Personalised book recommendation system based on opinion mining technique

2015 ◽  
Author(s):  
Kumari Priyanka ◽  
Anand Shanker Tewari ◽  
Asim Gopal Barman
Keyword(s):  
Recommendation System ◽  
Opinion Mining ◽  
Mining Technique

scholarly journals Symptoms to Disease Mapping and Doctor Recommendation System

2019 ◽  
Vol 9 (1) ◽  
pp. 1111-1119
Keyword(s):  
Decision Tree ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Disease Mapping ◽  
Uneven Distribution ◽  
Bayes Classifier ◽  
Doctor Who ◽  
Mining Technique ◽  
Review Mining ◽  
Doctor Recommendation

To find an appropriate doctor who is specialized to treat a certain disease while only symptoms are known is not easy job for the patients. In this paper, we describe a recommended framework to find the best doctors in accordance with patients' requirements. In the proposed system, first it considers only those doctors whose profile match with patients' requirements. Second, the best doctors will be recommended out of previously obtained doctors based on the parameter patients' feedback i.e., patients' review. Our proposal will suggest a doctor recommendation system that uses review mining technique, which can be used in those countries that have huge uneven distribution of medical resources. In our model we have used the decision tree for symptoms to disease mapping and Naive Bayes classifier for sentiment analysis which are connected to each other using a bridge of python logic and the required output is top doctors based on the users input

A hybrid recommender system for the mining of consumer preferences from their reviews

2019 ◽  
Vol 46 (5) ◽  
pp. 664-682
Author(s):  
Li Chen Cheng ◽  
Ming-Chan Lin
Keyword(s):  
Opinion Mining ◽  
Information Overload ◽  
Consumer Preferences ◽  
Online Reviews ◽  
User Generated Content ◽  
Mining Technique ◽  
Movie Recommendation ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Filtering Approach

Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users’ reviews to detect consumer opinions about the movie they have watched and to explore the individual’s preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of ‘data sparsity’. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods.

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