scholarly journals Analisis Pro Kontra Vaksin Covid 19 Menggunakan Sentiment Analysis Sumber Media Sosial Twitter

2021 ◽  
Vol 2 (1) ◽  
pp. 34-42
Author(s):  
I Wayan Desta Gafatia ◽  
Novri Hadinata
Keyword(s):  
Information Technology ◽  
Social Media ◽  
Clinical Trials ◽  
Sentiment Analysis ◽  
Rapid Growth ◽  
Government Policies ◽  
Bayes Method ◽  
Basic Emotions ◽  
Opinion Analysis ◽  
Twitter Users

The development of information technology today has experienced very rapid growth. One of the developments in information technology, namely social media such as Twitter, Facebook, and Youtube, are some of the most popular communication media in today's society. Twitter is often used to express emotions about something, either praising or criticizing in the form of emotion. Human emotions can be categorized into five basic emotions, namely love, joy, sadness, anger, and fear. Twitter users' emotional tweets can be known as opinion or sentiment analysis (opinion analysis or sentiment analysis). Sentiment analysis is also carried out to see opinions or tendencies towards a problem or policy, whether they tend to have negative or positive opinions. The COVID-19 vaccine has become one of the discussions with a fairly high intensity on social media. Vaccine-related tweets have increased as government policies evolve. The responses of netizens also varied, ranging from clinical trials of vaccines, free vaccines, vaccine effectiveness, halal vaccines, to the implementation of vaccinations. This research produces a system that can analyze tweet sentiment related to the covid 19 vaccine in Indonesia where the tweet is obtained using the Twitter API. This system uses the Multinominal Naive Bayes method for the classification process.

Exploring user sentiment towards sponsorship and ambush marketing

2019 ◽  
Vol 20 (4) ◽  
pp. 583-602 ◽  
Author(s):  
Nick Burton
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Value Added ◽  
Statistical Modelling ◽  
Consumer Attitudes ◽  
Content Type ◽  
Event Marketing ◽  
Ambush Marketing ◽  
Ethics Research ◽  
Twitter Users

Purpose The purpose of this paper is to explore consumer attitudes towards ambush marketing and official event sponsorship through the lens of sentiment analysis, and to examine social media users' ethical responses to digital event marketing campaigns during the 2018 FIFA World Cup. Design/methodology/approach The study employed a sentiment analysis, examining Twitter users’ utilization of sponsor and non-sponsor promotional hashtags. Statistical modelling programme R was used to access Twitter’s API, enabling the analysis and coding of user tweets pertaining to six marketing campaigns. The valence of each tweet – as well as the apparent user motivation underlying each post – was assessed, providing insight into Twitter users’ ethical impressions of sponsor and ambush marketer activities on social media and online engagement with social media marketing. Findings The study’s findings indicate that consumer attitudes towards ambush marketing may be significantly more positive than previously thought. Users’ attitudes towards ambush marketing appear significantly more positive than previously assumed, as users of social media emerged as highly responsive to creative and value-added non-sponsor campaigns. Originality/value The findings affirm that sentiment analysis may afford scholars and practitioners a viable means of assessing consumer attitudes towards social marketing activations, dependent upon campaign objectives and strategy. The study provides a new and invaluable context to consumer affect and ambush ethics research, advancing sponsorship and ambush marketing delivery and social sponsorship analytical practice.

scholarly journals Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data (Preprint)

2020 ◽  
Author(s):  
Yankun Gao ◽  
Zidian Xie ◽  
Dongmei Li
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Viral Infections ◽  
Application Programming Interface ◽  
Electronic Cigarette ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Twitter Users ◽  
Public Health Practitioners

BACKGROUND Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic. OBJECTIVE In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data. METHODS The study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups. RESULTS The US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic. CONCLUSIONS Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

scholarly journals Analysis of User Sentiment of Twitter to Draft KUHP

2020 ◽  
Vol 16 (3) ◽  
pp. 273
Author(s):  
Nawang Indah Cahyaningrum ◽  
Danty Welmin Yoshida Fatima ◽  
Wisnu Adi Kusuma ◽  
Sekar Ayu Ramadhani ◽  
Muhammad Rizqi Destanto ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
The Other ◽  
Mining Methods ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Bahasa Indonesia

Twitter is one of social media where its user can share many responses for a phenomenon through a tweet. This research used 5000 tweets from Twitter users in Bahasa Indonesia with keyword “RUU KUHP(Draft Law of KUHP)” from 16th of September until 22nd of September 2019. That tweets were processed using Rstudio software with sentiment analysis that is one of Text Mining methods. This research aims to classify Twitter users’ responses to RUU KUHP to be negative sentiment, poisitive negative, and neutral. Also, this research also aims to know about topics’ frequencies that were related to RUU KUHP through visualization with bar plot and also wordcloud. This research also aims to know words that are associated with the most frequent words. Form this research, can be known that Twitter users’ responses to RUU KUHP tend to have neutral sentiment that means they did not take side between agreeing or disagreeing. From this research, also can be known about 10 most frequent words, there are kpk, tunda, dpr, pasal, kesal, jokowi, presiden, masuk, ya, and sahkan. Beside that, can be known the other words that are associated with them and also their probability.

