scholarly journals Semisupervised Learning Based Opinion Summarization and Classification for Online Product Reviews

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Mita K. Dalal ◽  
Mukesh A. Zaveri
Keyword(s):  
Empirical Studies ◽  
Semisupervised Learning ◽  
Product Reviews ◽  
Automatic Summarization ◽  
Natural Language Text ◽  
Opinion Summarization ◽  
Average Accuracy ◽  
Online User Reviews ◽  
Comparative Feature ◽  
Feature Based

The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of users’ reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents a semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. It includes various phases like preprocessing and feature extraction and pruning followed by feature-based opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%.

scholarly journals Automatic Summarization in Chinese Product Reviews

2017 ◽  
Vol 15 (1) ◽  
pp. 373
Author(s):  
Li Zhen Liu ◽  
Wan Di Du ◽  
Han Shi Wang ◽  
Wei Song
Keyword(s):  
Product Reviews ◽  
Automatic Summarization

Identifying the Opinion Orientation of Online Product Reviews at Feature Level

2017 ◽  
Vol 8 (2) ◽  
pp. 92-111
Author(s):  
Nilanshi Chauhan ◽  
Pardeep Singh
Keyword(s):  
Sentiment Analysis ◽  
Present Article ◽  
Online Shopping ◽  
Service Providers ◽  
Online Reviews ◽  
Product Reviews ◽  
Striking Increase ◽  
Feature Based ◽  
Potential Customers ◽  
Pruning Methods

This article describes how e-commerce has become so vast that almost every product and service can be purchased online, to be delivered at our doorsteps. This has led to a striking increase in the number of online customers. In an attempt to make the online shopping more appealing and transparent to the online customers, the e-retailers allow their customers to express their opinion about the purchased products and services. Recently, analysis of such online reviews has become an active topic of research. This is because it is of immense concern to various stakeholders vs. online merchants, potential customers and the manufacturers of the particular product or service providers. The present article addresses the problem of summarization of such opinions expressed online and aims to create an organized feature-based summary as a solution. The proposed system depends on the frequency of occurrences of the potential features. A number of pruning methods are applied in order to obtain the final feature set and sentiment analysis has been done for each such feature.

Social Network Integration in Document Summarization

2014 ◽  
pp. 139-162 ◽  
Author(s):  
Atefeh Farzindar
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Semantic Analysis ◽  
Dynamic Network ◽  
Large Data ◽  
Automatic Summarization ◽  
Opinion Summarization ◽  
Event Based ◽  
Media Data

In this chapter, the author presents the new role of summarization in the dynamic network of social media and its importance in semantic analysis of social media and large data. The author introduces how summarization tasks can improve social media retrieval and event detection. The author discusses the challenges in social media data versus traditional documents. The author presents the approaches to social media summarization and methods for update summarization, network activities summarization, event-based summarization, and opinion summarization. The author reviews the existing evaluation metrics for summarization and the efforts on evaluation shared tasks on social data related tracks by ACL, TREC, TAC, and SemEval. In conclusion, the author discusses the importance of this dynamic discipline and great potential of automatic summarization in the coming decade, in the context of changes in mobile technology, cloud computing, and social networking.

A Novel Race Classification Method Based on Periocular Features Fusion

2017 ◽  
Vol 31 (08) ◽  
pp. 1750026 ◽  
Author(s):  
Hengxin Chen ◽  
Mingqi Gao ◽  
Karl Ricanek ◽  
Weiliang Xu ◽  
Bin Fang
Keyword(s):  
Image Size ◽  
Remarkable Feature ◽  
Features Fusion ◽  
Average Accuracy ◽  
Geometrical Features ◽  
Feature Based ◽  
Periocular Region ◽  
Application Fields ◽  
Local Feature Extraction ◽  
Human Eyes

Race identification is an essential ability for human eyes. Race classification by machine based on face image can be used in some practical application fields. Employing holistic face analysis, local feature extraction and 3D model, many race classification methods have been introduced. In this paper, we propose a novel fusion feature based on periocular region features for classifying East Asian from Caucasian. With the periocular region landmarks, we extract five local textures or geometrical features in some interesting regions which contain available discriminating race information. And then, these effective features are fused into a remarkable feature by Adaboost training. On the composed OFD-FERET face database, our method gets perfect performance on average accuracy rate. Meanwhile, we do a plenty of additional experiments to discuss the effect on the performance caused by gender, landmark detection, glasses and image size.

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