Sentiment analysis using Natural Language Processing (NLP) & deep learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Opis bibliograficzny
Główni autorzy:	Islam, Kazi Minhazul, Reza, Md. Safkat, Yeaser, MD. Samin
Kolejni autorzy:	Islam, MD Saiful
Format:	Praca dyplomowa
Język:	English
Wydane:	Brac University 2022
Hasła przedmiotowe:	NLP Deep learning Cyber bullying Racism Social media Prediction Bi-directional LSTM Machine learning Natural language processing (Computer science) Human-computer interaction
Dostęp online:	http://hdl.handle.net/10361/15823

id	10361-15823
record_format	dspace
spelling	10361-158232022-01-26T10:08:23Z Sentiment analysis using Natural Language Processing (NLP) & deep learning Islam, Kazi Minhazul Reza, Md. Safkat Yeaser, MD. Samin Islam, MD Saiful Rahman, Rafeed Department of Computer Science and Engineering, Brac University NLP Deep learning Cyber bullying Racism Social media Prediction Bi-directional LSTM Machine learning Natural language processing (Computer science) Human-computer interaction This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 19-21). It is an age of the Web and electronic media, and social media stages are one of the foremost frequently used communication mediums these days. But a few individuals utilize these platforms for a noxious reason and among those negative angles "Cyberbullying" is predominant. The way of monitoring user opinions throughout social media platforms such as Twitter and Facebook have been proven to be an e ective way of learning practically all of the consumers' thoughts which can open the door of potential future implementations. General emotion inspection can give us important data. The examination of supposition on informal communities, for example, Twitter or Facebook, has become an amazing method for nding out about the clients' sentiments and has a wide scope of utilizations. Notwithstanding, the productivity furthermore, the exactness of notion examination is being blocked by the di culties experienced in characteristic language handling (NLP). As of late, it is established that profound learning models are potential answers to the drawbacks of NLP. Natural language processing refers to a process that enables the machine to act like human and decreases the space between the person and the machine. Thus, NLP readily communicates with the computer in a straightforward sense. NLP has gained several uses in recent times. Each one of them are extremely e ective in daily life. An example can be a device which can be handled by voice commands. Several research workers are putting e ort on this idea in order to make even more real-life applications Natural Language Processing has tremendous potential to facilitate the use of computer interfaces for humans, as people will ideally communicate in their own language to the computer instead of learning an exclusive language based on computer instructions. In case of programming, traditional programming language's importance has always been underrated. This concept is questionable. We believe that modern Natural Language Processing techniques can make possible the use of natural language to express programming ideas, thus drastically increasing the accessibility of programming to non-expert users. Our team thinks that the implementation of natural language to convey programming concepts may be made possible by contemporary natural language processing techniques so that programming is accessible to inexperienced consumers substantially. The following paper surveys the most recent analysis that have utilized profound methodology how to take care of conclusion investigation issues, for example, assessment extremity. Models utilizing term recurrence opposite record recurrence (TF-IDF) and content insertion was implemented to an arrangement of datasets. At last, one similar examination of the exploratory outcomes was carried out in respect to several models and information highlights. Kazi Minhazul Islam Md. Safkat Reza MD. Samin Yeaser B. Computer Science 2022-01-04T06:14:54Z 2022-01-04T06:14:54Z 2021 2021-09 Thesis ID 17101456 ID 17101106 ID 17301063 http://hdl.handle.net/10361/15823 en Brac 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. 21 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	NLP Deep learning Cyber bullying Racism Social media Prediction Bi-directional LSTM Machine learning Natural language processing (Computer science) Human-computer interaction
spellingShingle	NLP Deep learning Cyber bullying Racism Social media Prediction Bi-directional LSTM Machine learning Natural language processing (Computer science) Human-computer interaction Islam, Kazi Minhazul Reza, Md. Safkat Yeaser, MD. Samin Sentiment analysis using Natural Language Processing (NLP) & deep learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Islam, MD Saiful
author_facet	Islam, MD Saiful Islam, Kazi Minhazul Reza, Md. Safkat Yeaser, MD. Samin
format	Thesis
author	Islam, Kazi Minhazul Reza, Md. Safkat Yeaser, MD. Samin
author_sort	Islam, Kazi Minhazul
title	Sentiment analysis using Natural Language Processing (NLP) & deep learning
title_short	Sentiment analysis using Natural Language Processing (NLP) & deep learning
title_full	Sentiment analysis using Natural Language Processing (NLP) & deep learning
title_fullStr	Sentiment analysis using Natural Language Processing (NLP) & deep learning
title_full_unstemmed	Sentiment analysis using Natural Language Processing (NLP) & deep learning
title_sort	sentiment analysis using natural language processing (nlp) & deep learning
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/15823
work_keys_str_mv	AT islamkaziminhazul sentimentanalysisusingnaturallanguageprocessingnlpdeeplearning AT rezamdsafkat sentimentanalysisusingnaturallanguageprocessingnlpdeeplearning AT yeasermdsamin sentimentanalysisusingnaturallanguageprocessingnlpdeeplearning
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Sentiment analysis using Natural Language Processing (NLP) & deep learning

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