Rasel, Annajiat AlimHossain, Md SabbirNayla, Nishat2022-05-182022-05-1820212021-09ID 18141007ID 21341040http://hdl.handle.net/10361/16634Cataloged from PDF version of thesis.Includes bibliographical references (pages 28-30).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.Product market demand analysis plays a significant role for originating business strategies due to its noticeable impact on the competitive business field. Furthermore, there are roughly 228 million native Bengali speakers, the majority of whom use Banglish text to interact with one another on social media. Consumers are buying and evaluating items on social media with Banglish text as social media emerges as an online marketplace for entrepreneurs. People use social media to find preferred smartphone brands and models by sharing their positive and bad experiences with them. As a result, our goal is to gather Banglish text data and use sentiment analysis and named entity identification to assess Bangladeshi market demand for smartphones in order to determine the most popular smartphones by gender. We scraped data from social media with instant data scrapers and scraped data from Wikipedia with python web scrapers. Using Python’s Pandas and Seaborn libraries, the raw data is filtered using NLP methods. To train our datasets for named entity recognition, we utilized Spacey’s custom NER model, Amazon Comprehend Custom NER. A tensorflow sequential model was deployed with parameter tweaking for sentiment analysis. Meanwhile, we used the Google Cloud Translation API to estimate the gender of the reviewers using the BanglaLinga library. In this article, we use natural language processing (NLP) approaches and several machine learning models to identify the most in-demand items and services in the Bangladeshi market. Our model has an accuracy of 87.99 percent in Spacy Custom Named Entity recognition, 95.51 percent in Amazon Comprehend Custom NER, and 87.02 percent in the Sequential model for demand analysis. After Spacy’s study, we were able to manage 80 % of mistakes related to misspelled words using a mix of Levenshtein distance and ratio algorithms.30 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Market demand analysisSentiment analysisNatural language processingName entity recognitionTensor-flowGender predictionBanglish TextComputational linguistics.English language -- Data processing.Natural language processing (Computer science)Thesis