Enhanced Techniques for Opinion Mining
R. Cynthia Monica Priya1, J.G.R. Sathiaseelan2
1R.Cynthia Monica Priya*, Department of Computer Science, Bishop Heber College , Tiruchirapalli, India.
2Dr.J.G.R. Sathiaseelan, Department of Computer Science, Bishop Heber College , Tiruchirapalli, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 26, 2020. | Manuscript published on March 10, 2020. | PP: 1834-1838 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2968039520/2020©BEIESP | DOI: 10.35940/ijitee.E2968.039520
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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: The exponential growth of online information sharing has led to the need to analyze them. Opinion Mining or Sentiment Analysis incorporates a systematic approach to favor analysis of the available online synthesized data .This paper includes a lexicon based approach towards analysis. The proposed SLN (Slang Negation) Algorithm follows a lexicon based approach to Opinion Mining. The slang lexicon provides the expansion of the acronyms and their scores. Three types of negations namely explicit, implicit and pseudo negation are handed. Slang and negation handling are necessary to support semantic orientation. The proposed SLN algorithm has shown significant improvement in the precision, recall, f-measure and accuracy values as compared to the use of sentiment lexicon approach.
Keywords: lexicon, Negation, Pseudo, SLN Algorithm
Scope of the Article: Software Engineering Techniques and Production Perspectives