Spam and scam detection through text analysis

This report summarizes an experimental study to detect spammer and scammer existence in e-commerce platform. The combination studies of analysing business review and rating were used to categorize the text review into two classifications, namely Truthful and Deceptive in which were classified furthe...

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Main Author: Prawira, Nathania Anggraini
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Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/140012
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1400122023-07-07T18:37:01Z Spam and scam detection through text analysis Prawira, Nathania Anggraini - School of Electrical and Electronic Engineering Chen Li Hui elhchen@ntu.edu.sg Engineering::Electrical and electronic engineering This report summarizes an experimental study to detect spammer and scammer existence in e-commerce platform. The combination studies of analysing business review and rating were used to categorize the text review into two classifications, namely Truthful and Deceptive in which were classified further into Positive and Negative classes. Background knowledge for manual data labeling is discussed later. In this study, a sub-domain of Machine Learning Processing, such as Natural Language Processing (NLP) was implemented for the machine to simulate and classify the given text in human ability degree. The raw corpus collection was predicted with the application of TFIDF Transformer with Count Vectorizer initialization. Furthermore, attention mechanism was believed to pay greater attention to certain factors and help addressing the text focus during the data processing. Hence, the application of attention mechanism may enhance the output prediction accuracy and Transformer model was also considered in this study. The experimental model comparison was made between the integration of a single and multiple classifiers in BERT model. Some programming modules, such as, PyTorch, Scikit-Learn, Keras, spaCy and Natural Language Toolkit (NLTK) were widely used in this experiment. Bachelor of Engineering (Information Engineering and Media) 2020-05-26T04:17:23Z 2020-05-26T04:17:23Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/140012 en A3054-191 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Prawira, Nathania Anggraini
Spam and scam detection through text analysis
description This report summarizes an experimental study to detect spammer and scammer existence in e-commerce platform. The combination studies of analysing business review and rating were used to categorize the text review into two classifications, namely Truthful and Deceptive in which were classified further into Positive and Negative classes. Background knowledge for manual data labeling is discussed later. In this study, a sub-domain of Machine Learning Processing, such as Natural Language Processing (NLP) was implemented for the machine to simulate and classify the given text in human ability degree. The raw corpus collection was predicted with the application of TFIDF Transformer with Count Vectorizer initialization. Furthermore, attention mechanism was believed to pay greater attention to certain factors and help addressing the text focus during the data processing. Hence, the application of attention mechanism may enhance the output prediction accuracy and Transformer model was also considered in this study. The experimental model comparison was made between the integration of a single and multiple classifiers in BERT model. Some programming modules, such as, PyTorch, Scikit-Learn, Keras, spaCy and Natural Language Toolkit (NLTK) were widely used in this experiment.
author2 -
author_facet -
Prawira, Nathania Anggraini
format Final Year Project
author Prawira, Nathania Anggraini
author_sort Prawira, Nathania Anggraini
title Spam and scam detection through text analysis
title_short Spam and scam detection through text analysis
title_full Spam and scam detection through text analysis
title_fullStr Spam and scam detection through text analysis
title_full_unstemmed Spam and scam detection through text analysis
title_sort spam and scam detection through text analysis
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/140012
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