Aspect Level Sentiment Polarity Classification Using Sequence Attention Mechanism with Amplified Intelligence
| dc.contributor.guide | Vadivu G | |
| dc.coverage.spatial | ||
| dc.creator.researcher | Sindhu C | |
| dc.date.accessioned | 2021-09-20T04:24:42Z | |
| dc.date.available | 2021-09-20T04:24:42Z | |
| dc.date.awarded | ||
| dc.date.completed | 2021 | |
| dc.date.registered | ||
| dc.description.abstract | With the exploding web technology, user generated content is all over the web and is accessible almost everywhere in this world. Such an accessibility has paved way for an easier suggestion seeking paradigm. In this fast-paced world, we humans find no time to stand and stare, which well suits this scenario, where, we don t want to read through each and every review of a product or a service. Sentiment Analysis is, thus an inevitable process, which helps every manufacturer and brand-owner to analyse the pros and cons of his own item and his competing products. Furthermore, be it any service or product, the experience shared about it is multi-dimensional with loads of emotions splashed into it newline | |
| dc.description.note | ||
| dc.format.accompanyingmaterial | DVD | |
| dc.format.dimensions | ||
| dc.format.extent | ||
| dc.identifier.uri | http://hdl.handle.net/10603/341153 | |
| dc.language | English | |
| dc.publisher.institution | Department of Computer Science Engineering | |
| dc.publisher.place | Kattankulathur | |
| dc.publisher.university | SRM University | |
| dc.relation | ||
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Automation and Control Systems | |
| dc.subject.keyword | Computer Science | |
| dc.subject.keyword | Engineering and Technology | |
| dc.title | Aspect Level Sentiment Polarity Classification Using Sequence Attention Mechanism with Amplified Intelligence | |
| dc.title.alternative | ||
| dc.type.degree | Ph.D. |
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