Public’s Sentiment Analysis on Shopee-Food Service Using Lexicon-Based and Support Vector Machine

Abstract

Technology field following how era keep evolving. Social media already on everyone’s daily life and being a place for writing their opinion, either review or response for product and service that already being used. Twitter are one of popular social media on Indonesia, according to Statista data it reach 17.55 million users. For online business sector, knowing sentiment score are really important to stepping up their business. The use of machine learning, NLP (Natural Processing Language), and text mining for knowing the real meaning of opinion words given by customer called sentiment analysis. Two methods are using for data testing, the first is Lexicon Based and the second is Support Vector Machine (SVM). Data source that used for sentiment analyst are from keyword ‘ShopeeFood’ and ‘syopifud’. The result of analysis giving accuracy score 87%, precision score 81%, recall score 75%, and f1-score 78%.

Identify and predict incorrect prices by Machine Learning Model

“…Recent development of data mining provides a promising approach to address the challenge. Data mining on the e-commerce platform is mainly text mining, such as analyzing the user's emotions (Shalehanny, 2021;Agustina, 2020). There are two stages in analyzing user emotions: the first stage, processing the text-by-text processing techniques (Joachims, 1998).…”

“…The second stage is constructing a machine learning model for classification. Machine learning models are commonly used as KNN algorithm (Fix, 1952), Naive Bayes (Good, 1965) decision trees (Quinlan, 1993;Breiman, 1984), Support Vector Machine learning (Vapnik, 1995), model aggregation algorithms, including Boosting (Freund, 1995;Breiman, 1998) and random forests (Breiman, 2001). These models are promising to address the above identified challenge.…”

Word Vectors for Criticism of a Korean Film - 'Decision to Leave' by Chanwook Park

“…For example, by training the model on movie review texts, it learns to recognize the sentiment expressed in text -whether it's positive, negative, or neutral. This is a valuable application for understanding public opinion, customer feedback, or any text data with sentiment [1][2][3].…”