Open AccessSentiment analysis of customer response of telecommunication operator in Twitter using DCNN-SVM Algorithm
Author(s) -
Ivan Firdausi,
Imam Mukhlash,
Athyah D. S. Gama,
Nurul Hidayat
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1490/1/012071
Subject(s) - support vector machine , computer science , social media , service (business) , recall , convolutional neural network , plan (archaeology) , precision and recall , operator (biology) , product (mathematics) , feeling , artificial intelligence , algorithm , world wide web , marketing , business , psychology , mathematics , biochemistry , chemistry , geometry , archaeology , repressor , transcription factor , gene , cognitive psychology , history , social psychology
Along with the development of the times, social media is in great demand by various circles of society because social media allows users to express their thoughts or feelings freely. It is important for a company to know public responses about the product or service offered. With this public response, companies can analyze customer needs and plan more satisfying products or services. To be able to know the sentiments of responses, it is necessary to classify responses. Therefore, in this study used the Deep Convolutional Neural Network (DCNN) method as a feature extraction and Support Vector Machine (SVM) as its classification. The performance results of this research are 63% for accuracy, 63% for precision, and 50% for recall of test data.