Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM
The use of online food ordering through food systems or applications continues to increase, requiring vendors to implement marketing and sales strategies through surveys, feedback. The problems that arise are building a system analysis model from a collection of tweets with hashtags or usernames for ordering food online . The Support Vector Machine (SVM) algorithm is used for text classification. Tweets are collected into data sets, training data, and testing data, then a classification model of the SVM Algorithm is built. Preprocessing data, tweets are cleansing, tokenized, and stopword remove. From the collected tweets, they are grouped into 10 variables to identify demographic profiles. The results of the analysis are classified as positive sentiments, namely residence, price range, using promos, paid types, halal food while negative sentiments are ethnicity, culture, vegetarianism, place. Classification accuracy is important to validate the results of the SVM model. From 500 train data tweet, the resulting classification is 66% positive sentiment and 34% negative sentiment. Overall accuracy model Linier SVM result 83.2% with accuracy 92.55%.
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