scholarly journals SENTIMEN ANALISIS TERHADAP KEBIJAKAN PEMBELAJARAN JARAK JAUH SELAMA PANDEMI COVID-19 MENGGUNAKAN ALGORITMA NAÏVE BAYES

2021 ◽  
Vol 14 (1) ◽  
pp. 51-56
Author(s):  
Darsono Nababan
Keyword(s):  
Social Media ◽  
Distance Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Ministry Of Education ◽  
The People ◽  
Positive Sentiment ◽  
Learning Policies ◽  
Negative Sentiment

Social media is a means to convey aspirations directly, but every aspiration is from social media users. Everyone who expresses opinions on social media contains positive, negative, and neutral sentiments. The implementation of the Ministry of Education and Culture's policy on the implementation of distance learning policies during the pandemic COVID-19 received various responses from the people of Indonesia. neutral as many as 894 comments, then 52 comments with negative sentiment, and 32 comments with positive sentiment with an accuracy value of 98.79%

scholarly journals Analisis Sentimen Persepsi Masyarakat Terhadap Pemilu 2019 Pada Media Sosial Twitter Menggunakan Naive Bayes

2020 ◽  
Vol 4 (3) ◽  
pp. 552
Author(s):  
Safitri Juanita
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Participation Rate ◽  
Naïve Bayes ◽  
Training Dataset ◽  
Community Perception ◽  
Positive Perception ◽  
Positive Sentiment ◽  
Negative Sentiment

According to the BAWASLU evaluation a variety of related negative content supports supporting prospective couples to burst into various social media pages. So sometimes the content leads to a hoax issue to the issue of religious and inter-group Racial (SARA). One of the social media used by the people of Indonesia is Twitter, according to Kompas.com number of Twitter daily users globally claimed to be increasing, this appears to be the 3rd Quarter Twitter Financial Report of 2019 on Twitter's 3rd quarter of 2019 Financial reports, daily active users on the Twitter platform are recorded to increase by 17 percent, to the number of 145 million users. So it is necessary that a sentiment analysis study can capture a pattern of community perception on social media Twitter against the 2019 elections and it is expected that this research can help interested parties to increase voter participation rate in the next 5 years. This research method uses the Indonesian tweet data taken from 16 April 2018-16 April 2019, further data in preprocessing, text transformation, stemming Bahasa Indonesia, specifying attribute class, load dictonary and a classification of Naive Bayes using Weka. The conclusion of this study was the classification of Naive Bayes finding that the 2019 election tweet dataset had a negative perception pattern of 52% much greater than the positive perception of 18% and the neutral perception had a value of 31% higher than positive perception. Naive Bayes ' degree of classification accuracy against the training dataset is 81% and the dataset testing 76%, the average precision value for positive sentiment is 86.65%, negative sentiment is 77.15%, and neutral sentiment is worth 80.95% while the average recall rate on positive sentiment is 36.8%, negative sentiment is 93.2% and the neutral sentiment is 86.8%

scholarly journals Analisis Sentimen Masyarakat Terhadap Virus Corona Berdasarkan Opini Masyarakat Menggunakan Metode Naïve Bayes Classifier

2021 ◽  
Vol 1 (4) ◽  
pp. 220-232
Author(s):  
Suhardiman Suhardiman ◽  
Fitri Purwaningtias
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Optimal Classifier ◽  
Conducting Research ◽  
Use Of Social Media ◽  
Negative Sentiment

The current use of social media is not only to communicate between friends, but is often also used as a means to convey an aspiration to the community, especially the Indonesian people regarding government issues, or problems related to health and other problems. One of the uses of this social media is to use it as a means of conveying digital aspirations, such as some slogans that are used as hashtags, namely #dirumahaja #lockdown, #usemasker, #protocol, #imun, #vaccine. From the slogan used as a hashtag, researchers are interested in conducting research on how much negative sentiment and positive sentiment there are, using the Naïve Bayes Classifier method, which is a machine learning method that uses probability calculations. The basic concept used by Nave Bayes is the Bayes Classifier Theorem, which is a theorem in statistics to calculate probability, the Bayes Optimal Classifier calculates the probability of one class from each existing attribute group, and determines which class is the most optimal, as for the advantages of using Nave Bayes Classifier in document classification can be viewed from the process that takes action based on existing data to provide solutions to these sentiments.

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

scholarly journals Trends in Patient, Physician, and Public Perception of Ulnar Collateral Ligament Reconstruction Using Social Media Analytics

2021 ◽  
Vol 9 (3) ◽  
pp. 232596712199005
Author(s):  
Jonathan S. Yu ◽  
James B. Carr ◽  
Jacob Thomas ◽  
Julianna Kostas ◽  
Zhaorui Wang ◽  
...  
Keyword(s):  
Social Media ◽  
Public Perception ◽  
Ulnar Collateral Ligament ◽  
Return To Play ◽  
Social Media Analytics ◽  
Care Providers ◽  
Collateral Ligament ◽  
The Past ◽  
Positive Sentiment ◽  
Negative Sentiment

Background: Social media posts regarding ulnar collateral ligament (UCL) injuries and reconstruction surgeries have increased in recent years. Purpose: To analyze posts shared on Instagram and Twitter referencing UCL injuries and reconstruction surgeries to evaluate public perception and any trends in perception over the past 3 years. Study Design: Cross-sectional study. Methods: A search of a 3-year period (August 2016 and August 2019) of public Instagram and Twitter posts was performed. We searched for >22 hashtags and search terms, including #TommyJohn, #TommyJohnSurgery, and #tornUCL. A categorical classification system was used to assess the sentiment, media format, perspective, timing, accuracy, and general content of each post. Post popularity was measured by number of likes and comments. Results: A total of 3119 Instagram posts and 267 Twitter posts were included in the analysis. Of the 3119 Instagram posts analyzed, 34% were from patients, and 28% were from providers. Of the 267 Twitter posts analyzed, 42% were from patients, and 16% were from providers. Although the majority of social media posts were of a positive sentiment, over the past 3 years, there was a major surge in negative sentiment posts (97% increase) versus positive sentiment posts (9% increase). Patients were more likely to focus their posts on rehabilitation, return to play, and activities of daily living. Providers tended to focus their posts on education, rehabilitation, and injury prevention. Patient posts declined over the past 3 years (–28%), whereas provider posts increased substantially (110%). Of posts shared by health care providers, 4% of posts contained inaccurate or misleading information. Conclusion: The majority of patients who post about their UCL injury and reconstruction on social media have a positive sentiment when discussing their procedure. However, negative sentiment posts have increased significantly over the past 3 years. Patient content revolves around rehabilitation and return to play. Although patient posts have declined over the past 3 years, provider posts have increased substantially with an emphasis on education.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

scholarly journals SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

2019 ◽  
Vol 15 (2) ◽  
pp. 247-254
Author(s):  
Heru Sukma Utama ◽  
Didi Rosiyadi ◽  
Dedi Aridarma ◽  
Bobby Suryo Prakoso
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
Toll Road ◽  
Textual Data ◽  
Bayes Algorithm

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

scholarly journals Analysis of Social Media Users Sentiments against Omnibus Law Based on Hashtags on Twitter

SISTEMASI ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 197
Author(s):  
Okta Fanny ◽  
Heri Suroyo
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Main Topic ◽  
Accuracy Score ◽  
Test Results ◽  
Bayes Classifier ◽  
The Public ◽  
Average Accuracy ◽  
Positive Sentiment ◽  
Negative Sentiment

From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

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