Social media trend analysis to predict the success of products using deep learning technique
| bracu.degree.level | Undergraduate | |
| bracu.type.group | Student Works | |
| datacite.rights | Open Access | |
| dc.contributor.advisor | Ashraf, Faisal Bin | |
| dc.contributor.author | Khan, Farden Ehsan | |
| dc.contributor.author | Ruhan, Ahmed Mahir | |
| dc.contributor.author | Shamsuddin, Rifat | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| dc.date.accessioned | 2023-10-15T07:03:05Z | |
| dc.date.available | 2023-10-15T07:03:05Z | |
| dc.date.copyright | ©2022 | |
| dc.date.issued | 9/29/2022 | |
| dc.description | Cataloged from PDF version of thesis. | |
| dc.description | Includes bibliographical references (pages 37-38). | |
| dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. | en_US |
| dc.description.abstract | In recent times, social media usage has reached such heights that it has become a powerhouse in producing trends, bringing such topics that would have remained outside of popular consciousness. Our goal is to analyze how the success of products such as movies can be affected by people’s shared thoughts and reactions about it on social media. From the data extracted from social media comments, we will study the sentiment of people regarding a certain movie. For our research, the work will be based on unreleased movies and predict the outcome after release. Accumulated reviews about a movie will be analyzed to decipher whether the public sentiment is positive or negative towards it and estimate the willingness to buy a specific film. From this we will find the correlation between how positive and negative attention can affect the success of a production. | en_US |
| dc.description.degree | Bachelor of Science in Computer Science | |
| dc.description.statementofresponsibility | Farden Ehsan Khan | |
| dc.description.statementofresponsibility | Ahmed Mahir Ruhan | |
| dc.description.statementofresponsibility | Rifat Shamsuddin | |
| dc.format.extent | 48 pages | |
| dc.identifier.other | ID 19101418 | |
| dc.identifier.other | ID 19101330 | |
| dc.identifier.other | ID 19101336 | |
| dc.identifier.uri | http://hdl.handle.net/10361/21814 | |
| dc.language.iso | en | en_US |
| dc.publisher | BRAC University | en_US |
| dc.rights | Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. | |
| dc.subject | Social media | en_US |
| dc.subject | Trend analysis | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Sentiment analysis | en_US |
| dc.subject | KNN | en_US |
| dc.subject | Text mining | en_US |
| dc.subject | MLP | en_US |
| dc.subject | RoBERTa | en_US |
| dc.subject | Random forest | en_US |
| dc.subject | BART | en_US |
| dc.subject | Decision Tree | en_US |
| dc.subject | DistilBERT | en_US |
| dc.subject.lcsh | Social media--Economic aspects | |
| dc.subject.lcsh | Machine learning | |
| dc.title | Social media trend analysis to predict the success of products using deep learning technique | en_US |
| dc.type | Thesis | en_US |