scholarly journals Analysis of Public Opinion on Religion and Politics in Indonesia using K-Means Clustering and Vader Sentiment Polarity Detection

2020 ◽  
Vol 3 ◽  
pp. 749-754
Author(s):  
Tanzilal Mustaqim
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Data Clustering ◽  
Visual Analysis ◽  
Religion And Politics ◽  
Analysis Data ◽  
Analysis Process ◽  
Processing Data ◽  
Public Media

Religion and politics are two things that are closely related to each other and cannot be separated. Various public responses expressed by various public media such as print media and social media that can be classified as positive, neutral and negative, one of which is using Twitter. Twitter is a microblogging social media that contains many writings with many types from various types of users including posts that contain opinions about religion and politics. This research conducted an analysis process in the form of extraction of hidden insight data, visual analysis and sentiment analysis of public opinion related to religion and politics. The analysis was conducted on 5433 datasets written on Twitter on November 12, 2019. The analysis process began with data pre-processing, data clustering and sentiment analysis. Pre-processing data generates clean data from characters and non-essential data for use in the process of data clustering and sentiment analysis. Data clustering produces extraction of hidden insight data using k-means clustering. Sentiment data analysis uses vader sentiment polarity detection to determine dataset sentiments. The results of tests carried out using jupyter notebook show insight data hidden in the form of 50 unique words that are divided into 5 clusters of 10 words each then the sentiment analysis process is carried out in each cluster. Another result is visual analysis in the form of word cloud and hashtag clustering which shows the dominant words of each piece of data according to sentiment and word count. Also pointed out words that have a frequency of dominant emergence accompanied by word sentiments. The process of analyzing public opinion datasets related to religion and politics using k-means clustering and vader polarity detection sentiments can be done well.

scholarly journals Public Sentiment Analysis of Online Transportation in Indonesia through Social Media Using Google Machine Learning

2021 ◽  
pp. 153
Author(s):  
Desak Ayu Savita ◽  
I Ketut Gede Darma Putra ◽  
Ni Kadek Dwi Rusjayanthi
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Level Of Service ◽  
Public Sentiment ◽  
Analysis Process ◽  
Transportation Sector ◽  
Negative Sentiment

Public opinion is important to agencies or parties in particular fields, as it may indicate a tendency of public's view towards something (such as an object or process). One of them is in the transportation sector. Transportation has become a necessity for the community, many things more effective and efficient online, so that online transportation becomes important for society. The proliferation of online transportation, caused citizens to express opinions through social media. It is important to know the level of service of online transportation considering the large number of users, so that it can be used as a basis for improvement. One of the methods public opinion in social media is by sentiment analysis. The study used the help of Google Machine Learning for the sentiment analysis process that can produce 82,6% of accuracy number, 82,2% of precision, 83,3% of recall with the most sentiment result indicate to public opinion falls into the negative sentiment category for Gojek companies in media social of Twitter.

scholarly journals A Crowdsourcing Based Framework for Sentiment Analysis

2020 ◽  
Vol 16 (4) ◽  
pp. 285-295
Author(s):  
Fatima Zohra Ennaji ◽  
Abdelaziz El Fazziki ◽  
Hasna El Alaoui El Abdallaoui ◽  
Hamada El Kabtane
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Real Life ◽  
Data Sources ◽  
Valuable Data ◽  
Analysis Process ◽  
Validity Check ◽  
Online Platforms ◽  
Majority Consensus

As social networking has spread, people started sharing their personal opinions and thoughts widely via these online platforms. The resulting vast valuable data represent a rich source for companies to deduct their products’ reputation from both social media and crowds’ judgments. To exploit this wealth of data, a framework was proposed to collect opinions and rating scores respectively from social media and crowdsourcing platform to perform sentiment analysis, provide insights about a product and give consumers’ tendencies. During the analysis process, a consumer category (strict) is excluded from the process of reaching a majority consensus. To overcome this, a fuzzy clustering is used to compute consumers’ credibility. The key novelty of our approach is the new layer of validity check using a crowdsourcing component that ensures that the results obtained from social media are supported by opinions extracted directly from real-life consumers. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The obtained results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design.

scholarly journals Participatory Social Sensor: A Framework to Social Media Data Acquisition and Analysis

2019 ◽  
Author(s):  
Igor Araujo ◽  
Paulo Henrique Lopes Rettore ◽  
João Guilherme Maia de Menezes
Keyword(s):  
Social Media ◽  
Data Acquisition ◽  
Visual Analysis ◽  
Social Behaviors ◽  
Data Access ◽  
Heterogeneous Data ◽  
Social Media Data ◽  
Analysis Process ◽  
Media Data ◽  
Social Sensor

Nowadays, understanding urban mobility, transit, people viewpoint, and social behaviors has been the focus of many research and investments. However, data access is restricted to private companies and governments. In addition, the costs to create a sensor infrastructure on a given area is prohibitive. Then, using Location-Based Social Media (LBSM) may provide a new way to better comprehend the social behaviors, by the use of a users viewpoint. In this work, we propose the use of LBSM as participatory sensing, designing the Participatory Social Sensor (PSS), a friendly framework to social media data acquisition and analysis. We develop the Twitter data acquisition and analysis process, aiming to achieve the user application goals through a file setup,where the user specifies the spatial area, temporal interval, tags, and other parameters. As a result, the PSS shows a set of visual analysis which provides a context overview, allowing an easy way to researchers make-decision. A case study, Detection and Enrichment Service for Road Events Based on Heterogeneous Data Merger for VANETs, based on PSS framework was published in the current conference.

