scholarly journals Extraction Opinion of Social Media in Higher Education Using Sentiment Analysis

bit-Tech ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Thomas Edison Tarigan ◽  
Robby C Buwono ◽  
Sri Redjeki
Keyword(s):  
Higher Education ◽  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Naive Bayes ◽  
Training Data ◽  
Test Results ◽  
Bayes Classifier ◽  
Tertiary Institution ◽  
Management Performance

The purpose of this research is to extract social media Twitter opinion on a tertiary institution using sentiment analysis. The results of sentiment analysis will provide input to universities as a form of evaluation of management performance in managing institutions. Sentiment analysis generated using the Naïve Bayes Classifier method which is classified into 4 classes: positive, normal, negative and unknown. This study uses 1000 data tweets used for training data needs. The data is classified manually to determine the sentiment of the tweet. Then 20 tweet data is used for testing. The results of this study produce a system that can classify sentiments automatically with 75% test results for sentiment, some obstacles in processing real-time tweets such as duplicate tweets (spam tweets), Indonesian structures that are quite complex and diverse.

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 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 Analysis of Personality Characteristic Using the Naïve Bayess Classifier Algorithm (Case Study Official Twitter of Basuki Tjahaja Purnama's and Anies Baswedan)

2019 ◽  
Vol 7 (2) ◽  
pp. 14
Author(s):  
Ireicca Agustiorini Harsehanto ◽  
M. Didik R. Wahyudi
Keyword(s):  
Social Media ◽  
Big Five ◽  
Personality Characteristic ◽  
Training Data ◽  
Big Five Personality ◽  
Personality Theory ◽  
Test Results ◽  
Bayes Classifier ◽  
Testing Data

Abstract - This research uses data from social media Twitter based on the results of tweets from user_timeline @basuki_btp and @aniesbaswedan. This study uses 2100 tweet data. Data that has been collected is then pre-processed first and labeled manually. The next process is classification using the Naïve Bayess Classifier Algorithm using the Big Five Personality Theory. Based on the test results using 500 tweet data as training data and 1600 tweet data as testing data. The classification results obtained by using the Naïve Bayes Classifier Method and grouped in the "Big Five" personality groups: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism on tweet data in Indonesian.

scholarly journals Sentiment Analysis Berbasis Algoritma Naïve Bayes Classsifier untuk Identifikasi Persepsi Masyarakat Terhadap Produk / Layanan Perusahaan

2020 ◽  
Vol 5 (1) ◽  
pp. 126-135
Author(s):  
Affandy Affandy ◽  
Oktania Nandiyati
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Classifier ◽  
Use Of Data ◽  
Strategic Plans ◽  
Good Classification ◽  
Calculation Results ◽  
A Company

Twitter is the most popular microblogging service in Indonesia, with nearly 23 million users. In the era of big data such as the current tweets from customers, observers, potential consumers, or the community of users of products or services of a company will greatly help companies in knowing the industrial and consumer landscape, so as to determine strategic plans that will contribute to the company's growth. However, the use of data from social media such as Twitter is hampered by a number of technical difficulties in the process of collecting, processing, and analysing. Specifically, this research applies the Naïve Bayes Classifier algorithm in the process of sentiment analysis of tweets data into a prototype application that is intended to make it easier for companies / organizations to know people's perceptions of their products or services. The NBC algorithm was chosen because this algorithm is able to do a good classification even though it uses small training data, but has high accuracy and process speed for handling large training data. From the evaluation results found a prototype running well where the keywords entered will trigger the Twitter API to crawl the data then the mining process can be monitored at each stage and at the end of the process, the system will show the final sentiment level values and the representation of the calculation results log in a chart form over a certain period of time.

Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier

2020 ◽  
Vol 2 (1) ◽  
pp. 22
Author(s):  
Mohammad Zoqi Sarwani ◽  
Muhammad Shubkhan Salafudin ◽  
Dian Ahkam Sani
Keyword(s):  
Social Media ◽  
Big Five ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Surrounding Environment ◽  
Testing Data

With the development of social media trends among students by using Facebook social media, students can communicate and pour out everything that is felt in the form of status. Personality is the character or various characters of a person - therefore, how a person to adjust to the surrounding environment for the achievement of communication smoothly. In the personality category, many things classify a person's category in the psychologist theory. In this exercise, the Big Five, the psychologist theory, is described in five codes, namely Openness, Conscientiousness, Extraversion, Agreeables, Neuroticism. Naive Bayes Classifier is used to determine the highest probability value with the aim to determine the highest value. The data used are two namely training data and testing data obtained from the Facebook status of students. From the data obtained can be tested in the system that the accuracy value is 88%.

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

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