SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA FOR PAKISTANI FASHION BRAND MONITORING USING MACHINE LEARNING
Keywords:
Sentiment Analysis, Brand Monitoring, Romanized Urdu NLP, Random Forest, Pakistani Fashion E-commerce, Social Media Analytics, Trend PredictionAbstract
The present paper is introducing an industrial grade product, called Brand Pulse, that integrates brand monitoring with the trend intelligence in the environment of the Pakistani fashion industry, inspired by the use of social media. In this paper, it is used a unique bilingual lexicon in English and a set of romanized Urdu with the help of Random forest to make binary trend direction predictions of brand trends (UP/DOWN). A total of 10,602 data points were collected from seven different platforms (Instagram, Facebook, Twitter-X, TikTok, YouTube, Daraz.pk, Google Reviews) of 17 of the top fashion brands in Pakistan. With five features (restricted to Brand ID, Platform ID, Likes, Shares and Sentiment Score), a random forest classifier model with 250 estimators and maximum depth of 3 was able to get 94% accuracy on the "free" test sample and 93-95% accuracy in each of the validation folds. The entire prediction process is available via a FastAPI RESTful API service, and an interactive Streamlit application.
