Sarcastic Sentiment Detection with Fuzzy Logic
Vijaykumar S. Bidve1, Kalyani Pathak2, Kruttika Bhagwat3, Karishma Suryawanshi4
1Vijaykumar S. Bidve*, Information Technology Department Marathwada Mitra Mandal’s, College of Engineering, Pune.
2Kalyani Pathak, Information Technology Department Marathwada MitraMandal’s, College of Engineering, Pune.
3Kruttika Bhagwat, Information Technology Department Marathwada Mitra Mandal’s, College of Engineering, Pune.
4Karishma Suryawanshi, Information Technology Department Marathwada MitraMandal’s, College of Engineering, Pune
Manuscript received on June 01, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 808-813 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9911069520/2020©BEIESP | DOI: 10.35940/ijeat.E9911.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the context of text classification of sentiment detection to great, instances are naturally fuzzy and therefore to get clear-cut outcome by extracting the opinion in a new innovative way i.e. In a fuzzy combination way and assign a relevant sentiment, usually either positive or negative. Due to this new approach it will be possible to have advancement for extracting the opinions of people more deeply even if instance in a sentences are manipulated more complexly.
Keywords: Mixed feature rule formation algorithm, sentiment detection, twitter, machine learning, bag of words, doc2vec.