| Type of Material: | Thesis |
| Title: | Efficient multi domain adaptation in sentiment analysis using machine learning and cross domain semantic library |
| Researcher: | Patel, Dipakkumar Chinubhai |
| Guide: | Amin, Kiran R |
| Department: | FACULTY OF ENGINEERING AND TECHNOLOGY |
| Publisher: | Ganpat University |
| Place: | Mehsana |
| Year: | 2023 |
| Language: | English |
| Subject: | Computer Science | Computer Science Information Systems | Engineering and Technology | Computer Science and Applications | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; FACULTY OF ENGINEERING AND TECHNOLOGY, Ganpat University, Mehsana; 2023; 17276341004 |
| Fulltext: | Shodhganga |
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| 001 | 455355 | |
| 003 | IN-AhILN | |
| 005 | 2024-09-25 10:03:13 | |
| 008 | __ | 240925t2023||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_455355 |
| 040 | __ | |aGANU_384012|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aPatel, Dipakkumar Chinubhai|eResearcher |
| 110 | __ | |aFACULTY OF ENGINEERING AND TECHNOLOGY|bGanpat University|dMehsana|ein|0U-0132 |
| 245 | __ | |aEfficient multi domain adaptation in sentiment analysis using machine learning and cross domain semantic library |
| 260 | __ | |aMehsana|bGanpat University|c2023 |
| 300 | __ | |a1167 KB|dDVD |
| 500 | __ | |aSentiment Classification, Multi-Domain Sentiment Analysis, Domain Adaptation, Enhanced Cross Entropy, Improve Grey Wolf Optimization |
| 502 | __ | |cFACULTY OF ENGINEERING AND TECHNOLOGY, Ganpat University, Mehsana|d2023|o17276341004|bPhD |
| 518 | __ | |d2023|oDate of Award |
| 518 | __ | |oDate of Registration|d2017 |
| 518 | __ | |oDate of Notification|d2023-09-25 |
| 518 | __ | |oDate of Viva-voce|d2023-09-25 |
| 520 | __ | |aNowadays, the rapid growth of the internet has led to the way for most effortless data generation. These data can be in the form of web pages, blogs, emails, posts on various social networks, or anything that is uploaded to the internet. There must be a technique to retrieve valuable information from this vast data storage. Classification is one of the retrieval techniques for automatic categorization of the data into specified categories. Sentiment Analysis (SA) is the classification problem that is necessary to scrutinize the user-generated data into any of the two classifications (negative or positive). Sentiment Analysis is implemented by machine learning techniques and lexicon-oriented techniques. Due to accuracy, simplicity, and adaptability, machine-learning approaches have lured the researchers. Traditional sentiment analysis techniques are trained on one topic (also called the domain) and tested on the same topic. The domain on which the machine is trained is called the source domain, and the test |
| 650 | __ | |2UGC|aComputer Science and Applications |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Information Systems |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aAmin, Kiran R|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/515449|yShodhganga |
| 905 | __ | |afromsg |
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