Predicting IMDB Movie Rating Using Deep Learning
| Autor: | Anjusha Pimpalshende, Saikiran Gogineni |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Recall
Computer science business.industry Deep learning Volume (computing) 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Task (project management) Naive Bayes classifier Text processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Architecture business Area under the roc curve computer 0105 earth and related environmental sciences |
| Zdroj: | 2020 5th International Conference on Communication and Electronics Systems (ICCES). |
| DOI: | 10.1109/icces48766.2020.9137994 |
| Popis: | Automatic text classification is considered as a significant task in the current digital world where a huge volume of data is present in the form of text. So, it has been necessary to come up with efficient algorithms that could classify reviews and emotions provided by the user without human intervention. This will help others in choosing a better movie to watch. Several metrics such as accuracy, Recall, and area under the ROC curve are used to determine the efficiency of algorithms and to suggest accurate classifiers. In our paper, the efficiency of classical machine learning algorithms and deep learning architectures on the IMDB movie review dataset has been contrasted and proposed the most efficient architecture. The former part of the paper focuses on pre-processing the text documents and the latter part of the paper experiments with advanced deep learning techniques on the text. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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