Ensemble of feature sets and classification algorithms for sentiment classification

In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification. The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more a...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Information sciences Ročník 181; číslo 6; s. 1138 - 1152
Hlavní autoři: Xia, Rui, Zong, Chengqing, Li, Shoushan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 15.03.2011
Témata:
Algorithms
Bayesian analysis
Classification
Classifier combination
Comparative study
Ensemble learning
Maximum entropy
Sentiment classification
Strategy
Support vector machines
Tasks
Text classification
Texts
Classifier combination
Ensemble learning
Comparative study
Sentiment classification
Text classification
ISSN:0020-0255, 1872-6291
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, we make a comparative study of the effectiveness of ensemble technique for sentiment classification. The ensemble framework is applied to sentiment classification tasks, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure. First, two types of feature sets are designed for sentiment classification, namely the part-of-speech based feature sets and the word-relation based feature sets. Second, three well-known text classification algorithms, namely naı¨ve Bayes, maximum entropy and support vector machines, are employed as base-classifiers for each of the feature sets. Third, three types of ensemble methods, namely the fixed combination, weighted combination and meta-classifier combination, are evaluated for three ensemble strategies. A wide range of comparative experiments are conducted on five widely-used datasets in sentiment classification. Finally, some in-depth discussion is presented and conclusions are drawn about the effectiveness of ensemble technique for sentiment classification.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2010.11.023