Sentiment Classification of Opinions based on Multi-source Transfer Learning Using Structural Correspondence Learning

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Dehghani Ashkezari Saeed; DERHAMI VALI; ZAREH BIDOKI ALI MOHAMMAD; Basiri Mohammad Ehsan

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Journal: JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT) Year:2019 | Volume:8 | Issue:2 Page(s): 89-101

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Title

Sentiment Classification of Opinions based on Multi-source Transfer Learning Using Structural Correspondence Learning

Author(s)

Dehghani Ashkezari Saeed | DERHAMI VALI | ZAREH BIDOKI ALI MOHAMMAD | Basiri Mohammad Ehsan | Issue Writer Certificate

Keywords

Sentiment Classification of OpinionsQ1

Transfer LearningQ1

Structural Correspondence LearningQ1

Multi-sourceQ1

Boosting AlgorithmQ1

Abstract

Sentiment Classification of Opinions is a field of Natural Language Processing which has been considered in recent years by researchers due to popularity of Internet stores and the possibility of expressing opinions about sold goods or services. To train classifier models, we need labeled datasets, but as there are not rich labeled samples and as labeling is a difficult and time-consuming process, we must employ labeled samples of other domains. In this article, a new method for binary classification of opinions is proposed based on multi-domain Transfer Learning. The proposed method tries to adapt different domains by using Structural Correspondence Learning; and based on repetitive procedure of the Boosting Algorithm, a weight is assigned to classified samples of different domains and the class of each opinion is specified by merging these classifiers. Weighting the dataset samples to boost the process of classification based on the Adaboost algorithm and combining it with the Structural Corresponding Learning is the most important innovation of the current research. The Amazon dataset of four different domains, each one containing 1000 positive and 1000 negative opinions is used for training the proposed model. Accuracy measures of %89. 64, %93. 97, %92. 39 and %90. 17 are obtained for Electronics, DVD, Books and Kitchen domains, respectively. It illustrates that the proposed method is very effective compared with the similar methods.

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APA: Copy

Dehghani Ashkezari, Saeed, DERHAMI, VALI, ZAREH BIDOKI, ALI MOHAMMAD, & Basiri, Mohammad Ehsan. (2019). Sentiment Classification of Opinions based on Multi-source Transfer Learning Using Structural Correspondence Learning. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), 8(2 ), 89-101. SID. https://sid.ir/paper/245866/en

Vancouver: Copy

Dehghani Ashkezari Saeed, DERHAMI VALI, ZAREH BIDOKI ALI MOHAMMAD, Basiri Mohammad Ehsan. Sentiment Classification of Opinions based on Multi-source Transfer Learning Using Structural Correspondence Learning. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT)[Internet]. 2019;8(2 ):89-101. Available from: https://sid.ir/paper/245866/en

IEEE: Copy

Saeed Dehghani Ashkezari, VALI DERHAMI, ALI MOHAMMAD ZAREH BIDOKI, and Mohammad Ehsan Basiri, “Sentiment Classification of Opinions based on Multi-source Transfer Learning Using Structural Correspondence Learning,” JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), vol. 8, no. 2 , pp. 89–101, 2019, [Online]. Available: https://sid.ir/paper/245866/en

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