Sentiment Analysis of Multilingual Roman Text for E-Commerce Reviews using Machine Learning Approaches

Abstract

Sentiment analysis, a type of natural language processing (NLP) analyzes the text data to extract and identify subjective information including attitudes, opinions, and feelings. Sentiment analysis can be used to examine audience feedback and reviews in the context of multilingual product reviews. In this paper, a sentiment analysis model using machine learning approaches has been developed for multilingual product reviews in Roman Urdu or Sindhi to determine how the public feels about certain posts, products, etc. The importance of sentiment analysis for product context reviews in many languages in Roman is multifaceted. It can offer insightful information on the preferences of the likes and dislikes of the audience. To accomplish multilingual sentiment analysis, a dataset of reviews in Roman Urdu and Sindhi languages from diverse online platforms and social media sources like YouTube, Facebook, TikTok, Daraz, and Instagram was collected.To identify pertinent features essential for categorizing reviews into negative, positive, or neutral sentiments based on polarity, the Term Frequency Inverse Document Frequency (TF-IDF) method was used. For classification, five different machine learning classifiers including Linear Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbors (KNN) were used. The classification results were measured in terms of precision score, recall score, and F1-score. With TF-IDF, the SVM, and LR outperformed than other classifiers and obtained an F1-score of 0.77%, and 0.78%. To further improve the classification accuracy, the Synthetic Minority Over-sampling TEchnique (SMOTE) was used to manage the class imbalance problem. With SMOTE, the classification accuracy of LR and SVM was improved to 0.79% and 0.80%.