TY - GEN
T1 - Fuzzy k-nearest neighbor for restaurants business sentiment analysis on tripadvisor
AU - Billyan, Baiq
AU - Sarno, Riyanarto
AU - Sungkono, Kelly Rossa
AU - Tangkawarow, Irene R.H.T.
N1 - Publisher Copyright:
© 2019 IEEE
PY - 2019/7
Y1 - 2019/7
N2 - 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%.
AB - 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%.
KW - Data Analysis
KW - Fuzzy K-Nearest Neighbor
KW - Sentiment Analysis
KW - Social Media
KW - TripAdvisor
UR - http://www.scopus.com/inward/record.url?scp=85077954111&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT46704.2019.8938564
DO - 10.1109/ICOIACT46704.2019.8938564
M3 - Conference contribution
AN - SCOPUS:85077954111
T3 - 2019 International Conference on Information and Communications Technology, ICOIACT 2019
SP - 543
EP - 548
BT - 2019 International Conference on Information and Communications Technology, ICOIACT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Information and Communications Technology, ICOIACT 2019
Y2 - 24 July 2019 through 25 July 2019
ER -