatively compared to candidate 02 Prabowo Subianto. For candidate 01 Joko Widodo the NRC Lexicon analysis gave a value of 17 and bing lexicon of -273 with an average value of -0,246, while for candidate 02 Prabowo Subianto the NRC Lexicon analysis gave a value of 238 and bing lexicon of -73 with an average value of -0.02430939.

scholarly journals Sentiment Analysis of Imran Khan’s Tweets

2021 ◽  
Vol 36 (3) ◽  
pp. 473-494
Author(s):  
Sadia Saeed ◽  
Tehseen Zahra ◽  
Asim Ali Fayyaz
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Negative Polarity ◽  
Positive Polarity ◽  
Negative Polarity Items ◽  
Polarity Items ◽  
Interdisciplinary Approaches ◽  
Computer Scientists ◽  
The Face ◽  
Negative Face

In the recent past, sentiment analysis has been an area of interests of psychologists, sociologists, neurologists, computer scientists, and linguists including corpus linguists and computational linguists. Interdisciplinary approaches to researching various issues especially the analysis of social media websites such as Facebook, Twitter, and Instagram are becoming popular nowadays. The availability of data on social media has made it easier to analyse the opinion or sentiments of its users. Analysis of these sentiments could reveal the face of users and it could help in various decision-making processes. Sentiment analysis is a system of knowing polarity (positive, negative, and neutral) in discourse. Moreover, sentiments can enable and disable certain functions of discourse and can divert the attention of the audience from important to a less important issue or otherwise, hence, there is a need to analyse the sentiments. In this research, sentiments (Polarity) of Imran Khan’s tweets are analysed with the help of R studio. Data for this study is collected from Imran Khan’s one-year’s tweets, tweeted from 1st January 2018 to 20th November 2018. Later we saved the data in. csv files. The results of the polarity check revealed that he has used all three types of sentiments that is positive, negative, and neutral. However, he mostly used neutral or free polarity items (FPIs) that is 67.41% in his tweets. Among positive and negative polarity items the number of negative polarity items (NPIs) is higher that is 23.21% as compared to positive polarity items (PPIs) which are only 9.40%. The manual analysis of results revealed that only software is not enough and there is a need to check the accuracy of the results manually. The use of negative polarity/negative face reveals that he tries to be independent and autonomous in his decisions (Goffman, 1967). The use of positive polarity items shows he tries to show his positive face to others. Moreover, sentiment analysis demonstrates the presence of themes propagated through the use of various lexical items.

USING OF LSTM NETWORKS IN SENTIMENT ANALYSIS OF DOCUMENTS IN KAZAKH LANGUAGE

2020 ◽  
Vol 69 (1) ◽  
pp. 366-370
Author(s):  
N.K. Kadyrbek ◽  
◽  
М.Е. Mansurova ◽  
М.Е. Kyrgyzbayeva ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Mobile Phones ◽  
User Reviews ◽  
Average Value ◽  
First Case ◽  
The Neural Network

Due to the growing trust in information in social media resources, interest in the field of sentiment analysis is growing. Because sentiment analysis is one of the main technologies for monitoring the opinions of millions of users of social networks. The article discusses the use of LSTM networks in the analysis of the tonality of texts in the Kazakh language. For training the neural network, 1000 user reviews of mobile phones were used. The experiments were carried out in two ways: in the first case, preprocessing of the analyzed reviews was carried out, in the second case, the preprocessing was not carried out. The average value of the metric for assessing the quality of the pre-processed model reached 80%. This indicator is 11% higher than for a model trained on data without preprocessing. The results of the study allowed us to conclude that the preprocessing of the texts improves the quality of the model.

Message sharing and verification behaviour on social media during the COVID-19 pandemic: a study in the context of India and the USA

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anumegha Sharma ◽  
Payal S. Kapoor
Keyword(s):  
Social Media ◽  
Information Sharing ◽  
Peer Review ◽  
Health Anxiety ◽  
Study Finding ◽  
Negative Polarity ◽  
Positive Polarity ◽  
Health Crisis ◽  
Content Type ◽  
Message Type

PurposeTechnology has eased access to information. During the ongoing COVID-19 pandemic, ease of access and transmission of information via social media has led to ambiguity, misinformation and uncertainty. This research studies the aforementioned behaviours of information sharing and verification related to COVID-19, in the context of social media.Design/methodology/approachTwo studies have been carried out. Study 1, with Indian social media users, is a two-factor between-subjects experimental design that investigated the effect of message polarity (positive versus negative) and message type (news versus rumour) on the dissemination and verification behaviour of COVID-19-related messages. The study also investigated the mediation of perceived message importance and health anxiety. Study 2 is a replica study conducted with US users.FindingsThe study finding revealed significantly higher message sharing for news than rumour. Further, for the Indian users, message with positive polarity led to higher message sharing and message with negative polarity led to higher verification behaviour. On the contrary, for the US users, message with negative polarity led to higher message sharing and message with positive polarity led to higher verification behaviour. Finally, the study revealed message importance mediates the relationship of message type and message sharing behaviour for Indian and US users; however, health anxiety mediation was significant only for Indian users.Practical implicationsThe findings offer important implications related to information regulation during a health crisis. Unverified information sharing is harmful during a pandemic. The study sheds light on this behaviour such that stakeholders get insights and better manage the information being disseminated.Originality/valueThe study investigates the behaviour of sharing and verification of social media messages between users containing health information (news and rumour) related to the ongoing COVID-19 pandemic.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2020-0282

