TY - GEN
T1 - Sentiment Analysis in the Mobile Application Review Document Using the Improved K-Nearest Neighbor Method
AU - Indriati,
AU - Kusyanti, Ari
AU - Zakia, Dea
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - The large number of smartphone users makes Indonesia a very potential market for business, especially mobile application developers. Each application store allows application users to provide a review of the applications used. From the existing application store, none has the sentiment analysis feature to filter or categorize positive reviews and negative reviews. Sentiment analysis is computational research of opinions, sentiments, and emotions that are expressed textually. The review document will go through several stages starting from preprocessing, calculation of term weighting to the calculation of cosine similarity (degree of similarity) to the training data used. The next process is sorting the level of similarity, determining the new k-values to produce a category for the document. The testing process of Sentiment Analysis in the Mobile Application Review Document Using the Improved K-Nearest Neighbor Method resulted in the best average accuracy of 88,76%. The best accuracy from all scenario test is 90,67 % and optimal k value is 15.
AB - The large number of smartphone users makes Indonesia a very potential market for business, especially mobile application developers. Each application store allows application users to provide a review of the applications used. From the existing application store, none has the sentiment analysis feature to filter or categorize positive reviews and negative reviews. Sentiment analysis is computational research of opinions, sentiments, and emotions that are expressed textually. The review document will go through several stages starting from preprocessing, calculation of term weighting to the calculation of cosine similarity (degree of similarity) to the training data used. The next process is sorting the level of similarity, determining the new k-values to produce a category for the document. The testing process of Sentiment Analysis in the Mobile Application Review Document Using the Improved K-Nearest Neighbor Method resulted in the best average accuracy of 88,76%. The best accuracy from all scenario test is 90,67 % and optimal k value is 15.
KW - Classification
KW - Improve K-Nearest Neighbor
KW - review document
KW - Sentiment Analysis
UR - https://www.scopus.com/pages/publications/85080121836
U2 - 10.1109/SIET48054.2019.8986037
DO - 10.1109/SIET48054.2019.8986037
M3 - Conference contribution
AN - SCOPUS:85080121836
T3 - Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019
SP - 332
EP - 337
BT - Proceedings of 2019 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Sustainable Information Engineering and Technology, SIET 2019
Y2 - 28 September 2019 through 30 September 2019
ER -