Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| Định dạng: | Luận văn |
| Ngôn ngữ: | English |
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Brac University
2024
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/22084 |
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10361-22084 |
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10361-220842024-03-13T21:00:55Z Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning Sarkar, Ripa Hassan, Md. Mehedi Azad, Farin Beante Hossin, Md. Nibras Shakil, Arif Department of Computer Science and Engineering, Brac University Facebook YouTube Positive Negative Sarcastic Logistic regression K-nearest neighbor Multinomial naive bayes Random forest Tree classifier Support vector machine Adaboost classifier Gaussian naive bayes Machine learning Natural language processing (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 80-86). Internet-based applications like social media platforms and blogs have exploded in popularity, and with them have come reviews and commentary on people’s daily lives. Unfortunately, the vast majority of these evaluations and commentary are critical. even in material devoted to reviewing food, like food blogs, vlogs and cook ing videos. Data collection and analysis based on people’s subjective feelings about a certain topic, product, subject, or service is known as sentiment analysis. By using techniques from natural language processing and text mining, sentiment analysis is able to recognize and extract empathetic details from written content. In this study, we’ll go through a high-level introduction to the process for doing so, as well as the uses of sentiment analysis. After that, it analyzes the methods in order to weigh their merits and drawbacks via a series of comparisons and assessments. Several classifiers (Logistic Regression, Multinomial Naive Bayes, K-Nearest Neighbors, De cision Tree, Random Forest, AdaBoost, and SVM) are used to divide the sentiment into one of three categories, like positive, negative, or sarcastic. Ripa Sarkar Md. Mehedi Hassan Farin Beante Azad Md. Nibras Hossin B.Sc. in Computer Science 2024-01-09T05:54:32Z 2024-01-09T05:54:32Z 2022 2022-09 Thesis ID: 18201083 ID: 19101570 ID: 19101598 ID: 20301463 http://hdl.handle.net/10361/22084 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. 86 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Facebook YouTube Positive Negative Sarcastic Logistic regression K-nearest neighbor Multinomial naive bayes Random forest Tree classifier Support vector machine Adaboost classifier Gaussian naive bayes Machine learning Natural language processing (Computer science) |
| spellingShingle |
Facebook YouTube Positive Negative Sarcastic Logistic regression K-nearest neighbor Multinomial naive bayes Random forest Tree classifier Support vector machine Adaboost classifier Gaussian naive bayes Machine learning Natural language processing (Computer science) Sarkar, Ripa Hassan, Md. Mehedi Azad, Farin Beante Hossin, Md. Nibras Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Shakil, Arif |
| author_facet |
Shakil, Arif Sarkar, Ripa Hassan, Md. Mehedi Azad, Farin Beante Hossin, Md. Nibras |
| format |
Thesis |
| author |
Sarkar, Ripa Hassan, Md. Mehedi Azad, Farin Beante Hossin, Md. Nibras |
| author_sort |
Sarkar, Ripa |
| title |
Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning |
| title_short |
Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning |
| title_full |
Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning |
| title_fullStr |
Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning |
| title_full_unstemmed |
Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning |
| title_sort |
sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22084 |
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