scholarly journals Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores

2018 ◽  
Vol 7 (3) ◽  
pp. 479-486 ◽  
Author(s):  
Mastura Md Saad ◽  
Nursuriati Jamil ◽  
Raseeda Hamzah
Keyword(s):  
Support Vector Machine ◽  
Decision Tree ◽  
Emotion Recognition ◽  
Speech Synthesis ◽  
Support Vector ◽  
Text Documents ◽  
Inverse Document Frequency ◽  
Recognition Experiment ◽  
Document Frequency ◽  
Future Work

In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification.

scholarly journals Aspect Category Classification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 229-235
Author(s):  
Syaifulloh Amien Pandega Perdana ◽  
Teguh Bharata Aji ◽  
Ridi Ferdiana
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Term Weighting ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Bahasa Indonesia

Ulasan pelanggan merupakan opini terhadap kualitas barang atau jasa yang dirasakan konsumen. Ulasan pelanggan mengandung informasi yang berguna bagi konsumen maupun penyedia barang atau jasa. Ketersediaan ulasan pelanggan dalam jumlah besar pada website membutuhkan suatu framework untuk mengekstraksi sentimen secara otomatis. Sebuah ulasan pelanggan sering kali mengandung banyak aspek sehingga Aspect Based Sentiment Analysis (ABSA) harus digunakan untuk mengetahui polaritas masing-masing aspek. Salah satu tugas penting dalam ABSA adalah Aspect Category Detection. Metode machine learning untuk Aspect Category Detection sudah banyak dilakukan pada domain berbahasa Inggris, tetapi pada domain bahasa Indonesia masih sedikit. Makalah ini membandingkan kinerja tiga algoritme machine learning, yaitu Naïve Bayes (NB), Support Vector Machine (SVM), dan Random Forest (RF) pada ulasan pelanggan berbahasa Indonesia menggunakan Term Frequency–Inverse Document Frequency (TF-IDF) sebagai term weighting. Hasil menunjukkan bahwa RF memiliki kinerja paling unggul dibandingkan NB dan SVM pada tiga domain yang berbeda, yaitu restoran, hotel, dan e-commerce, dengan nilai f1-score untuk masing-masing domain adalah 84.3%, 85.7%, dan 89,3%.

scholarly journals Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale

2021 ◽  
Vol 6 (3) ◽  
pp. 236-251
Author(s):  
Novira Azpiranda ◽  
Ahmad Afif Supianto ◽  
Nanang Yudi Setiawan ◽  
Endang Suryawati ◽  
R. Sandra Yuwana ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Business Strategy ◽  
Confusion Matrix ◽  
Support Vector ◽  
Inverse Document Frequency ◽  
Customer Reviews ◽  
A Value ◽  
Document Frequency ◽  
System Usability Scale ◽  
System Usability

Al-Ghiff Steak is a restaurant located in Cirebon City that offers quality steaks at affordable prices. For maintaining a competitive Al-Ghiff Steak advantage and reputation, it is important to build a good relationship with customers and have a business strategy that considers customer opinions. However, in its implementation, Al-Ghiff Steak has difficulty when collecting and processing customer review data manually. Therefore, it is necessary to conduct sentiment analysis by utilizing Google Reviews to determine customer perspectives regarding Al-Ghiff Steak products and services. This analysis was conducted on 968 Google Review reviews from 2016 to 2020 using the Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Classification testing is done with a confusion matrix against four parameters: accuracy, precision, recall, and f1-score. SVM with TF-IDF gets accuracy value 83%, precision 64%, recall 60% and f1-score 59%. The sentiment classification result is then visualized in the form of a dashboard. We utilize the System Usability Scale (SUS) for usability testing, which produces a value of 77.5. This result achieve the Acceptable category and an Excellent rating.

scholarly journals Analisis Sentimen Opini Pemindahan Ibu Kota Pada Twitter Dengan Metode Support Vector Machine

2021 ◽  
Vol 14 (1) ◽  
pp. 49
Author(s):  
Tezza Fazar Tri Hidayat ◽  
Garno Garno ◽  
Azhari Ali Ridha
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Support Vector ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency

Relokasi ibu kota Indonesia kini telah diresmikan oleh Presiden Joko Widodo pada 26 Agustus 2019 ke Kalimantan, ini adalah sejarah baru dalam sejarah Indonesia karena belum pernah terjadi sebelumnya, sehingga memunculkan banyak pendapat atau tanggapan dari masyarakat. Analisis sentimen adalah kegiatan yang digunakan untuk menganalisis pendapat atau opini seseorang tentang suatu topik. Twitter adalah media sosial yang digunakan untuk mengekspresikan pendapat pengguna dan menyatukannya pada suatu topik. Support Vector Machine adalah metode text mining yang mencakup metode klasifikasi dan Term Frequency - Inverse Document Frequency adalah metode pembobotan karakter. SVM dan TF-IDF dapat digunakan untuk menganalisis sentimen opini publik tentang topik pemindahan ibukota Indonesia. Tujuan dari penelitian ini adalah untuk mengklasifikasikan opini publik tentang topik memindahkan Ibu Kota Indonesia dari ribuan tweet yang telah dikumpulkan dan disaring. Tweet pada dari 22-29 Maret 2020 telah diproses menjadi 992 tweet dan terdiri dari 221 data dengan label positif dan 771 data negatif. Dan menggunakan metode SVM yang memiliki akurasi 77,72% dan dikombinasikan dengan TFIDF yang meningkatkan akurasinya menjadi 78,33%.

scholarly journals Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 138-149
Author(s):  
Ukhti Ikhsani Larasati ◽  
Much Aziz Muslim ◽  
Riza Arifudin ◽  
Alamsyah Alamsyah
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Feature Weighting ◽  
Support Vector ◽  
Chi Square ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency

Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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