Penerapan Ensemble Learning Menggunakan Metode Support Vector Machine, Naïve Bayes Classifier, dan Valence Aware Dictionary for Sentiment Reasoning untuk Meningkatkan Akurasi Sentiment Analysis pada Review Aplikasi Google Play

Tania Sunyoto, Djoni Haryadi Setiabudi, Alvin Nathaniel Tjondrowiguno

Abstract


In an age where almost everyone owns a smartphone, more and more mobile applications are being developed and distributed to Google Play. To decide which application to download, customers are influenced by ratings and reviews. Reviews provide more information than ratings, but there are so many that they are difficult and take a long time to obtain. The application of sentiment analysis supported by high accuracy in reviews can make it easier for customers to get sentiment information from th e application and help them make decisions to download / use the application or not. This research uses a combination of Naïve Bayes and SVM machine learning models with the VADER lexicon model, then Ensemble Learning is carried out using Majority Voting, Majority Weighted Voting, and Stacking to improve accuracy. The results of this system indicate that by using Ensemble Learning the accuracy result increases but not significantly even decreases from SVM results of 88.88% to 88.87% using Stacking.

Keywords


sentiment analysis; text classification; lexicon; VADER; SVM; ensemble learning; review; Google Play Store.

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References


Beri, A. 2020. Sentiment Analysis using VADER.

URL=https://towardsdatascience.com/sentimental-analysisusing-vader-a3415fef7664

Bonta, V., Kumaresh, N., & Janardhan, N. 2019. A

Comprehensive Study on Lexicon Based Approaches for

Sentiment Analysis. Asian Journal of Computer Science and

Technology (AJCST), 8(2), 1-6. DOI=10.51983/ajcst2019.8.S2.2037

Calderon, P. 2017. VADER sentiment analysis explained.

URL=https://medium.com/@piocalderon/vader-sentimentanalysis-explained-f1c4f9101cd9

Gupta, L. 2019. Google Play Store Apps.

URL=https://www.kaggle.com/lava18/google-play-storeapps

Lai, C.-H. & Hsu, C.-Y. 2021. Rating prediction based on

combination of review mining and user preference analysis.

Information Systems, 99. DOI=10.1016/j.is.2021.101742

López, F. 2020. Ensemble Learning: Stacking, Blending &

Voting. URL=https://towardsdatascience.com/ensemblelearning-stacking-blending-voting-b37737c4f483

Malik, H., Shakshuki, E. M., & Yoo W.-S. 2018. Comparing

mobile apps by identifying ‘Hot’ features. Future Generation

Computer Systems. DOI=10.1016/j.future.2018.02.008

Monkey Learn. 2021. Sentiment Analysis: A Definitive

Guide. URL=https://monkeylearn.com/sentiment-analysis/

Reddy, V. 2018. Sentiment Analysis using SVM.

URL=https://medium.com/@vasista/sentiment-analysisusing-svm-338d418e3ff1

Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves,

M., & Benevenuto, F. 2016. SentiBench - a benchmark

comparison of state-of-the-practice sentiment analysis

methods. EPJ Data Science, 5(1).

DOI=10.1140/epjds/s13688-016-0085-1

Singla, Z., Randhawa, S., & Jain, S. 2017. Sentiment analysis

of customer product reviews using machine learning. 2017

International Conference on Intelligent Computing and

Control (I2C2). DOI=10.1109/I2C2.2017.8321910

Soumik, M. M. J., Farhavi, S. S. M., Eva, F., Sinha, T., &

Alam, M. S. 2019. Employing Machine Learning techniques

on Sentiment Analysis of Google Play Store Bangla

Reviews. 2019 22nd International Conference on Computer

and Information Technology (ICCIT).

DOI=10.1109/ICCIT48885.2019.9038348


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