Sentiment Analysis of Ojek Online User Satisfaction Based on the Naïve Bayes and Net Brand Reputation Method

2021 ◽  
Author(s):  
Alam Rahmatulloh ◽  
Rahmi Nur Shofa ◽  
Irfan Darmawan ◽  
Ardiansah
Keyword(s):  
Sentiment Analysis ◽  
User Satisfaction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Brand Reputation

scholarly journals Naïve Bayes Twitter Sentiment Analysis In Visualizing The Reputation Of Communication Service Providers: During Covid-19 Pandemic

2021 ◽  
Vol 12 (5) ◽  
pp. 1753-1764
Author(s):  
Khyrinaairinfariza Abu Samah, Et. al.
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Service Providers ◽  
Naïve Bayes ◽  
Significant Error ◽  
Communication Service ◽  
The Core ◽  
Public Sentiment ◽  
Brand Reputation ◽  
Word Clouds

We present the real-world public sentiment expressed on Twitter using the proposed conceptual model (CM) to visualize the communication service providers (CSP) reputation during the Covid-19 pandemic in Malaysia from March 18 until August 18, 2020. The CM is a guideline that entails public tweets directly or indirectly mentioned to the three biggest CSP in Malaysia: Celcom, Maxis, and Digi. A text classifier model optimized for short snippets like tweets is developed to make bilingual sentiment analysis possible. The two languages explored are Bahasa Malaysia and English since they are the two most spoken languages in Malaysia. The classifier model is trained and tested on a huge multidomain dataset pre-labeled with the labels “0” and “1”, which resemble “positive” and “negative”, respectively. We used the Naïve Bayes (NB) technique as the core of the classifier model. Functionality testing has done to ensure no significant error that will render the application useless, and the accuracy testing score of 89% is considered quite impressive. We came out with the visualization through the word clouds and presented -56%, -42%, and -43% of Net Brand Reputation for Celcom, Maxis, and Digi.

scholarly journals Naïve Bayes Algorithm For Sentiment Analysis Windows Phone Store Application Reviews

SinkrOn ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 13 ◽  
Author(s):  
Normah Normah
Keyword(s):  
Sentiment Analysis ◽  
User Satisfaction ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Good Potential ◽  
Bayes Algorithm ◽  
And Performance ◽  
Windows Phone

Reading reviews helps consumers choose the applications, helping companies and developers monitor user satisfaction to improve quality of features and services, read overall and manually could spend the time and laborious, if read at a glance, information not conveyed perfectly. This study analyzes user sentiment Windows Phone Store applications by automatically classifying reviews into positive or negative opinion category. Naïve bayes has good potential because of its simplicity and performance as a model of classifying text on many domains. The model was evaluated using 10 Fold Cross Validation. Measurements were made with the Confusion Matrix and the ROC curve. The accuracy produced in this study is 84.50%, indicating that Naïve Bayes is a good model in classifying text especially in the case of sentiment analysis.

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.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

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.

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