Mining Aspects on the Social Network
Sapna Juneja1, Abhinav Juneja2, Rohit Anand3, Paras Chawla4
1Sapna Juneja, Department of Computer Science, B.M.I E.T., Sonepat, India.
2Abhinav Juneja, Department of Computer Science, B.M.I.E.T., Sonepat, India.
3Rohit Anand, Department of Electronics and Communication Engineering, G.B. Pant Engg, College, New Delhi, India.
4Paras Chawla, Department of Electronics and Communication Engineering, Chandigarh University, Mohali (Punjab), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 26 August 2019 | PP: 285-289 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10450789S19/19©BEIESP | DOI: 10.35940/ijitee.I1045.0789S19
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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: This paper proposes an effective concept of mining the feedback of product given by the user. In return various solutions are suggested according to the ratings of the aspect and its corresponding weightage. The satisfaction of user is determined by the help of user’s rating and weight of the aspect determines the significance of each aspect in the user’s review. These methodologies are thus, important and play a significant role for the manufacturers and producers to improvise their product and eventually leading to rise in the market value of that particular product. The methodology here extracts the aspects from the feedbacks of users with the help of conditional probability and bootstrap technique. Also an approach that is supervised and is called by the name, Naïve Bayes is used to classify aspect ratings and the sentiment words are considered as properties or features.
Keywords: Aspect, Aspect Extraction, Aspect Weight, Aspect Ratings, Core Term, Naïve Bayes Conditional Probability.
Scope of the Article: Social Networks