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The Sentiment analysis of Movie reviews using the Transfer Learning approach

Paranjape, Tanmay Shrikant (2022) The Sentiment analysis of Movie reviews using the Transfer Learning approach. Masters thesis, Dublin, National College of Ireland.

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Movie reviews are often considered an important factor in determining movie success. It has been often observed that the consumers refer the genuine reviews and decide whether the movie is worth the price of admission. The professional reviews posted by experts and reviews posted by consumers affect the decision to purchase a movie ticket. The transfer learning approach in deep learning is a method in which a model developed while solving one task is been used to solve another task. The use of Pre-trained models is a popular approach in Natural language processing and this study aims to use pre-trained models for Sentiment analysis of movie reviews. The dataset on IMDB movie reviews has been taken from Kaggle and BERT (Bi-directional Encoder representation from Transformers) & USE (Universal sentence encoder) has been used for the methodology to answer the research question. The NLTK (Natural Language toolkit) has been adapted for pre-processing because of its prominent features. The accuracy of 84.49% has been achieved by USE whereas the BERT and LSTM achieved 75.70% & 82.75% respectively.

Item Type: Thesis (Masters)
Uncontrolled Keywords: LSTM; BERT; USE; Encoder; Transformers
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 > Film 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: 27 Feb 2023 16:40
Last Modified: 01 Mar 2023 17:55

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