scholarly journals Sentiment Analysis Of Traveloka App Using Naïve Bayes Classifier Method

2019 ◽  
Author(s):  
Ronal Watrianthos
Keyword(s):  
Public Opinion ◽  
Service Quality ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
User Experiences ◽  
A Value ◽  
Positive Sentiment

Traveloka is currently the most popular startup in Indonesia with share traffic reaching 78.49% using smartphone and monthly visits whichreached 28.92 million based on a report in similarweb.com in May 2019. Traveloka, based on record, has been downloaded 10 million times since 2014with rating reaches 4.4 out of 5 stars. As of May 2019, there were 386,646 reviews from users in the PlayStore, ranging from positive and negativereviews. However, it is necessary to analyze with certain methods to summarize the review. Every review given will get a conclusion after collected, andsentiment analysis will provide user experiences from the Traveloka application within certain period. This research was conducted using the NaïveBayes Classifier method based on a review from the playstore to determine service quality. The purpose of this study is to find out the perceptions ofusers based on the measurement of service quality so that the results can be an evaluation for Traveloka in improving services. Studies show that duringthis period public opinion produced negative sentiments with Vmap value of 0.31020 greater than positive sentiment with a value of 0.16132.

scholarly journals Analisis Sentimen Masyarakat Terhadap Pilpres 2019 Berdasarkan Opini Dari Twitter Menggunakan Metode Naive Bayes Classifier

2020 ◽  
Vol 1 (3) ◽  
pp. 185-199
Author(s):  
Khoirul Zuhri ◽  
Nurul Adha Oktarini Saputri
Keyword(s):  
Sentiment Analysis ◽  
Hate Speech ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
The Public ◽  
Positive Sentiment

Twitter is a social media that is currently popular, where the public is free to comment and write anything. It is not uncommon for the public to comment with harsh words and even hate speech. The 2019 presidential election drew many comments, some praised, criticized and insulted. To be able to dig up information and classify a text, sentiment analysis is needed. In this study, sentiment analysis is a process of classifying textual documents into two classes, namely negative and positive sentiment classes. Opinion data were obtained from the Twitter social network in the form of tweets. The data used was 3337 tweets consisting of 80% training data and 20% training data. Training data is data with known sentiment. This study aims to determine whether a tweet is a positive or negative tweet conveyed on Twitter in Indonesian. The classification of tweet data uses the naïve Bayes classifier algorithm. The classification results of the test data show that the Naïve Bayes Classifier algorithm provides an accuracy value of 71%. The accuracy value for each sentiment is 71% for positive sentiment and 70% for negative sentiment

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 Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 28-39
Author(s):  
Adri Priadana ◽  
Ahmad Ashril Rizal
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tourism Industry ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Trending Topics ◽  
The Government ◽  
Negative Sentiment

The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively.

Indonesian Twitter Sentiment Analysis Application on The Covid l9 Vaccine Using Naive Bayes Classifier

2021 ◽  
Author(s):  
Adhitia Erfina ◽  
Moneyta Dholah Rosita Ndk ◽  
Rahmat Hidayat ◽  
Aris Subagja ◽  
Haerul Ramadhan ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Analysis Application
Sign in / Sign up

Export Citation Format

Share Document