REVIEW SENTIMEN ANALISIS APLIKASI SOSIAL MEDIA DI GOOGLE PLAYSTORE MENGGUNAKAN METODE LOGISTIC REGRESSION
DOI:
https://doi.org/10.24912/pserina.v1i1.17504Abstrak
Everyone has their own nature and way of thinking, which makes their style of conveying what they are thinking is different from one to another. Not only that, what they are conveying can have a different meanings according to the person who listens to it and which perspective they take it into. From this, the reviews given have different meanings for each person which other people can easily understand the meaning behind it. Although humans can distinguish whether the review given by other people has a positive or negative meaning, machine cannot understand this without being given instructions first, in order for machine to find out the meaning in the text, Therefore, this research was conducted. The purpose of designing this system is to make it easier to analyze a collection of reviews on a social media application from google playstore without the need to see all the review given by other users. This system is also designed to fulfill the purpose of evaluating using the TF-IDF method and the Logistic Regression method on the program to be created. The intended evaluation is from the level of accuracy obtained from the use of the model formed using the method chosen and how many predictions have the correct value based on the result of Confusion Matrix from the program that has been designed . From the results obtained, it can be concluded whether the social media application has a good reputation from its users or not.Referensi
David G. Kleinbaum & Mitchel Klein, (1994), “Logistic Regression: A Self-Learning Text, 2nd
Edition”, Springer, New York, NY, h. 5 - 6.
Güner, Levent, Emilie Coyne, & Jim Smit. (2019). "Sentiment analysis for amazon. com
reviews.". Big Data in Media Technology (DM2583) KTH Royal Institute of Technology,
Stockholm, h.3 – 7.
Tom Mitchell, (1997), “Machine Learning”, McGraw-Hill Science/Engineering/Math, h. 17.
Nengsih, Warnia, M. Mahrus Zein, & Nazifa Hayati. (2021). "Coarse-Grained Sentiment
Analysis Berbasis Natural Language Processing–Ulasan Hotel.". Jurnal Nasional Teknik
Elektro dan Teknologi Informasi 10.1:, h.41-48.
Nguyen, Heidi, Aravind Veluchamy, Mamadou Diop, & Rashed Iqbal. (2018). “Comparative
study of sentiment analysis with product reviews using machine learning and lexiconbased approaches”. SMU Data Science Review 1, no. 4, h.7.