A study of Bangladeshi customers reviews sentiment analysis using BNLP and machine learning approaches

Abstract

This work concerns a sentiment analysis of the Bangladeshi customer reviews through bangla natural language processing and machine learning approaches. The main goal is to create a model that makes it possible to define whether customers have a positive or negative attitude toward a company. For training and testing the model 6,445 reviews of products or services have been collected and annotated for sentiment. Some of the preprocessing steps include cleaning the text data where noise such as stop words and any special characters were removed from the text data so as to allow for analysis. The analysis used text preprocessing methodologies such as TF-IDF to quantize the textual data into machine learning formats. Decision Tree, Random Forest, SVM, SGD, XGB Classifier used to identify the model with optimal performance in sentiment classification. The project also entailed designing the application programming interface using Streamlit and setting the function where users can enter the custom text and immediately get a sentiment analysis report. First outcomes indicate that the models attained high accuracy of sentiment prediction which proves the importance of using machine learning strategies in analyzing customer’s opinions. This paper emphasizes the role of NLP in analyzing consumer behavior and offers a useful instrument – the Customer Sentiment Analyzer – for businesses to evaluate customer’s opinions. It also contributes to the literature on processing regional language provides an understanding of the sentiment of Bangladeshi markets.