Helpfulness Prediction of Product Assessments using Machine Learning
Kagolanu Trishul1, Srinath R. Naidu2
1Kagolanu Trishul, Department of Computer Science & Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham (Tamil Nadu), India.
2Srinath R. Naidu, Department of Computer Science & Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham (Tamil Nadu), India.
Manuscript received on 04 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 05 September 2019 | PP: 369-376 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10680782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1068.0782S719
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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: Paper Customers express their opinion on products through reviews. Since there will be a lot of reviews that will be posted, only those reviews which are helpful should be made accessible to the customer. Hence, helpfulness of review needs to be predicted. This work categorizes the features into reviewer, review text and review metadata. Machine Learning algorithms Linear Regression and Random Forests are used for prediction of helpfulness using these features. It is observed that rating of a review has the highest influence on predicting helpfulness followed by user average rating deviation, difficult words and positive words. This work defines the features such as stem sim length and lem sim length which are derived from the product description which have performed reasonably well. Using all the features with Random Forests algorithm for prediction gave the best performance in automatically predicting helpfulness.
Keywords: Helpfulness Prediction, Lem Sim Length, Machine Learning, Random Forests, Stem Sim Length.
Scope of the Article: Machine Learning