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 Sarcasm in Sentiment Analysis of Indonesian Text: A Literature Review

2019 ◽  
Vol 13 (2) ◽  
pp. 54-59
Author(s):  
Sherly Christina
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Negative Polarity ◽  
Sentiment Classification ◽  
Detection Methods ◽  
The Internet ◽  
Online Groups ◽  
Internet Users

Social media, blogs and online groups become a forum that makes is easy for the Indonesian people to express their opinions, suggestions, complaints and even criticisms of a subject liberally. Sentiment analysis is a method for classifying positive, neutral, and negative polarity of the opinions that expressed by the internet users. Sarcasm is one of the challenges to classsifying the sentiments of an opinion. This research is a literature review to examine several studies to find out the methods for detecting sarcasm and to know the effect of sarcasm on the sentiment classification accuracy. The result of this literature review can be used as a reference for developing the sarcasm detection methods.

A Study on Diverse Methods and Performance Measures in Sentiment Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Meesala Shobha Rani ◽  
Sumathy Subramanian
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Internet Technology ◽  
User Generated Content ◽  
Spam Detection ◽  
Performance Factors ◽  
Pros And Cons ◽  
Opinion Spam ◽  
And Performance

: With the vast development of internet technology 2.0, millions of people are sharing their opinions on different social networking sites. To obtain the necessary information from the huge volume of user-generated data, the attention on sentiment analysis among the research community is growing. Growth and prominence of sentiment analysis is synchronized with an increase in social media and networking sites. Users generally use natural language for speaking, writing, and expressing their views based on various sentiment orientations, ratings, and the features of different products, topics, and issues. This helps to produce ambiguity at the end of the customer's decision based on criticism to form an opinion based on such comments. To overcome the challenges of user-generated content such as noisy, irrelevant information and fake reviews, there is a significant demand for an effective methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies and highlights the challenges and performance factors of various approaches of sentiment analysis including text preprocessing, opinion spam detection, and aspect level sentiment analysis. Background: User-generated content is growing all over the globe and people more eagerly express their views on social media towards various aspects. The opinionated text is difficult to interpret and arrive at a conclusion based on the feedback gathered from reviews on various sites. Hence, the significance of sentiment analysis is growing to analyze the usergenerated data. Objective: The paper presents an exhaustive review that provides an overview of the pros and cons of the existing techniques and highlights the current techniques in sentiment analysis namely text pre-processing, opinion spam detection, and aspect level sentiment analysis based on machine learning and deep learning. This will be useful to researchers who focus on the challenges very specifically and identify the most common challenges to work forward for a new solution.

scholarly journals VADER Sentiment Analysis without and with English Punctuation Marks

2021 ◽  
Vol 10 (2) ◽  
pp. 1483-1488
Keyword(s):  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
English Language ◽  
Negative Polarity ◽  
Analysis Tool ◽  
Positive Polarity ◽  
Quotation Mark ◽  
Question Mark ◽  
Full Stop

Sentiment analysis is the foremost task in Natural Language Processing to understand the user’s attitude (positive, neutral, or negative) by capturing their thoughts, opinions, and feeling about a particular product. This helps companies to fulfill customer satisfaction and make better future decisions about the product. Various techniques have been used in the literature forsentiment analysis, such as polarity scores, classifications, and automated sentiment analysis. In this paper, Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis tool has been employed on a Twitter dataset (downloaded from https://www.kaggle.com). The study aims to measure the performance of VADER sentiment while concatenating fourteen English language punctuations marks, including Exclamation (!), Comma (,), Full Stop (.), Question Mark (?), Round Brackets (), Curly Brackets {}, Square Brackets [], Colon (:), Apostrophe (‘), Dash (-), Hyphen (--), Semi-Colon (;), Slash (/), Quotation Mark (“ ”) and to observe whether the polarity (positive, neutral and negative) of a sentence changes or remains the same. After the analysis, the study found that Exclamation (!) maximizes the average positive polarity and average negative polarity and lowers the average neutralpolarity. The Hyphen (--) and Comma (,) increase the average positive and neutral polarity and decrease the aver-age negative polarity. For Round Brackets (), Curly Brackets {}, Square Brackets [], Colon (:), Apostrophe (‘), Dash (-), Semi-Colon (;), Slash (/) and Full Stop (.) the average positive and average neutral polarity decreases and average negative polarity increases.

