Feature-based Opinion Summarization Research Articles

Editorial preface for the special issue “Advances in security, privacy and trust technologies”

With the fast development of Internet technologies, the information and communication systems are continuously facing new security, trust and privacy challenges. Indeed, the potentially invasive nature of the information has implications on privacy of the users and their online activity such as transactions. Likewise, the implicit reliance on technology to make decisions on one’s behalf makes essential to study new mechanisms for expressing and reasoning about trust. This special issue brings together research articles covering diverse aspects of security, privacy and trust in computer and networked-based information systems, including privacy concerns, tools to help secure information, methods for development and assessment of trustworthy systems, analysis of vulnerabilities and attacks, trends and new developments, secure operating systems and applications, security issues in wireless networks and pervasive systems, design and test strategies for secure and survivable systems, and cryptology, among others. The papers of the special issue are selected based on strict review results, where papers went through three rounds of review and revision. As a result, nine papers were selected and are arranged in the special issue as follows. Xiong et al. in the first paper ‘‘The finite volume element method for a parameter identification problem’’ use finite volume element method to solve a parameter identification problem of parabolic equations with over-specified data. The authors provide the numerical scheme of the unknown function and control parameters and obtain the error estimates of approximate solution. The proposed method is experimentally evaluated and compared with the exact solution to confirm the good accuracy of the presented scheme. The second paper by Chen et al. ‘‘An Opinion Mining Framework For Cantonese Reviews’’, analyze opinion mining for Cantonese language. The authors motivate the need to construct a particular lexical database for Cantonese and explore some Cantonese special written-tradition rules and incorporate them into the feature-based opinion summarization system framework. The experimental results show that the proposed framework significantly outperforms the traditional Mandarin sentiment analysis method using ICTCLAS. In the third paper ‘‘A Secure Remote Data Integrity Checking Cloud Storage System from Threshold Encryption’’, Yao et al. study the confidentiality and integrity issues in cloud storage system. The authors address the privacy issue of decentralized cloud storage system using threshold cryptography. The threshold encryption scheme is therefore integrated with a secure decentralized erasure code to form a secure cloud storage system. Zhang et al. in the fourth paper ‘‘How to build a faster private information retrieval protocol’’, propose a secure multi-bit homomorphic encryption scheme based on Learning With Errors over Rings (RLWE) assumption. The authors use canonical embedding to transform ring & Fatos Xhafa fatos@cs.upc.edu

Discovering Chinese sentence patterns for feature-based opinion summarization

This study discovers part-of-speech (POS) patterns of sentences that express opinions in Chinese product reviews. The use of these patterns makes it possible to identify opinion sentences, feature words, and opinion/feeling words. Degree words and negation words are used in determining the orientation of opinions as well as the degree of their intensity. In order to identify the subject of opinions, the associations between opinion/feeling words, feature words, and corresponding features were ascertained. An algorithm for feature-based opinion summarization is then proposed based on these patterns and association rules. Both car and movie reviews were collected for discovering patterns and testing of the patterns and algorithm. The experimental results demonstrate that the proposed algorithm and approaches perform well on Chinese product reviews.

An opinion mining framework for Cantonese reviews

Many research works are concentrating in how to handle the opinion mining for English, Chinese and Japanese, etc. However, little work has been done on opinion mining for Cantonese which is a world-wide influential language with 70 million speakers. In this paper, we point out that instead of utilizing the Mandarin lexical database, it’s necessary to construct a particular lexical database for Cantonese. Besides, we explore some Cantonese special written-tradition rules and incorporate them into the feature-based opinion summarization system framework. The experimental results show that our framework significantly outperform the traditional Mandarin sentiment analysis method using ICTCLAS.

Semisupervised Learning Based Opinion Summarization and Classification for Online Product Reviews

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%.