Social media trend analysis to predict the success of products using deep learning technique
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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| التنسيق: | أطروحة |
| اللغة: | English |
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Brac University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10361/21814 |
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10361-21814 |
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10361-218142023-10-15T21:05:05Z Social media trend analysis to predict the success of products using deep learning technique Khan, Farden Ehsan Ruhan, Ahmed Mahir Shamsuddin, Rifat Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Social media Trend analysis Deep learning Sentiment analysis KNN Text mining MLP RoBERTa Random forest BART Decision Tree DistilBERT Social media--Economic aspects Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-38). 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. Farden Ehsan Khan Ahmed Mahir Ruhan Rifat Shamsuddin B.Sc. in Computer Science 2023-10-15T07:03:05Z 2023-10-15T07:03:05Z ©2022 2022-09-29 Thesis ID 19101418 ID 19101330 ID 19101336 http://hdl.handle.net/10361/21814 en 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. 48 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Social media Trend analysis Deep learning Sentiment analysis KNN Text mining MLP RoBERTa Random forest BART Decision Tree DistilBERT Social media--Economic aspects Machine learning |
| spellingShingle |
Social media Trend analysis Deep learning Sentiment analysis KNN Text mining MLP RoBERTa Random forest BART Decision Tree DistilBERT Social media--Economic aspects Machine learning Khan, Farden Ehsan Ruhan, Ahmed Mahir Shamsuddin, Rifat Social media trend analysis to predict the success of products using deep learning technique |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Ashraf, Faisal Bin |
| author_facet |
Ashraf, Faisal Bin Khan, Farden Ehsan Ruhan, Ahmed Mahir Shamsuddin, Rifat |
| format |
Thesis |
| author |
Khan, Farden Ehsan Ruhan, Ahmed Mahir Shamsuddin, Rifat |
| author_sort |
Khan, Farden Ehsan |
| title |
Social media trend analysis to predict the success of products using deep learning technique |
| title_short |
Social media trend analysis to predict the success of products using deep learning technique |
| title_full |
Social media trend analysis to predict the success of products using deep learning technique |
| title_fullStr |
Social media trend analysis to predict the success of products using deep learning technique |
| title_full_unstemmed |
Social media trend analysis to predict the success of products using deep learning technique |
| title_sort |
social media trend analysis to predict the success of products using deep learning technique |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21814 |
| work_keys_str_mv |
AT khanfardenehsan socialmediatrendanalysistopredictthesuccessofproductsusingdeeplearningtechnique AT ruhanahmedmahir socialmediatrendanalysistopredictthesuccessofproductsusingdeeplearningtechnique AT shamsuddinrifat socialmediatrendanalysistopredictthesuccessofproductsusingdeeplearningtechnique |
| _version_ |
1814307152188145664 |