4/1/2023 0 Comments Cara buka lapak di tokopedia![]() ![]() ![]() Today E-commerce popularity has made web an excellent source of gathering customer reviews/opinions about a product that they have purchased. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. From the experiment results, it shows the method can be used to classify customer sentiments in social media Twitter automatically and Elevenia is the highest e-commerce with customer satisfaction. The purpose of this research is to find out how to process the huge customer sentiment Twitter to become useful information for the e-commerce company, and which of those top-3 e-commerce companies has the highest level of customer satisfaction. We use Twitter data for sentiment analysis because it's faster, cheaper and easier from both the customer and the researcher side. We compare the sentiments towards of top-3 e-commerce sites visited companies, they are Bukalapak, Tokopedia and Elevenia. This research performs sentiment analysis using naive bayes classifier classification method with TF-IDF weighting. Hence, using social media data, it provides a new practical way to measure marketing intelligent effort. Many Indonesian customers express their sense of satisfaction or dissatisfaction towards the company through social media. One element of marketing intelligence is to assess customer satisfaction. Marketing intelligence is an important activity to measure competitive position. The rapid growth of e-commerce market in Indonesia, making various e-commerce companies appear and there has been high competition among them.
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