scholarly journals Monitoring Twitter Conversations for Targeted Recruitment in Cancer Trials in LA County: Protocol for a Mixed-Methods Pilot Study (Preprint)

2018 ◽  
Author(s):  
Katja Reuter ◽  
Praveen Angyan ◽  
NamQuyen Le ◽  
Alicia MacLennan ◽  
Sarah Cole ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Social Media ◽  
Clinical Trials ◽  
Pilot Study ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Translational Science ◽  
The Social ◽  
Media Intervention ◽  
Twitter Users

BACKGROUND Insufficient recruitment of participants remains a critical roadblock to successful clinical research, in particular clinical trials. Social media (SM) provides new ways for connecting potential participants with research opportunities. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues and increasing enrollment in cancer clinical trials. However, there is a lack of evidence that Twitter offers practical utility and impact. OBJECTIVE The objective of this pilot study is to examine the feasibility and impact of using Twitter monitoring data (i.e., user activity and their conversations about cancer-related conditions and concerns expressed by Twitter users in LA County) as a tool for enhancing clinical trial recruitment at a comprehensive cancer center. METHODS We will conduct a mixed-methods interrupted time series study design with a before and after SM recruitment intervention. Based on a preliminary analysis of eligible trials, we plan to onboard at least 84 clinical trials across six disease categories: breast cancer, colon cancer, kidney cancer, lymphoma, non-small cell lung cancer, and prostate cancer that are open to accrual at the USC Norris Comprehensive Cancer Center (USC Norris). We will monitor messages about the six cancer conditions posted by Twitter users in LA County. Recruitment for the trials will occur through the Twitter account (@USCTrials). Primary study outcomes include, first, feasibility and acceptance of the social media intervention among targeted Twitter users and the study teams of the onboarded trials, which will be assessed using qualitative interviews and 4-point Likert scale, and calculating the proportion of targeted Twitter users who engaged with outreach messages. Second, impact of the social media intervention will be measured by calculating the proportion of people who enrolled in trials. The enrollment rate will be compared between the active intervention period and the prior 10 months as historical control for each disease trial group. RESULTS This study has been funded by the National Center for Advancing Translational Science (NCATS) through a Clinical and Translational Science Award (CTSA) award. Study approval was obtained from the Clinical Investigations Committee (CIC) at USC Norris and the Institutional Review Board (IRB) at USC. Recruitment on Twitter started in February 2018. Data collection will be completed in November 2018. CONCLUSIONS This pilot project will provide preliminary data and practical insight into the application of publicly available Twitter data to identify and recruit clinical trial participants center across six cancer disease types. We will shed light on the acceptance of the SM intervention among Twitter users and study team members of the onboarded trials. If successful, the findings will inform a multisite, randomized controlled trial to determine the efficacy of the social media intervention across different locations and populations.

scholarly journals Sentiment Analysis of Face-To-Face Learning Based on Social Media

2021 ◽  
Vol 4 (3) ◽  
pp. 102-106
Author(s):  
Hendra Saputra Batubara ◽  
Ambiyar Ambiyar ◽  
Syahril Syahril ◽  
Fadhilah Fadhilah ◽  
Ronal Watrianthos
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Health Professionals ◽  
Research Data ◽  
Bayes Method ◽  
Negative Attitudes ◽  
Face To Face ◽  
Education And Health ◽  
Face To Face Learning

The use of restricted face-to-face learning during the epidemic in Indonesia was discussed not just by education and health professionals, but also on social media. The study used the Twitter dataset with the keywords 'school' and 'face-to-face' to examine public opinion about face-to-face learning. The research data was obtained from Twitter utilizing Drone Emprit Academic, and it was then processed using the Naive Bayes method to create sentiment analysis. During that time, research revealed that 32% of people were positive, 54% were negative, and 14% were indifferent. Because of worries about the dangers associated with the use of face-to-face learning, negative attitudes predominate.  

scholarly journals A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

2021 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
Amrita Mishra ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Comprehensive Analysis ◽  
Social Media Data ◽  
The Public ◽  
Opinion Analysis ◽  
Twitter Data ◽  
Media Data

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.

scholarly journals IMPLEMENTATION OF BACKPROPAGATION NEURAL NETWORK IN SENTIMENT ANALYSIS ON TWITTER TO PUBLIC FIGURES

Compiler ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 101
Author(s):  
Achmad Safruddin ◽  
Arief Hermawan ◽  
Adityo Permana Wibowo
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Sentiment Analysis ◽  
Test Data ◽  
Text Processing ◽  
Correct Classification ◽  
Backpropagation Neural Network ◽  
Public Figures ◽  
Twitter Users ◽  
Topic Sentiment

Sentiment analysis is a process for identifying or analyzing people's opinions on a topic. Sentiment analysis analyzes each word in a sentence to find out the opinions or sentiments expressed in the sentence. The opinions expressed can be in the form of positive or negative opinions. Twitter is one of the most popular social media in Indonesia. Twitter users always discuss various kinds of topics every day. One of the things discussed on Twitter and which has become a trending topic several times is about public figures. This study discusses the analysis of positive or negative sentiments towards public figures based on tweet data carried out by text processing. The results of text processing are classified using a backpropagation neural network. Tests were carried out using 69 test data, resulting in an accuracy of 62.3%, with 43 correct classification results.

