scholarly journals Analisis Sentiment Tweets Berbahasa Sunda Menggunakan Naive Bayes Classifier dengan Seleksi Feature Chi Squared Statistic

2019 ◽  
Vol 4 (3) ◽  
pp. 87
Author(s):  
Yono Cahyono ◽  
Saprudin Saprudin
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Chi Square ◽  
Chi Squared ◽  
Use Of Social Media

At present the development of the use of social media in Indonesia is very rapid, in Indonesia there are a variety of regional languages, one of which is the Sundanese language, where some people especially those living in West Java use Sundanese language to express comments, opinions, suggestions, criticisms and others in social media. This information can be used as valuable data for individuals or organizations in decision making. The huge amount of data makes it impossible for humans to read and analyze it manually. Sentiment analysis is the process of classifying opinions, analyzing, understanding, evaluating, emotions and attitudes towards a particular entity such as individuals, organizations, products or services, topics, events, in order to obtain information. The purpose of this research is the Naїve Bayes Classifier (NBC) classification algorithm and Feature Chi Squared Statistics selection method can be used in Sundanese-language tweets sentiment analysis on Twitter social media into positive, negative and neutral categories. Chi Square Statistic feature test results can reduce irrelevant features in the Naïve Bayes Classifier classification process on Sundanese-language tweets with an accuracy of 78.48%.

scholarly journals Analisis Sentimen Masyarakat Berdasarkan Opini dari Sosial Media Menggunakan Metode Naive Bayes Classifier (Study Kasus : Universitas Sjakhyakirti)

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Paisal Paisal
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Mining Operations ◽  
Use Of Social Media

<p class="SammaryHeader" align="center"><strong>Abstract</strong></p><p><em>The use of social media today is not only to communicate between friends, but also is needed to make facilities to convey the aspirations of certain people in Indonesia about legal issues relating to government and other issues. One of the aspirations conveyed through social media is a hash that is widely seen by one of the Sjakhyakirti University from the use of social media. Then there arises a lot of sentiment from every community, there are those that give positive sentiments and also negative sentiments that can have a good or bad impact on daily life. days in the community. Some reasons for positive and negative sentiments sourced from this social media, will use social media. From this debate the researchers found a solution where this hashtag can provide good results for the general public or vice versa. In analyzing this, the researcher uses the Naïve Bayes Classifier method which is one of the machine learning methods that uses calculations, the classification of automated hashes can help minimize personal misclassification by obtaining positive or negative sentiment information by using data mining that is carried out by using tools that execute the tools that execute data mining operations that have been determined based on the analysis of models of hidden data on big data thus outlining the discovery of knowledge about Sjakhyakirti University.</em></p><p><strong><em>Keywords </em></strong><strong><em>:</em></strong><strong><em> </em></strong><em>Social</em><em> </em><em>Media, Sjakhyakirti, Naïve Bayes Classifie</em></p><p class="SammaryHeader" align="center"><strong>Abstrak</strong></p><p><em>Pemanfaatan sosial media </em><em>saat </em><em>ini tidak hanya untuk berkomunikasi antara teman saja, akan tetapi sering juga dijadikan sebuah sarana untuk menyampaikan suatu aspirasi bagi masyarakat khususnya masyarakat indonesia mengenai masalah hukum ataupun masalah yang berhubungan dengan pemerintahan</em><em> serta masalah lainnnya</em><em>. Salah satu aspirasi yang disampaikan melalui sosial media ini adalah sebuah hastag yang banyak dilihat setiap harinya </em><em>salah satunya </em><em>mengenai </em><em>Universitas Sjakhyakirti </em><em>dari </em><em>pemanfaaat sosial media </em><em>ini </em><em>maka </em><em>munculah banyak sentimen dari setiap masyarakat, ada yang memberikan sentimen positif dan juga sentimen negatif mengenai tanggapan terhadap hastag tersebut yang dapat berdampak baik atau buruk bagi kehidupan sehari-hari dimasyarakat.</em><em> B</em><em>eberapa alasan sentimen posit</em><em>i</em><em>f</em><em> </em><em>dan negatif yang bersumber dari sosial media ini</em><em>, </em><em>akan memanfaatkan sosial media</em><em>. Dari </em><em>permasalahan ini peneliti menghasilkan sebuah solusi dimana hastag tersebut apakah dapat memberikan dampak yang baik bagi masyarakat umumumnya ataupun sebaliknya. Dalam menganalisa ini, peneliti menggunakan metode Naïve Bayes Classifier yang merupakan salah satu metode machine learning yang menggunakan perhitungan probabilitas, pengklasifikasian hastag otomatis ini dapat disesuaikan sehingga meminimalisasi aksi salah pengklasifikasian secara personal dengan memproleh informasi sentimen positif atau negative</em><em> dengan menggunakan data mining yang dilakukan dengan tool weka yang mengeksekusi operasi data mining yang telah didefinisikan berdasarkan model analisis dari data tersembunyi pada sejumlah data besar sehingga menguraikan penemuan pengetahuan mengenai Universitas Sjakhyakirti.</em></p><strong><em>Kata kunci : </em></strong><em>Sosial Media, Sjakhyakirti, Naïve Bayes Classifie</em>

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.

scholarly journals ANALISIS SENTIMEN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER DENGAN SELEKSI FITUR CHI SQUARE

2014 ◽  
Vol 3 (3) ◽  
pp. 92 ◽  
Author(s):  
JUEN LING ◽  
I PUTU EKA N. KENCANA ◽  
TJOKORDA BAGUS OKA
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Computational Study ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Chi Square

Sentiment analysis is the computational study of opinions, sentiments, and emotions expressed in texts. The basic task of sentiment analysis is to classify the polarity of the existing texts in documents, sentences, or opinions. Polarity has meaning if there is text in the document, sentence, or the opinion has a positive or negative aspect. In this study, classification of the polarity in sentiment analysis using machine learning techniques, that is Naïve Bayes classifier. Criteria for text classification decisions, learned automatically from learning the data. The need for manual classification is still required because training the data derived from manually labeling, the label (feature) refers to the process of adding a description of each data according to its category. In the process of labeling, feature selection is used and performed by chi-square feature selection, to reduce the disturbance (noise) in the classification. The results showed that the frequency of occurrences of the expected features in the true category and in the false category have an important role in the chi-square feature selection. Then classification breaking news by Naïve Bayes classifier obtained an accuracy of 83% and a harmonic average of 90.713%.

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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