Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya

DOI

10.2991/aebmr.k.211226.011How to use a DOI?

Keywords

Twitter; LDA; SVM; Aspect Categorization; Sentimen Classification

Abstract

Food is always close to us, along with the growing population, the food business will continue to grow. During the Covid-19 Pandemic, even though many people stay at home, they occasionally want to buy their favorite food outside the home, whether it’s buying directly onsite or online. The aspect categorization is carried out by combining the LDA method and Semantic Similarity to categorize tweets into four culinary aspects (1) Price, (2) Taste, (3) Place, and (4) Service. The best performance of aspect categorization is by combining the LDA and TF-ICF 100% for term extension. Next, the classification stage with Word Embedding to extract features using GloVe and SVM with three-parameter modifications of the SVC method: (1) C-SVC, (2) Linear SVC, and (3) SVCnu with various kernel changes to get the best results. Then an increase in classification accuracy is carried out using SentiCircle. The results of this study show that aspects of service (4) Has a review with a high negative sentiment that reaches 10.869% compared to sentiment reviews on other aspects in percent (price: 4.348, taste: 6.522, place: 4.521) so that business owners culinary needs to make improvements to pay more attention to customer service to reduce the number of negative reviews on this service aspect. The results also show that changes in sentiment (on positive or negative sentiment) are influenced by the aspect of each review.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

Download article (PDF)

TY  - CONF
AU  - Drajad Bima Ajipangestu
AU  - Riyanarto Sarno
PY  - 2021
DA  - 2021/12/31
TI  - Sentiment Analysis Based on The Aspect of Culinary and Restaurant Review Using Latent Dirichlet Allocation and Support Vector Machine to Improve the Profitability of Culinary Business and Restaurant in Surabaya
BT  - Proceedings of the 3rd International Conference on Business and Management of Technology (ICONBMT 2021)
PB  - Atlantis Press
SP  - 80
EP  - 86
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.211226.011
DO  - 10.2991/aebmr.k.211226.011
ID  - Ajipangestu2021
ER  -