Abstract
Beauty products are an important requirement for people, especially women. But, not all beauty products give the expected results. A review in the form of opinion can help the consumers to know the overview of the product. The reviews were analyzed using a multi-aspect-based approach to determine the aspects of the beauty category based on the reviews written on femaledaily.com. First, the review goes through the preprocessing stage to make it easier to be processed, and then it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting. From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect.
Highlights
Beauty products are an important requirement for people, especially women
The review goes through the preprocessing stage to make it easier to be processed, and it used the Support Vector Machine (SVM) method with the addition of Semantic Similarity and TF-IDF weighting
From the test result using semantic, get an accuracy of 93% on the price aspect, 92% on the packaging aspect, and 86% on the scent aspect
Summary
7 is an example of the stemming stages. Remove Symbol, Punctuation and Case Folding This process changes the capital letters to lowercase ‘toner’,’wardah’,’murah’,’c ocok’,’muka’,’membuat’,’je rawatan’. Feature Extraction N-Gram example of remove symbol, punctuation, and case This study uses N-Gram as the feature extraction. This study will use the unigram feature because bigram categorized the feature by two words, and it Tokenizing caused many word features are not in the training data. The aim is to find word features calculate the Semantic Similarity aims to classify the unrecognized frequency of the words (TF). After that, it will be data by the model that has been made. The calculation of the document that contains several words (DF) will be Algoritma Semantic Similarity For every feature in sentence do carried out and inverse the DF Value (idtf). For each word in ten word do Tfidfvectorizer to finish it
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