Optimized Swarm Search-Based Feature Selection for Text Mining in Sentiment Analysis

2015 ◽  
Author(s):  
Simon Fong ◽  
Elisa Gao ◽  
Raymond Wong
Keyword(s):  
Feature Selection ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Selection For

scholarly journals Research of Clustering Algorithms using Enhanced Feature Selection

2019 ◽  
Vol 9 (2) ◽  
pp. 4612-4615
Keyword(s):  
Feature Selection ◽  
Text Mining ◽  
Clustering Algorithms ◽  
Present Situation ◽  
Features Selection ◽  
Entity Extraction ◽  
Text Documents ◽  
Clustering Techniques ◽  
Selection For ◽  
Video And Audio

In Present situation, a huge quantity of data is recorded in variety of forms like text, image, video, and audio and is estimated to enhance in future. The major tasks related to text are entity extraction, information extraction, entity relation modeling, document summarization are performed by using text mining. This paper main focus is on document clustering, a sub task of text mining and to measure the performance of different clustering techniques. In this paper we are using an enhanced features selection for clustering of text documents to prove that it produces better results compared to traditional feature selection.

scholarly journals Comparison of Naive Bayes Algorithm and Support Vector Machine using PSO Feature Selection for Sentiment Analysis on E-Wallet Review

2020 ◽  
Vol 1641 ◽  
pp. 012085
Author(s):  
Dwi Andini Putri ◽  
Dinar Ajeng Kristiyanti ◽  
Elly Indrayuni ◽  
Acmad Nurhadi ◽  
Denda Rinaldi Hadinata
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Selection For ◽  
Bayes Algorithm

scholarly journals Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 138-149
Author(s):  
Ukhti Ikhsani Larasati ◽  
Much Aziz Muslim ◽  
Riza Arifudin ◽  
Alamsyah Alamsyah
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Feature Weighting ◽  
Support Vector ◽  
Chi Square ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency

Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.

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