scholarly journals Evolution of corporate reputation during an evolving controversy

2019 ◽  
Vol 23 (1) ◽  
pp. 52-71 ◽  
Author(s):  
Siyoung Chung ◽  
Mark Chong ◽  
Jie Sheng Chua ◽  
Jin Cheon Na
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Corporate Reputation ◽  
Supervised Machine Learning ◽  
Future Research ◽  
Content Type ◽  
Twitter Users ◽  
Corporate Crisis ◽  
The Impact

PurposeThe purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.Design/methodology/approachUsing a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.FindingsThe findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.Research limitations/implicationsEven with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.Practical implicationsFirst, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.Originality/valueThis study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

scholarly journals DESIGN OF PEOPLE PROFILING AND MODELING REPUTATION COMPUTATION BASED ON SENTIMENT ANALYSIS

SINERGI ◽  
2019 ◽  
Vol 23 (1) ◽  
pp. 1
Author(s):  
Ahmad Mafazi Damanhuri ◽  
Zhang Huaping
Keyword(s):  
Information Technology ◽  
Social Media ◽  
Decision Support ◽  
Sentiment Analysis ◽  
Poor People ◽  
Famous People ◽  
Access Information

The number of popular people is still growing because of the easiness to access information technology. Every time people upload things and let people watch it and give it a like or comment. People who can impress other people will grow their popularity and fame. Some famous people make influences, help poor people with powers, and others are causing troubles. Community these days drives people perspective by share their thoughts on social media. They spread information and makes others want to see things they are talked about. Troublesome popular people defended by their fan base and attacked by other communities. By these cases, the research tried to gather information on social media and used it for calculation and profiling. The method that proposed to rely on this information is based on sentiment analysis to look up someone’s record and listing them into top 10 best got from DBpedia. This system shows the list of people and contains all important record about that person which can be used for decision support for a policy or rewarding people. The results have successfully visualized the output in the list of people with any further details following by clicking their names.

scholarly journals Implementasi Algoritma Klasifikasi Terhadap Tweet Pornografi Kaum Homoseksual Pada Twitter

2020 ◽  
Vol 6 (2) ◽  
pp. 204-212
Author(s):  
Taopik Hidayat ◽  
Rangga Pebrianto ◽  
Risca Lusiana Pratiwi ◽  
Windu Gata ◽  
Daniati Uki Eka Saputri
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Personal Relationships ◽  
Same Sex ◽  
The Social ◽  
Twitter Users ◽  
Negative Impacts

Abstract: Twitter is one of the social media with the number of users who reach millions of users. The number of Twitter users in 2019 increased by 17 percent in 2018 to 145 million users with a variety of good both positive and bad. The negative impacts that occur such as the spread of status, images, and videos that affect pornography especially among freedom groups. Homosexuals are sexually oriented people who like the same sex that occurs in men, the rejection often experienced by men makes one of the reasons intellectuals use Twitter social media to show their personal relationships, open to each other, socializing with same sex, looking for conversation, to become a place to find a partner. The purpose of this study is to determine the positive and negative sentiments to determine the level of accuracy of intellectual pornography tweets in Indonesia from data taken from Twitter tweets by using the TF-IDF and k-NN methods. The results of this study get an accuracy value of 88.25% containing pornography and the remaining 11.75% not containing pornography will contain news, news, and other information.Keywords: homosexual, sentiment analysis, twitterAbstrak: Twitter merupakan salah satu media sosial dengan jumlah pengguna mencapai jutaan pengguna. Jumlah pengguna Twit-ter pada tahun 2019 dicatat meningkat 17 persendari tahun 2018 menjadi 145 juta pengguna dengan berbagai dampak baik dampak positif maupun dampak negatif. Dampak negatif yang ditimbulkannya seperti penyebaran status, gambar, dan video yang bersifat pornografi khsusunya di kalangan kaum homoseksual. Homoseksual merupakan orang yang berorientasi seksual sebagai penyuka sesama jenis yang terjadi pada kaum pria, Penolakan yang sering dialami kaum homoseksual men-jadikan salah satu alasan kaum homoseksual menggunakan media sosial Twitter untuk menunjukkan identitas diri mereka, saling terbuka, bersosialisasi dengan sesama jenis, mencari penghasilan, hingga menjadi ajang pencarian pasangan. Tujuan dari penelitian ini adalah untuk mengetahui sentimen positif dan negatif untuk mengetahui tingkat akurasi terhadap tweet pornografi kaum homoseksual di Indonesia dari data yang diambil dari tweet Twitter dengan menggunakan metode TF-IDF dan k-NN. Hasil penelitian ini mendapatkan nilai accuracy sebesar 88,25% mengandung unsur pornografi dan sisanya sebesar 11,75 tidak mengandung unsur pornografi akan tetapi berisi iklan, berita, dan informasi lainnya.Kata kunci: homoseksual, sentimen analisis, twitter

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