Fuzzy k-nearest neighbor for restaurants business sentiment analysis on tripadvisor

Baiq Billyan, Riyanarto Sarno, Kelly Rossa Sungkono, Irene R.H.T. Tangkawarow

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Social media has grown so rapidly, so people easily to share their opinions, moments, etc. There are several types of research about social media, one of which is Sentiment Analysis (SA) that can also be referred to as opinions meaning (OM). Sentiment Analysis focuses on the classification of patterns that are derived from words that are positive words, negative words, and neutral words. In this paper, the researcher uses sentiment analysis with a machine learning approach and uses Fuzzy K-Nearest Neighbor (FK-NN) as the classification method. The dataset uses English text classification, to predicted sentiment of customer reviews about the positive or negative review. The predicted results show that Sentiment Analysis FK-NN is slightly close to the results of the previous research method, namely Probabilistic Latent Semantic Analysis (PLSA), which FK-NN is 72.05% and PLSA is 76%.

Original languageEnglish
Title of host publication2019 International Conference on Information and Communications Technology, ICOIACT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages543-548
Number of pages6
ISBN (Electronic)9781728116556
DOIs
Publication statusPublished - Jul 2019
Event2nd International Conference on Information and Communications Technology, ICOIACT 2019 - Yogyakarta, Indonesia
Duration: 24 Jul 201925 Jul 2019

Publication series

Name2019 International Conference on Information and Communications Technology, ICOIACT 2019

Conference

Conference2nd International Conference on Information and Communications Technology, ICOIACT 2019
Country/TerritoryIndonesia
CityYogyakarta
Period24/07/1925/07/19

Keywords

  • Data Analysis
  • Fuzzy K-Nearest Neighbor
  • Sentiment Analysis
  • Social Media
  • TripAdvisor

Fingerprint

Dive into the research topics of 'Fuzzy k-nearest neighbor for restaurants business sentiment analysis on tripadvisor'. Together they form a unique fingerprint.

Cite this