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.  

Tweet Sentiment Analysis Regarding the Brazilian Stock Market

2019 ◽  
Author(s):  
Murilo C. Medeiros ◽  
Vinicius R. P. Borges
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Visual Analysis ◽  
Vector Space Model ◽  
Principal Component ◽  
Sentiment Classification ◽  
Space Model ◽  
Analysis Process

This paper describes a methodology for analyzing sentiments and for knowledge discovery in tweets regarding the Brazilian stock market. The proposed methodology starts by preprocessing and characterizing tweets to obtain an associated vector-space model. After that, a dimensionality reduction is em- ployed by using Principal Component Analysis and t-Stochastic Neighbor Embedding. Sentiment analysis of stock market tweets is performed by considering the tasks of sentiment classification, topic modeling and clustering, along with a visual analysis process. Experiments results showed satisfactory performances in single and multi-label sentiment classification scenarios. The visual analysis process also revealed interesting relationships among topics and clusters.

scholarly journals Sentiment Analysis of Perpustakaan Nasional Republik Indonesia Through Social Media Twitter

MATICS ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 90
Author(s):  
Fakhris Khusnu Reza Mahfud
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
User Needs ◽  
Twitter Data ◽  
Knn Classification ◽  
National Libraries

The library is a gate of science and a heart of civilization. Indonesia already has a Perpustakaan Nasional consisted of 27 floors and is equipped with facilities that are adequate for user needs. Apart from that, we need to see opinions from the community as users. Public opinion about the library is critical for library managers to evaluate services and facilities from the library. One way to find out the views of the community is by using social media twitter. Twitter social media is often used in channelling opinions or expressing opinions about specific topics; besides social media, twitter is commonly used for digital campaign movements. Submission of views and even digital campaigns on Twitter social media greatly influence the opinions and even behaviour of society in various ways. This study analyzes tweets about national libraries by classifying, positive opinions, negative opinions and neutral opinions. In this study, twitter data will go through the preprocessing, weighting, and classification stages. TF-IDF and TF binary are used in weighting in this study. The classification used in this study is Naive Bayes and KNN. Accuracy, precision, and recall values were also used in this study to evaluate classification performance. The highest classification performance using KNN classification with TF-IDF weighting resulted in the value of accuracy, precision, and recall of 83.33%, 79.2%, and 83.3% respectively.

scholarly journals Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter

2019 ◽  
Vol 9 (1) ◽  
pp. 53
Author(s):  
Nfn Bahrawi
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Relevant Information ◽  
Processing Technique ◽  
Web Based ◽  
Analysis Process ◽  
Analysis System

<p class="JGI-AbstractIsi">Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.</p>

scholarly journals Literature review on Real-time Location-Based Sentiment Analysis on Twitter

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Dilmini Rathnayaka ◽  
Pubudu K.P.N Jayasena ◽  
Iraj Ratnayake
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Sentiment Analysis ◽  
Real Time ◽  
Analysis Data ◽  
Time Data ◽  
Twitter Data ◽  
Analysis Methods ◽  
Real Time Data ◽  
Twitter Data Analysis

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

scholarly journals Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter

2020 ◽  
Vol 17 (22) ◽  
pp. 8701 ◽  
Author(s):  
Wen Shi ◽  
Diyi Liu ◽  
Jing Yang ◽  
Jing Zhang ◽  
Sanmei Wen ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Healthcare System ◽  
Health Risks ◽  
Emotional Support ◽  
Social Distancing ◽  
The Us ◽  
Almost All

During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity—positive or negative—for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans’ emotions, rather than to trigger humans’ amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.

Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine

2019 ◽  
Vol 15 (3) ◽  
pp. 275-283 ◽  
Author(s):  
Iana Sabatovych
Keyword(s):  
Social Media ◽  
Social Movements ◽  
Public Opinion ◽  
Prediction Model ◽  
Sentiment Analysis ◽  
Social Engagement ◽  
Naive Bayes ◽  
Public Attitudes ◽  
Social Media Platforms ◽  
Online Prediction

A wide variety of social media platforms have become integral to contemporary forms of social engagement, including mass protests. Twitter is considered specifically indicative of public attitudes in this regard. This study attempts to examine the feasibility of using Twitter sentiment analysis to predict the 2014 revolution in Ukraine. Tweets representing public opinion are clustered by means of the ‘StreamKM++’ algorithm into three classes (likely, neutral and unlikely). The resulting prediction model for the three classes (using Naïve Bayes) was 96.75 per cent. As such, this study offers a promising way to perform an online prediction of social movements.

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