Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes

Abstract

Online product ratings usually provide descriptive reviews and also reviews in the form of ratings. Likewise, what was done at the Lazada online store. Descriptive review can provide a clear view compared to a rating review to other potential buyers. However, in reality there is a mismatch between the description review and the rating given. This creates a lack of information for sellers as well as potential buyers. Automatic classification of buyer descriptive reviews is proposed in this study so that there is a match between descriptive reviews and rating reviews. This automatic classification descriptive review uses the Naive Bayes algorithm with n-gram feature extraction and TF-IDF word weighting. The results of this study obtained the best accuracy of 94.06%, a recall of 91.73% and precision of 90.71% in Bigram feature extraction. With this accuracy value it can be used as a reference or model for classifying product description reviews, so that the feedback process between sellers and buyers can run well.

Potensi Aplikasi Mobile untuk Pengembangan Usaha berdasarkan Ulasan Pengguna (Studi Kasus: Appon dan Kuncie)

“…Persepsi atau ulasan pengguna mengenai sebuah sistem yang terdapat pada situs terbuka bernilai positif dan negatif yang dapat diklasifikasi [2]. Ulasan pengguna produk cenderung diungkapkan dengan cara ekspresif yang membuat ulasan menjadi lebih komprehensif dan dapat digunakan sebagai dasar untuk evaluasi produk [9]. Ulasan pengguna yang digunakan sebagai evaluasi produk harus diklasifikasi terlebih dahulu untuk menentukan posisi tiap ulasan yang diungkapan dalam kalimat tersebut bersifat positif atau negatif [2].…”

Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling

“…In simple sentences (sentences containing one clause), usually a negation can reverse the polarity of all the words in a sentence [13]. However in, compound sentences (many clauses) a negation usually reverses the polarity of some words in the sentence and the number of words [14] ll be reversed varies according to the linguistic features [15]. In this study, the text weighting used is TFIDF and the N-gram feature.…”