scholarly journals LITERASI MEDIA UNTUK MENGANTISIPASI BERITA PALSU (HOAX) DI MEDIA SOSIAL BAGI MASYARAKAT PULAU TIDUNG KEPULAUAN SERIBU

Sarwahita ◽  
2019 ◽  
Vol 16 (01) ◽  
pp. 68-74
Author(s):  
Aulia Rahmawati ◽  
Krisanjaya Krisanjaya
Keyword(s):  
Social Media ◽  
Media Literacy ◽  
Active Form ◽  
Public Understanding ◽  
Fake News ◽  
Average Value ◽  
A Value ◽  
Post Test ◽  
Value Range ◽  
Thousand Islands

Community Service is carried out by donating Training to Anticipate Fake News (Hoax) on Social Media, which is a pro-active form and participation of Universities in the Thousand Islands Police Resort program through the local government in overcoming the problem of media literacy (dissemination of hoax news), so that people can distinguish which information is correct and which information is fake or hoax so that the acceleration and effectiveness of development programs can be achieved which is marked by the better quality of public understanding related to false information (hoax) on social media. Media Literacy Training Anticipating Fake News (Hoax) in Social Media uses the Empowering 8 (E8) model approach. Post test results from 26 participants who took part in the training, there was a significant increase in value, namely as many as 26.56 points from the average value of the pre test value of 55 points with a value range of 20 to 70 points, to 74.56 points with a value range of 60 to 80 points Pengabdian kepada masyarakai ini dilaksanakan dengan mengadakan Pelatihan Literasi Media Mengantisipasi Berita Palsu (Hoax) Di Media Sosial, yang merupakan wujud pro aktif  dan partisipasi Perguruan Tinggi terhadap program Polres Kepulauan Seribu melalui pemerintah setempat dalam mengatasi persoalan literasi media (penyebaran berita hoax), agar masyarakat bisa membedakan mana informasi yang benar dan mana informasi yang palsu atau hoax sehingga dapat tercapai akselerasi dan efektivitas program pembangunan yang ditandai oleh semakin baiknya kualitas pemahaman masyarakat terkait informasi yang palsu (hoax) pada media sosial. Pelatihan Literasi Media Mengantisipasi Berita Palsu (Hoax) Di Media Sosial menggunkan pendekatan model Empowering 8 (E8). Hasil post test dari 26 peserta yang mengikuti pelatihan, terdapat peningkatan nilai yang cukup signifikan, yakni sebanyak 26.56 poin dari nilai rata-rata nilai pretest adalah 55 poin dengan rentang nilai 20 s.d 70 poin, menjadi 74.56 poin dengan rentang nilai  60 s.d 80 poin

Human Event-Related Potentials to Higher-Order Acoustic Spatial Changes

2002 ◽  
Vol 16 (2) ◽  
pp. 114-118 ◽  
Author(s):  
Timo Ruusuvirta ◽  
Heikki Hämäläinen
Keyword(s):  
Working Memory ◽  
Mismatch Negativity ◽  
Spatial Information ◽  
Free Field ◽  
Event Related Potentials ◽  
Negative Polarity ◽  
Positive Polarity ◽  
Spatial Changes ◽  
Related Potentials ◽  
Human Event

Abstract Human event-related potentials (ERPs) to a tone continuously alternating between its two spatial loci of origin (middle-standards, left-standards), to repetitions of left-standards (oddball-deviants), and to the tones originally representing these repetitions presented alone (alone-deviants) were recorded in free-field conditions. During the recordings (Fz, Cz, Pz, M1, and M2 referenced to nose), the subjects watched a silent movie. Oddball-deviants elicited a spatially diffuse two-peaked deflection of positive polarity. It differed from a deflection elicited by left-standards and commenced earlier than a prominent deflection of negative polarity (N1) elicited by alone-deviants. The results are discussed in the context of the mismatch negativity (MMN) and previous findings of dissociation between spatial and non-spatial information in auditory working memory.

FACEBOOK SEBAGAI MEDIA BARU TAFSIR AL-QUR’AN DI INDONESIA

2018 ◽  
Vol 2 (2) ◽  
pp. 69-80
Author(s):  
Wildan Imaduddin Muhammad
Keyword(s):  
Social Media ◽  
Methodological Problem ◽  
The Self ◽  
Internet Technology ◽  
Basic Character ◽  
Hermeneutic Approach ◽  
The Times ◽  
Qur'anic Interpretation

This article analyzes the product of Salman Harun's Qur'anic  interpretation with  Facebook  as the medium. As one of the senior professors who pursue the field of interpretation, he has managed to follow the times by utilizing internet technology. There are two focus areas in the study; the first aspect of the sense of Indonesian tafsir attached to the self of Salman Harun, the two aspects of the novelty of discourse that became the basic character of social media. Both aspects are interesting to be studied with a hermeneutic approach. Given that  the  methodological problem that often arises from the hermeneutic approach is the context of the interpreter that is difficult to trace accurately, then this article finds its relevance to the case of Salman Harun's interpretation which uses the facebook media as the actualization of its interpretation product.

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