Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes

Susanti, Aisah Rini; Djatna, Taufik; Kusuma, Wisnu Ananta

doi:10.12928/telkomnika.v15i3.4284

Cited by 18 publications

(15 citation statements)

References 16 publications

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“…85 %. One of the application of naïve Bayes classifiers in media social mining domain is discussed in [11]. The study explored the application of Multinomial Naïve Bayes classifier technique to mine the sentiment opinion pattern of GSM based on customer's twitter account.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

Sadikin¹,

Alfiandi²

2018

IJECE

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Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

Sadikin¹,

Alfiandi²

2018

IJECE

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“…Complaints from users will be automatically classified into predefined categories, which are "Akademik (Academic)," "Kegiatan (Activity)," "Fasilitas (Facility)," "BEM (Student Board)," and "Lainnya (Other)," using Naive Bayes Classifier algorithm. Naïve Bayes itself is a probabilistic learning method to classify data, which is popularly used in machine learning and data mining researches [17,18]. The testing stage is performed using the USE Questionnaire with a seven-level Likert scale to measure the usability variables of the application.…”

Section: Methodsmentioning

confidence: 99%

UMN E-Complaint Application with Automatic Classification Feature

Ferdina,

Hansun,

Kristanda

2019

IJEAT

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In university, students’ complaints about the services provided are essential things to note because it could lead to higher student transfer if not appropriately handled. In Universitas Multimedia Nusantara (UMN), students can express their complaints through Dewan Keluarga Besar Mahasiswa (DKBM) UMN. By technology development, an online complaint submission system can be applied at UMN. A method that can be used in supporting efficient complaints processing is by using automated classification system since it can save both time and human resources. UMN e-complaints application with automated complaints classification feature was built using the CodeIgniter framework. The evaluation of the e-complaint application was conducted by using the USE Questionnaire. The results show that both DKBM UMN and students agree that the application is useful, easy to use, easy to learn, and satisfying.

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“…Out work can be utilized to take out right now accessible repetitive frameworks of making feeling power vocabulary. [31] This exploration produced a Decision Tree establishes in the element "aktif" in which the likelihood of the component "aktif" was from positive class in Multinomial Naive Bayes strategy. The assessment demonstrated that the most astounding precision of arrangement utilizing Multinomial Naïve Bayes.…”

Section: Related Workmentioning

confidence: 99%

Opinion mining framework using proposed RB-bayes model for text classication

Bhalla

Bagga

2019

IJECE

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<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>

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Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes

Cited by 18 publications

References 16 publications

Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

UMN E-Complaint Application with Automatic Classification Feature

Opinion mining framework using proposed RB-bayes model for text classication

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