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Ph.D.
(Engineering & Technology)
WEB PERSONALIZATION USING OPINION MINING

Ph.D. Scholar : Patel Jitali Dineshkumar
Research Supervisor : Dr. Hitesh Chhinkaniwala

Regi. No.: 14146051010
Abstract :
Referring to reviews, checking online comments and, visiting different websites before
buying any product is a call of the day. People go through reviews even before they go for
dinner in a restaurant. Mundane activities like purchasing daily wear or grocery or even
vegetables are heavily dominated by recommendations from friends, family members,
colleagues etc. Thus online reviews are an excellent source of information both for users
and organizations alike. In this thesis, a hybrid model-named aspect and context-based
latent factor model (ACMF) is proposed to predict user rating on an item based on star
ratings provided by users, feature-opinion information, and context information. ACMF
mainly consists of three phases: The first phase extracts spam reviews and discards
them, the second phase extracts features and opinions from written reviews and
eventually calculates the polarity score of opinions. In the last phase, ratings, reviews, and
context information us aggregated to predict the unknown rating of a user for better
recommendations and further improve user experience and address the sparsity
problem. The proposed model is tested on ratings and reviews downloaded from the
Amazon website. Experiment results show root mean squared error(RMSE) of ACMF has
been achieved significantly less than other relevant methods indicate that incorporating
sentiment and context aspects in the latent factor model can aid in improving the
recommendation performance.

Key words: Web personalization, Opinion mining, Sentiment analysis, Recommender
System, Sentiment aware Recommender System, Matrix Factorization, Context-aware
Matrix Factorization, User-generated text, Collaborative filtering.

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