A Feature Selection Method Based on Fisher’s Discriminant Ratio for Text Sentiment Classification

Wang, Suge; Li, Deyu; Wei, Yingjie; Li, Hongxia

doi:10.1007/978-3-642-05250-7_10

Cited by 38 publications

(19 citation statements)

References 10 publications

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“…O' keefe et al [15] compared three feature selection methods and feature weighting scheme for sentiment classification. Wang et al [14] proposed a new Fisher's discriminant ratio based feature selection method for text sentiment classification. Abbasi et al [17] found that information gain or genetic algorithm improves the accuracy of sentiment classification.…”

Section: Related Workmentioning

confidence: 99%

Optimal Feature Selection for Sentiment Analysis

Agarwal

Mittal

2013

Lecture Notes in Computer Science

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Abstract. Sentiment Analysis (SA) research has increased tremendously in recent times. Sentiment analysis deals with the methods that automatically process the text contents and extract the opinion of the users. In this paper, unigram and bi-grams are extracted from the text, and composite features are created using them. Part of Speech (POS) based features adjectives and adverbs are also extracted. Information Gain (IG) and Minimum Redundancy Maximum Relevancy (mRMR) feature selection methods are used to extract prominent features. Further, effect of various feature sets for sentiment classification is investigated using machine learning methods. Effects of different categories of features are investigated on four standard datasets i.e. Movie review, product (book, DVD and electronics) review dataset. Experimental results show that composite features created from prominent features of unigram and bi-gram perform better than other features for sentiment classification. mRMR is better feature selection method as compared to IG for sentiment classification. Boolean Multinomial Naïve Bayes (BMNB) algorithm performs better than Support Vector Machine (SVM) classifier for sentiment analysis in terms of accuracy and execution time.

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Section: Related Workmentioning

confidence: 99%

Optimal Feature Selection for Sentiment Analysis

Agarwal

Mittal

2013

Lecture Notes in Computer Science

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“…Hence, a feature selection phase is essential in our case. We apply two state-of-the-art feature selection methods that are proven effective in text categorization and sentiment classification, mutual information (MI) and Fisher discriminant ratio (FDR), to select the final subjective features from a document (Yang and Pedersen 1997;Wang et al 2009). …”

Section: Feature Selectionmentioning

confidence: 99%

“…; nÞ denote the ith positive document and jth negative document, respectively. We define two random variables d P,i (f) and d N,j (f) as in Wang et al (2009).…”

Section: Fisher Discriminant Ratiomentioning

confidence: 99%

Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents

2011

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With the growth of social media, document sentiment classification has become an active area of research in this decade. It can be viewed as a special case of topical classification applied only to subjective portions of a document (sources of sentiment). Hence, the key task in document sentiment classification is extracting subjectivity. Existing approaches to extract subjectivity rely heavily on linguistic resources such as sentiment lexicons and complex supervised patterns based on part-of-speech (POS) information. This makes the task of subjective feature extraction complex and resource dependent. In this work, we try to minimize the dependency on linguistic resources in sentiment classification. We propose a simple and statistical methodology called review summary (RSUMM) and use it in combination with well-known feature selection methods to extract subjectivity. Our experimental results on a movie review dataset prove the effectiveness of the proposed methodology.

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“…Though many researchers have investigated opinion classification from different perspectives, use of machine learning for opinion classification counts more [1,10,11,[19][20][21][22][23][24]. Among the Machine learning techniques, it is observed that SVM, naive bayes (NB) and decision tree approaches have achieved great success in opinion categorization [1,10,11,13,14,21,25].…”

Section: Review Of Literaturementioning

confidence: 99%

Opinion mining using principal component analysis based ensemble model for e-commerce application

Vinodhini

Chandrasekaran

2014

CSIT

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With the rapid expansion of e-commerce over the decades, more and more product reviews emerge on e-commerce sites. In order to effectively utilize the information available in the form of reviews, an automatic opinion mining system is needed to organize the reviews and to help the users and organizations in making an informed decision about the products. Opinion mining systems based on machine learning approaches are used to categorize the reviews containing the customer opinion into positive or negative reviews. In this paper we explore this new research area of applying a hybrid combination of machine learning approaches tied with principal component analysis as a feature reduction technique. We introduce two hybrid ensemble based models (i.e. bagging and bayesian boosting based) for opinion classification. The results are compared with two individual classifier models based on statistical learning (i.e. logistic regression and support vector machine) using a dataset of product reviews. The other objective is to compare the influence of using different n-gram schemes (unigrams, bigrams and trigrams). We found that ensemble based hybrid methods perform better in terms of various quality measures in classifying the opinion into positive and negative reviews. We also applied a pairwise statistical test to compare the significance of the classifiers.

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A Feature Selection Method Based on Fisher’s Discriminant Ratio for Text Sentiment Classification

Cited by 38 publications

References 10 publications

Optimal Feature Selection for Sentiment Analysis

Optimal Feature Selection for Sentiment Analysis

Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents

Opinion mining using principal component analysis based ensemble model for e-commerce application

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