Evaluation and Prediction of Common Patterns Found In Public Accessed Data on Social Network.
Mohammad Shabaz1, Urvashi Garg2
1Mohammad Shabaz, Department of Computer Science Engineering, Chandigarh University, Mohali (Punjab), India.
2Urvashi Garg, Department of Computer Science Engineering, Chandigarh University, Mohali (Punjab), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1258-1262 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6577048419/19©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: It has been discussed several times about the uses and applications of sentimental analysis which is the extractions of opinions, feelings or emotions of an individual or an organization through some of their descriptive data which is freely available. There are several approaches exists which are helpful in differentiating individual’s opinions by evaluating data which is present over social networking sites in the form of comments or tweets or posts, either through classification or clustering. Number of algorithms working over evaluation of sentiments or opinion which includes AS, naïve-base, k-means etc. In this paper we are going to introduce US_Frequency formula which provides the US_Frequency value that helps to find the most frequent used word from the data-set extracted from social network data which in result predict the common patterns found. The data-set which is used in this paper consists of live data extracted from social networking sites using API’s. The patterns are based on the similarity between different individual exists in the same group or profession.
Keywords: Sentimental Analysis, Patterns Evaluations, Predictions, Opinion mining, Text Mining.
Scope of the Article: Text Mining