Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches

Chaovalit, Pimwadee; Zhou, Liang

doi:10.1109/hicss.2005.445

Cited by 250 publications

(164 citation statements)

References 6 publications

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“…They concluded that due to scarcity of words in movie reviews, it is hard to use bag-of-words features using supervised learning methods. Their results were 85.54 % for 3-fold cross validation and 66.27 % when applied on the test data set [4].…”

Section: Related Workmentioning

confidence: 66%

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

et al. 2016

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With the rapid development of the World Wide Web, electronic word-of-mouth interaction has made consumers active participants. Nowadays, a large number of reviews posted by the consumers on the Web provide valuable information to other consumers. Such information is highly essential for decision making and hence popular among the internet users. This information is very valuable not only for prospective consumers to make decisions but also for businesses in predicting the success and sustainability. In this paper, a Gini Index based feature selection method with Support Vector Machine (SVM) classifier is proposed for sentiment classification for large movie review data set. The results show that our Gini Index method has better classification performance in terms of reduced error rate and accuracy.

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Section: Related Workmentioning

confidence: 66%

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

et al. 2016

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“…Many datasets don't have all data' label information. The main problem is that gathering label information takes long time and costly process [5]. On the other hand, unlabeled data is easy to obtain and also plenty in literature for researchers.…”

Section: Active Sample Selection In Ensemble Learningmentioning

confidence: 99%

“…On the other hand, unlabeled data is easy to obtain and also plenty in literature for researchers. At this stage, clustering methods are used for unlabeled data classification which is known as unsupervised method in literature but success of the separation the data is low than supervised methods' success [5]. Recently, many studies are made on forming new classification models which is based on supervised techniques but use less labeled data in training process because of difficulty in obtaining [10], [11].…”

Section: Active Sample Selection In Ensemble Learningmentioning

confidence: 99%

“…Success rate of those methods are generally high because the label information is given in train data. Methods working with labeled train set are named as supervised methods in literature [5]. But datasets with enough labels are not easy to find for studies.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it is impossible to get all labels in correctly split up [6]. In that case some other machine learning algorithms called as unsupervised methods are improved [5]. K-Means, which based on distances of given clusters' centers, is an example of unsupervised methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing Informative Sample Selection Strategies in Classification Ensembles

İlhan¹,

Amasyalı²

2014

IJMLC

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Abstract-Usage of more training data with label information gives more success for classification of datasets in machine learning. But in real life, obtaining data with label information is a cost-effective and long-lasting process. Herein, active learning algorithms are emerged. Active learning algorithms aim to maintain current success rate with fewer samples in train set or increase total success of model in training process. Active learning is not only functional for regular learning methods but also can be used in ensemble learning algorithms with specified techniques. In this study, two different active learning algorithms based on class probabilities of the samples are tested on five datasets classification. Ensemble learning methods are used as classification model. Comparative results presented as graphically and numerically.

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Ontology‐supported polarity mining

Zhou

Chaovalit

2007

J. Am. Soc. Inf. Sci.

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Polarity mining provides an in‐depth analysis of semantic orientations of text information. Motivated by its success in the area of topic mining, we propose an ontology‐supported polarity mining (OSPM) approach. The approach aims to enhance polarity mining with ontology by providing detailed topic‐specific information. OSPM was evaluated in the movie review domain using both supervised and unsupervised techniques. Results revealed that OSPM outperformed the baseline method without ontology support. The findings of this study not only advance the state of polarity mining research but also shed light on future research directions.

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Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches

Abstract: Abstract

Cited by 250 publications

References 6 publications

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier

Comparing Informative Sample Selection Strategies in Classification Ensembles

Ontology‐supported polarity mining

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