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multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 80卷 323-339页

作者： Liu, Yang Bi, Jian-Wu Fan, Zhi-Ping Northeastern Univ Sch Business Adm Dept Management Sci & Engn Shenyang 110167 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

multi-class sentiment classification has extensive application backgrounds, whereas studies on this issue are still relatively scarce. In this paper, a framework for multi-class sentiment classification is proposed, which includes two parts: 1) selecting important features of texts using the feature selection algorithm, and 2) training multi-class sentiment classifier using the machine learning algorithm. Then, experiments are conducted for comparing the performances of four popular feature selection algorithms (document frequency, CHI statistics, information gain and gain ratio) and five popular machine learning algorithms (decision tree, naive Bayes, support vector machine, radial basis function neural network and K-nearest neighbor) in multi-class sentiment classification. The experiments are conducted on three public datasets which include twelve data subsets, and 10-fold cross validation is used to obtain the classification accuracy concerning each combination of feature selection algorithm, machine learning algorithm, feature set size and data subset. Based on the obtained 3600 classification accuracies (4 feature selection algorithms x 5 machine learning algorithms x 15 feature set sizes x 12 data subsets), the average classification accuracy of each algorithm is calculated, and the Wilcoxon test is used to verify the existence of significant difference between different algorithms in multi-class sentiment classification. The results show that, in terms of classification accuracy, gain ratio performs best among the four feature selection algorithms and support vector machine performs best among the five machine learning algorithms. In terms of execution time, the similar comparisons are also conducted. The obtained results would be valuable for further improving the existing multi-class sentiment classifiers and developing new multi-class sentiment classifiers. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： multi-class sentiment classification Experimental comparison Feature selection algorithms Machine learning algorithms

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A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm

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INFORMATION SCIENCES 2017年 394卷 38-52页

作者： Liu, Yang Bi, Jian-Wu Fan, Zhi-Ping Northwestern Univ Sch Business Adm Dept Informat Management & Decis Sci Shenyang 110167 Peoples R China Northwestern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

multi-class sentiment classification is a valuable research topic with extensive applications;however, studies in the field remain relatively scarce. In the present paper, a method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm is proposed. First, an improved OVO strategy is proposed wherein the relative competence weight of each binary classifier is determined according to the K nearest neighbors and the class center of each class in the training sample set concerning the binary classifier. A method for multi-class sentiment classification is proposed based on this improved OVO strategy and the SVM algorithm. After converting the training texts into term feature vectors, the important features (terms) for multi-class sentiment classification are selected using the information gain (IG) algorithm. A binary SVM classifier is then trained on the training feature vectors of each pair of sentiment classes. To identify the sentiment class of a test text, a confidence score matrix of multiple SVM classifiers is constructed based on the results of multiple SVM classifiers. Using this score matrix, the sentiment class of the test text can be determined using the improved OVO strategy. The results of our experimental studies show that the performance of the proposed method is significantly better than that of the existing methods for multi-class sentiment classification. (C) 2017 Elsevier Inc. All rights reserved.

关键词： multi-class sentiment classification One-vs-one (OVO) strategy Support vector machine (SVM) algorithm Experimental study

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multiclass sentiment classification of Online Health Forums using Both Domain-independent and Domain-specific Features 17

Multiclass Sentiment Classification of Online Health Forums ...

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10th International Conference on Utility and Cloud Computing (UCC) / 4th International Conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Alnashwan, Rana Sorensen, Humphrey O'Riordan, Adrian Hoare, Cathal Univ Coll Cork Dept Comp Sci Cork Ireland

ISBN: (纸本)9781450355490

Online health-related discussion provides a rich source of information for both informing the public and providing feedback to health professionals to detect trends and inform policy. However, there are few studies that focus on analysing sentiment in medical forum discourse. Online health communities devoted to specific medical conditions and health-related problems support people with similar conditions, enabling them to exchange personal experiences. Analysing sentiment expressed by members of a health community in medical forum discourse can be valuable for identifying a particular aspect of the information space. In this paper, we identify sentiments expressed on online medical forums discussing Lyme disease. There are two goals in our research. First, to identify a set of categories that can represent a comprehensive connotation of emotions expressed in the discussions, while also being adequately distinct for the purposes of machine learning. Second, to identify the sentiments expressed by participants in individual posts. Three types of feature (content-free, content-specific and meta-level) are extracted and inductive learning algorithms utilized to build a feature-based classification model for an automated multiclass classification model. The experimental results demonstrate the effectiveness of our approach.

关键词： Big Data multi-class sentiment classification Feature extraction Machine learning Text mining Online health community

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classification of Online Medical Discourse by Modified Co-training 5

Classification of Online Medical Discourse by Modified Co-tr...

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5th IEEE International Conference on Big Data Computing Service and Applications (IEEE BigDataService) / Workshop on Big Data in Water Resources, Environment, and Hydraulic Engineering / Workshop on Medical, Healthcare, Using Big Data Technologies

作者： Alnashwan, Rana Sorensen, Humphrey O'Riordan, Adrian Univ Coll Cork UCC Dept Comp Sci Cork Ireland Princess Nourah bint Abdulrahman Univ PNU Coll Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9781728100593

Online health communities have become an important resource that both patients and family members rely on to gain medical information and interact with contributors and experts outside the clinical setting. However, few studies have focused on analysing sentiment in medical forum discourse. Analysing sentiment expressed by members of a health community in medical forum discourse can be of significant value, such as by identifying characteristics of an information space, determining themes that predominate among a large dataset, and allowing people to summarize topics within a big data set. In this paper, we identify sentiment expressed in online medical forums discussing Lyme Disease and lupus. In many sentiment analysis applications, the labelled training datasets are fairly expensive to obtain, whereas unlabelled datasets are readily available. Therefore, we propose an adaptation of a well-known semi-supervised learning technique, in which co-training is implemented by combining labelled and unlabelled data. We use domain-independent and domain-dependent sentiment features as two distinct sets of views of a post and utilize them in order to identify the multi-class sentiment expressed using an adaptive co-training model. The results of our experiments demonstrate the effectiveness of the proposed approach.

关键词： big data multi-class sentiment classification semi-supervised teaming co-training feature extraction machine learning text mining online health community

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multi-class sentiment Analysis Comparison Using Support Vector Machine (SVM) and BAGGING Technique-An Ensemble Method

Multi-Class Sentiment Analysis Comparison Using Support Vect...

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International Conference on Smart Computing and Electronic Enterprise (ICSCEE)

作者： Sharma, Shashank Srivastava, Sumit Kumar, Ashish Dangi, Abhilasha Manipal Univ Sch Comp & IT Jaipur Rajasthan India

ISBN: (纸本)9781538648384

multi-class analysis, as the term suggest is the classification of the data in more than two classes. However not much studies were focused on such analysis and researchers often confined themselves to the binary sentiment classifiers. In this paper, we proposed machine learning algorithm as an approach to predict the sentiment classification. The experiments are conducted on public data sets combined with ensemble method named BAGGING, an abbreviation for Bootstrap aggregation with 10-cross fold validation technique is used to obtain the classification accuracy. The result accuracy suggested the exploring further improvement using the combination of the multi-class sentiment classifiers.

关键词： Bagging Ensemble method machine learning algorithm multi-class sentiment classification

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