Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System

2014 ◽  
pp. 272-284 ◽  
Author(s):  
Ekaterina Pronoza ◽  
Elena Yagunova ◽  
Svetlana Volskaya
Keyword(s):  
Information Extraction ◽  
Recommendation System ◽  
Opinion Mining

Ontology-Based Opinion Mining for Online Product Reviews

2019 ◽  
pp. 123-148 ◽  
Author(s):  
Farheen Siddiqui ◽  
Parul Agarwal
Keyword(s):  
Information Extraction ◽  
Opinion Mining ◽  
Domain Ontology ◽  
Literature Survey ◽  
Extensive Literature ◽  
Product Reviews ◽  
Product Domain ◽  
User Centric ◽  
Selection Of

In this chapter, the authors work at the feature level opinion mining and make a user-centric selection of each feature. Then they preprocess the data using techniques like sentence splitting, stemming, and many more. Ontology plays an important role in annotating documents with metadata, improving the performance of information extraction and reasoning, and making data interoperable between different applications. In order to build ontology in the method, the authors use (product) domain ontology, ConceptNet, and word net databases. They discuss the current approaches being used for the same by an extensive literature survey. In addition, an approach used for ontology-based mining is proposed and exploited using a product as a case study. This is supported by implementation. The chapter concludes with results and discussion.

Product Recommendation System using Opinion Mining

2019 ◽  
Vol 67 (3) ◽  
pp. 66-69
Author(s):  
Ajith Kumar.V ◽  
Arun B ◽  
Balamurugan J ◽  
Nancy Deborah.R
Keyword(s):  
Recommendation System ◽  
Opinion Mining ◽  
Product Recommendation

scholarly journals A Neural Network-Inspired Approach for Improved and True Movie Recommendations

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Muhammad Ibrahim ◽  
Imran Sarwar Bajwa ◽  
Riaz Ul-Amin ◽  
Bakhtiar Kasi
Keyword(s):  
Neural Network ◽  
Data Analytics ◽  
Recommendation System ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Mobile App ◽  
Apache Hadoop ◽  
External Resources ◽  
Movie Recommendation ◽  
Day By Day

In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.

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