Sentiment Orientation Classification of Webpage Online Commentary Based on Intuitionistic Fuzzy Reasoning

2013 ◽  
Vol 347-350 ◽  
pp. 2369-2374 ◽  
Author(s):  
Xiao Feng Li ◽  
Dong Li
Keyword(s):  
High Precision ◽  
Membership Function ◽  
Fuzzy Reasoning ◽  
Sentiment Classification ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Online Comments ◽  
Semantic Orientation ◽  
Sentiment Orientation

An approach of sentiment classification for online comments based on intuitionistic fuzzy reasoning is presented on the basis of the analysis of characteristics of sentiment classification. The approach employs membership function, non-membership function and hesitant function to depict uncertainties of features, quantitatively, by sample training, as well as sentiment expressions influenced by adverbs of degree, conjunctions and negative words are considered. Then the semantic orientation of a text is synthesized on the level of phrases, sentences and texts in sequence by means of aggregations of intuitionistic fuzzy information of features. The presented approach obtains high precision and recall when test using public corpus

scholarly journals Intuitionistic Fuzzy Similarity Measures and Their Role in Classification

2016 ◽  
Vol 25 (2) ◽  
pp. 221-237 ◽  
Author(s):  
Leila Baccour ◽  
Adel M. Alimi ◽  
Robert I. John
Keyword(s):  
Similarity Measures ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Fuzzy Information ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Similarity ◽  
Metric Distance ◽  
Handwritten Arabic ◽  
Selection Of

AbstractWe present some similarity and distance measures between intuitionistic fuzzy sets (IFSs). Thus, we propose two semi-metric distance measures between IFSs. The measures are applied to classification of shapes and handwritten Arabic sentences described with intuitionistic fuzzy information. The experimental results permitted to do a comparative analysis between intuitionistic fuzzy similarity and distance measures, which can facilitate the selection of such measure in similar applications.

MODELS FOR MULTIPLE ATTRIBUTE DECISION MAKING WITH INTUITIONISTIC FUZZY INFORMATION

2007 ◽  
Vol 15 (03) ◽  
pp. 285-297 ◽  
Author(s):  
Z. S. XU
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Membership Function ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Multiple Attribute Decision Making ◽  
Fuzzy Information ◽  
Attribute Weights ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute

The intuitionistic fuzzy set (IFS) characterized by a membership function and a non-membership function, was introduced by Atanassov [K. Atanassov, "Intuitionistic fuzzy sets", Fuzzy Sets and Systems 20 (1986) 87–96] as a generalization of Zadeh' fuzzy set [L. A. Zadeh, "Fuzzy Sets", Information and Control 8 (1965) 338–353] to deal with fuzziness and uncertainty. In this paper, we investigate the multiple attribute decision making (MADM) problems, in which the information about attribute weights is incomplete, and the attribute values are expressed in intuitionistic fuzzy numbers (IFNs). We first define the concept of intuitionistic fuzzy ideal solution (IFIS), and then, based on the IFIS and the distance measure, we establish some optimization models to derive the attribute weights. Furthermore, based on the developed models, we develop some procedures for the rankings of alternatives under different situations, and extend the developed models and procedures to handle the MADM problems with interval-valued intuitionistic fuzzy information. Finally, we give some illustrative examples to verify the effectiveness and practicability of the developed models and procedures.

SNIPPET-BASED UNSUPERVISED APPROACH FOR SENTIMENT CLASSIFICATION OF CHINESE ONLINE REVIEWS

2011 ◽  
Vol 10 (06) ◽  
pp. 1097-1110 ◽  
Author(s):  
YIJUN LI ◽  
QIANG YE ◽  
ZIQIONG ZHANG ◽  
TIENAN WANG
Keyword(s):  
Search Engine ◽  
Basic Assumption ◽  
Online Reviews ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Data Set ◽  
Customer Reviews ◽  
Unsupervised Approach ◽  
Sentiment Orientation

Sentiment classification seeks to identify general attitude of a piece of text of comments or reviews on certain subject, be it positive or negative. Most existing researches on sentiment classification employ supervised learning approaches that rely on annotated data. However, sentiment is expressed differently on different subjects in different domains, and having annotated corpora for every domain of interest is not always practical. This paper proposes an unsupervised learning approach for classifying text of online reviews as recommended or not recommended. The proposed method is based on search engine snippet, summary information on the result page of a search engine. A basic assumption is that terms with similar orientation tend to co-occur. The co-occurrence is measured by utilizing snippets returned from search engines, with a query consisting of the text and a seed positive or negative word. With the information of snippets, the proposed method may estimate the association of candidate terms more accurately. This allows us to reliably predict the sentiment orientation of customer reviews. Texts of customer reviews are then classified as recommended or not recommended if the average sentiment orientations of its phrases are positive or negative. The research data set of this study consists of 600 Chinese online reviews about travel destinations retrieved from Ctrip.com. Our approach achieves an accuracy of 76.5%. Factors that influence the accuracy of the sentiment classification of Chinese online reviews were discussed.

High-precision Classification of Small Insects with Similar Color and Shape Using Image Processing

2016 ◽  
Vol 136 (8) ◽  
pp. 1120-1127 ◽  
Author(s):  
Naoya Ikemoto ◽  
Kenji Terada ◽  
Yuta Takashina ◽  
Akio Nakano
Keyword(s):  
Image Processing ◽  
High Precision

Methods for evaluating the computer network security with fuzzy number intuitionistic fuzzy dual Hamy mean operators

2020 ◽  
Vol 39 (3) ◽  
pp. 4427-4441
Author(s):  
Bin Xu
Keyword(s):  
Network Security ◽  
Membership Function ◽  
Fuzzy Number ◽  
Fuzzy Numbers ◽  
Computer Network ◽  
Information Aggregation ◽  
Security Evaluation ◽  
Uncertain Information ◽  
Intuitionistic Fuzzy ◽  
Computer Network Security

The concept of fuzzy number intuitionistic fuzzy sets (FNIFSs) is designed to effectively depict uncertain information in decision making problems which fundamental characteristic of the FNIFS is that the values of its membership function and non-membership function are depicted with triangular fuzzy numbers (TFNs). The dual Hamy mean (DHM) operator gets good performance in the process of information aggregation due to its ability to capturing the interrelationships among aggregated values. In this paper, we used the dual Hamy mean (DHM) operator and dual weighted Hamy mean (WDHM) operator with fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number intuitionistic fuzzy dual Hamy mean (FNIFDHM) operator and fuzzy number intuitionistic fuzzy weighted dual Hamy mean (FNIFWDHM) operator. Then the MADM methods are proposed along with these operators. In the end, we utilize an applicable example for computer network security evaluation to prove the proposed methods.

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