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Determining the effects of Consumer Sentiments on E-commerce sector using Sentiment Analysis : A Deep Learning Approach

Kashilkar, Ayusha Eknath (2023) Determining the effects of Consumer Sentiments on E-commerce sector using Sentiment Analysis : A Deep Learning Approach. Masters thesis, Dublin, National College of Ireland.

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In this modern world data stands the as one of the most valuable assets to companies. As humans are attached to their digital gadgets which generate immense amount of data per second. This generated data is used by companies to make analytical and financial decision. Ecommerce market stand as 4th on biggest industries in world with market value of 9.09 trillion dollar. As Amazon being on top of the ecommerce market, multiple purchases, transactions, and n number of users are active on amazon site at same moment of time. Companies have grown to keep customer first as policy. Users review about products are critical to company as from these review multiple insights can be found which can affect the company’s value, marketing, and financial decision. In this research, Natural Language processing techniques are used to find out the sentiments from customer review on different sectors of amazon using multiple machine learning and deep learning techniques which can be used by manufactures for enhancing their products.

Item Type: Thesis (Masters)
Yaqoob, Abid
Uncontrolled Keywords: Bi-directional Long Term Short Memory; Random forest; Support Vector Machine; NRCLex; GloVe; Ecommerce; text mining
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing
H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
H Social Sciences > HF Commerce > Electronic Commerce
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 19 May 2023 14:46
Last Modified: 19 May 2023 14:46

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