Identification of Astrological belief using Sentimental Analysis by Capturing Opinions from Cross-Domain Individuals
C. N. V. B. R. Sri Gowrinath1, B. Srinivasa S. P. Kumar2, Chilukuri Megh Phani Dutt3
1C. N. V. B. R. Sri Gowrinath, Assistant Professor, CBIT, Hyderabad B. Srinivasa S. P. Kumar, Assistant Professor, CBIT, Hyderabad.
2Chilukuri Megh Phani Dutt, Graduate Engineer Trainee, Mobis India Limited Research and Development Centre, Hyderabad.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3075-3080 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6002018520/2020©BEIESP | DOI: 10.35940/ijrte.E6002.018520
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Astrology usage is substantially composed of the belief and opinions by individuals in recent times. Astrology describes a huge set of predictions by using a wide range of horoscope charts along with scientific/mathematical computations. Planets movement around twelve houses is the vital thing for Astrological specifications. Astrology is having mutual sides of belief in proportionate and the belief is identified by using concepts like Sentiment analysis, Naïve Bayes classifiers and others. Sentiment Analysis specifies the odds in-favor and against the concept. The current study describes a model to identify the belief in and against cases on Astrological belief; also identifies the deviated opinions with the help of various features using confusion matrix. Training, Tuning and other analytical activities are used to build and verify a model for accuracy. The present study is mainly emphasized on identifying the belief, which is very useful to derive an unambiguous thesis on Astrology in nearby future.
Keywords: Astrology, Confusion Matrix, Tuning, Training, Accuracy, Semantria, Precision, Sentiment analysis.
Scope of the Article: Predictive Analysis.