Study of Topic Modelling and Sentiment Analysis with Word Vectorization for a Hotel Review dataset

Bakshi, Siddhant (2022) Study of Topic Modelling and Sentiment Analysis with Word Vectorization for a Hotel Review dataset. Masters thesis, Dublin, National College of Ireland.

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Abstract

Text classification and Analyzing Sentiments are essential in order to interpret and understand the general opinion within a text. Topic Modelling and Sentiment Analysis are used for the purpose of classifying and categorize unstructured text and for determining the polarity in it. For this research Sentiment Analysis and Topic Modelling is performed on a dataset of Hotel reviews. It is both useful for the customer and the business owner to under- stand the polarity od reviews, over the years review feedback have become important and have re-shaped the hotel industry. Machine learning and Deep learning models have been used for performing Topic modelling and Sentiment Analysis, Random Forest ,SVM and LSTM are used for Sentiment Analysis. LDA and LSA are used for Topic Modelling. Vectorization of words is also performed to improve computation and word2vec and TFIDF are used for the same. Also hyper-parameterized tuning is done for the ma- chine learning models.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Milosavljevic, Vladimir UNSPECIFIED
Uncontrolled Keywords:	LDA; LSA; Sentiment Analysis; Topic Modelling; SVM; NLP
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 > HD Industries. Land use. Labor > Specific Industries > Hospitality Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	17 May 2023 10:04
Last Modified:	17 May 2023 10:04
URI:	https://norma.ncirl.ie/id/eprint/6565

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