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 SENTIMENT ANALYSIS OF TWITTER DATA ON REMOTE LEARNING USING NAÏVE BAYES ALGORITHM

2021 ◽  
Vol 3 (3) ◽  
pp. 203-210
Author(s):  
Putri Rana Khairina ◽  
Desti Fitriati
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning System ◽  
Public Sentiment ◽  
Global Pandemic ◽  
Distance Learning System ◽  
Twitter Users ◽  
The Government ◽  
Bayes Algorithm

Covid-19 is widespread, resulting in a global pandemic. Distance Learning System (DLS) is considered as a solution but, the reality of the implementation of DLS is not in accordance with the expectations of the community. Many Twitter users wrote their opinions on DLS. The tendency of public sentiment can be used as a way to improve the existing education system in Indonesia and can be an input for the government in improving the DLS method that is being implemented. Thus, this study produced a system that can analyze tweet sentiment towards DLS. The tweet was obtained using the Twitter API. The method used is Naïve Bayes for the process of classification of positive, negative, and neutral sentiments using 600 data. Then, data sharing is done 80% data training and 20% data testing that will be in the text preprocessing first. The accuracy of sentiment analysis of DLS using the Naïve Bayes method using 3-fold Cross-Validation produces an average of 93%.

scholarly journals Implementasi Metode Klasifikasi Naive Bayes Pada Sistem Analisis Opini Pengguna Twitter Berbasis Web

2021 ◽  
Vol 10 (1) ◽  
pp. 46-54
Author(s):  
Apif Supriadi ◽  
Fatmasari
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Application Programming Interface ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Public Sentiment ◽  
Application Programming ◽  
Analysis System ◽  
Programming Interface

Abstract— Development of social media which is the result of technological development is an inseparable part of people's lives. Social media is a place where ordinary people express their feelings and opinions about something that concerns them. Inknowing the direction of public sentiment, surveys are usually done online or offline, this sentiment analysis system will facilitate and speed up the process of knowing the direction of public sentiment, in the case of research. This uses data from Twitter social media called tweets or tweets, web-based sentiment analysis system that will classify tweets into 3 (three) types of sentiments, namely positive, neutral and negative, then make a percentage to make it easier to see the direction of public sentiment. In classifying this system uses the Naive Bayes Classifier method and displays it in a web interface with the PHP programming language and uses the Application Programming Interface (API) to get data from Twitter. Intisari — Saat ini perkembangan media sosial yang merupakan hasil dari perkembangan teknologi menjadi bagian tak terpisahkan dari kehidupan masyarakat. Media sosial menjadi tempat masyarakat biasa mengutarakan berbagai perasaan dan opininya tentang suatu hal yang jadi perhatian mereka, dalam mengetahui arah sentimen masyarakat biasanya dilakukan survei baik secara online atau offline, sistem analisis sentimen ini akan memudahkan dan mempercepat proses mengetahui arah sentimen publik, dalam kasus penelitian ini menggunakan data dari media sosial Twitter yang disebut dengan tweets atau cuitan, sistem analisis sentimen berbasis web yang akan mengklasifikasikan cuitan kedalam 3 (tiga) jenis sentimen yaitu positif, netral dan negatif lalu melakukan persentasenya agar mempermudah melihat arah sentimen publik. Dalam melakukan klasifikasinya sistem ini menggunakan metode Naive Bayes Classifier dan menampilkannya dalam antarmuka web dengan bahasa pemrograman PHP dan menggunakan Application Programming Interface (API) dalam mendapatkan data dari Twitter.

scholarly journals Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

2021 ◽  
Vol 2 (2) ◽  
pp. 151
Author(s):  
Sulton Nur Hakim ◽  
Andika Julianto Putra ◽  
Annisa Uswatun Khasanah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Service Providers ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Total Accuracy

In the era of globalization, the internet has become a human need in doing various things. Many internet users are an opportunity for internet service providers, PT Telekomunikasi Indonesia (Telkom). One of PT Telkom's products is IndiHome. As the only state-owned enterprise engaged in telecommunications, PT Telkom is expected to meet the needs of the Indonesian people. However, based on the rating obtained by IndiHome products through the myIndiHome application on Google Play, it is 3.5 out of 87,000 more reviews. The reviews focus on how important the effect of word-of-mouth is on choosing and using internet provider products. The review data was collected on November 1, 2020 to December 15, 2020, with a total of 2,539 reviews as a sample.  The sentiment analysis process that has been carried out shows that the number of reviews included in the negative sentiment class was 1.160 reviews, and the positive class was 1.374 reviews out of a total of 2,539 reviews. The results indicate that service errors in IndiHome services are still quite high, reaching 46.7% as indicated by the number of negative reviews. The classification results show that the average value of the total accuracy of the Support Vector Machine (SVM) method is 86.54% greater than Naïve Bayes Classifier (NBC) method which has an average total accuracy of 84.69%.  Based on fishbone diagram analysis, there are 12nd problems on negative reviews that classify problems 5P factors: Price, People, Process, Place, and Product.

scholarly journals Community Opinion Sentiment Analysis on Social Media Using Naive Bayes Algorithm Methods

2019 ◽  
Vol 2 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Taqwa Hariguna ◽  
Vera Rachmawati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Central Java ◽  
Public Sentiment ◽  
Positive Class ◽  
Negative Class ◽  
Bayes Algorithm ◽  
F Measure

The election of Governor is an election event for the Regional Head for the future of the region and the country. The Central Java Governor election in 2018 was held jointly on 27 June 2018, which was followed by 2 candidate pairs of the governor. Its many responses from people through twitter's social media to bring up opinions from the public. Sentiment analysis of 2 research objects of Central Java Governor 2018 candidates with a total of 400 tweets with each candidate being 200 tweets. The used of tweets are divided into 3 classes: positive class, neutral class and negative class. In this study the classification process used the Naive Bayes Classifier (NBC) method, while for data preprocessing is using Cleansing, Punctuation Removal, Stopword Removal, and Tokenisation, to determine the sentiment class with the Lexicon Based method produces the highest accuracy in the Ganjar Pranowo dataset with an accuracy of 87,9545%, Precision value is 0.891%, Recall value is 0.88% and F-Measure is 0.851% while Sudirman Said dataset has an accuracy rate of 84.322%, Precision value of 0.867%, Recall value of 0.843% and F-Measure of 0.815%. From these results, we can conclude that the Ganjar Pranowo dataset was higher compared to Sudirman Said's dataset.

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.

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