Mixed Based Classifier Approach for Sentiment Analysis
| dc.contributor.author | Bhatia, Sudhanhsu | |
| dc.contributor.supervisor | Mishra, Ashutosh | |
| dc.contributor.supervisor | Miglani, Sumit | |
| dc.date.accessioned | 2015-07-28T06:10:42Z | |
| dc.date.available | 2015-07-28T06:10:42Z | |
| dc.date.issued | 2015-07-28T06:10:42Z | |
| dc.description | M.E. (Software Engineering) | en |
| dc.description.abstract | The increasing expansion of social media stuff provides massive collection of textual information. People share their thoughts and views on the WEB. So sentiment analysis used to classifies the sentiments or the opinions from this huge amount of data. There are already many algorithms to find the sentiment form the data but there are many difficulties present to handle data like slang words and miss-spelling so the efficiency and the accuracy of these algorithms became poor. In this methodology the underlying idea is to achieve a particular accuracy rate by a new mixed algorithm by using different approaches like POS, N-Gram and some lexicon techniques. | en |
| dc.description.sponsorship | Computer Science and Engineering, Thapar Univesity, Patiala | en |
| dc.format.extent | 2338051 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10266/3425 | |
| dc.language.iso | en | en |
| dc.subject | twitter API | en |
| dc.subject | Google API | en |
| dc.subject | Sentiment Analysis | en |
| dc.subject | Mongo DB | en |
| dc.subject | N-Gram | en |
| dc.subject | Computer Science | en |
| dc.subject | CSED | en |
| dc.title | Mixed Based Classifier Approach for Sentiment Analysis | en |
| dc.type | Thesis | en |
