Gradual Weight Updating for Sentiment Mining
Sugandha Nandedkar1, Sunil Kawale2, Jayantrao Patil3
1Sugandha Nandedkar, Department of Computer Science and Engineering, Dr. B. A. M. University, Aurangabad (MS), India.
2Prof. Dr. Sunil Kawale, Post Graduate Department of Statistics, Dr. B. A. M. University, Aurangabad (MS), India.
3Prof. Dr. Jayantrao Patil, Department of Computer Science and Engineering, R. C. Patel Institute of Technology, Shirpur (MS), India.
Manuscript received on November 15, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 3895-3899 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4099129219/2019©BEIESP | DOI: 10.35940/ijeat.B4099.129219
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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: Nowadays, many people prefer the use of social media for communicating and exchanging opinions with each other over face to face communication. This has lead to a generation of a tremendous amount of textual opinioned data. Understanding this opinioned data is useful from all perspectives. But the major challenge exists here is how to extract the exact sentiment hidden behind this huge data. To solve this problem, keyword spotting or dictionary-based approaches are followed. In this paper, we present a Gradual Weight Updating for sentiment mining. It not only considers the polarity of each word similar to the unigram methodology but, it also focuses on the entire cluster of words that contains the unigram. The different steps it follows for sentiment extraction of the word are polarity fetching, cluster marking, weight tagging, valence shifter, adversative conjunction handling, and final score generation. The paper contributions in the area of domain independent opinioned word extraction and accurate polarity mining with the help of context marking approach. We used the various opinionated datasets to compare and illustrate the performance of our proposed system.
Keywords: Natural Language Processing, Opinion Mining, Sentiment Analysis, Text Mining.