Enhanced Sentiment Analysis using Software Engineering with Machine Learning Algorithm
J.Satish Babu1, B.Jahnavi2, P.SaiAnusha3, S.Sai Manisha4, M.N Darshini5, G. Krishna Mohan6
1J.SatishBabu, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (A.P), India.
2B.Jahnavi, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (A.P), India.
3P.SaiAnusha, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (A.P), India.
4S.Sai Manisha, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (A.P), India.
5M.N Darshini, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (A.P), India.
6G. Krishna Mohan, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur (A.P), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1888-1891 | Volume-8 Issue-4, April 2019 | Retrieval Number: D7012048419/19©BEIESP
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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: Sentiment analysis has been related with different software engineering (SE) tasks, for example, Studies show that sentiment analysis instruments give dangerous outcomes when utilized out-of-the-compartment, since they are not expected to process SE datasets. The silver slug for a profitable use of sentiment analysis instruments to SE datasets may be their customization to the particular use setting. To achieve our objective, we retrained on a lot of 40k physically checked sentences/words secluded from Stack Overflow, a front line thought examination instrument misusing noteworthy learning. Despite such an exertion and dull preparing system, the outcomes were negative. We changed our concentration and played out a careful examination of the precision of these gadgets on a gathering of SEdatasets.
Keywords: Sentiment Analysis, Software Engineering, NLP.
Scope of the Article: Software Engineering