Please use this identifier to cite or link to this item:
https://rda.sliit.lk/handle/123456789/1363
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cooray, T. | - |
dc.contributor.author | Perera, G. | - |
dc.contributor.author | Chandrasena, D. | - |
dc.contributor.author | Alosius, J. | - |
dc.contributor.author | Kugathasan, A. | - |
dc.date.accessioned | 2022-02-23T05:25:33Z | - |
dc.date.available | 2022-02-23T05:25:33Z | - |
dc.date.issued | 2020-12-10 | - |
dc.identifier.isbn | 978-1-7281-8412-8 | - |
dc.identifier.uri | http://rda.sliit.lk/handle/123456789/1363 | - |
dc.description | Print on Demand(PoD) ISBN:978-1-7281-8413-5 | en_US |
dc.description.abstract | Aspect-based sentiment analysis (ABSA) is used in different fields for analyzing customer reviews to project an overall customer opinion on certain products. With the expansion of the internet, people are provided with an inexpensive and time-saving method to express their opinion to a larger audience, while various industries are handed with the opportunity to gather free information from it to obtain market value. The implementation of machine learning methods for the evaluation of aspects related to movies and television series has not been commenced, and it could be a new development for the industry. This study focuses on conducting an ABSA on a movie or a television series based on genre, story as well as cast and crew aspects. The data collected from social media through web scraping is processed to produce adequate results to get a broad understanding on how the popularity of the movie or the television series related to above mentioned aspects. Then, each aspect is further analyzed to gather precise information belonging to each aspect. The accuracy of the results of the proposed system has been achieved over 79%. The results proved that the solution is highly successful than the former works with high business value. | en_US |
dc.language.iso | en | en_US |
dc.publisher | 2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT | en_US |
dc.relation.ispartofseries | Vol.1; | - |
dc.subject | Aspect Based Sentiment Analysis | en_US |
dc.subject | Natural Language Processing | en_US |
dc.subject | Artificial Neural Network | en_US |
dc.subject | Support Vector Machine | en_US |
dc.subject | Bitmask Bidirectional Long Short-Term Memory | en_US |
dc.title | Aspect Based Sentiment Analysis for Evaluating Movies and TV series Publisher: IEEE Cite This PDF | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ICAC51239.2020.9357129 | en_US |
Appears in Collections: | 2nd International Conference on Advancements in Computing (ICAC) | 2020 Department of Computer Science and Software Engineering-Scopes Department of Computer Science and Software Engineering-Scopes |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Aspect_Based_Sentiment_Analysis_for_Evaluating_Movies_and_TV_series.pdf Until 2050-12-31 | 396.18 kB | Adobe PDF | View/Open Request a copy |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.