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Document-level sentiment classification using hybrid machine learning approach

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Abstract

It is a practice that users or customers intend to share their comments or reviews about any product in different social networking sites. An analyst usually processes to reviews properly to obtain any meaningful information from it. Classification of sentiments associated with reviews is one of these processing steps. The reviews framed are often made in text format. While processing the text reviews, each word of the review is considered as a feature. Thus, selection of right kind of features needs to be carried out to select the best feature from the set of all features. In this paper, the machine learning algorithm, i.e., support vector machine, is used to select the best features from the training data. These features are then given input to artificial neural network method, to process further. Different performance evaluation parameters such as precision, recall, f-measure, accuracy have been considered to evaluate the performance of the proposed approach on two different datasets, i.e., IMDb dataset and polarity dataset.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Abinash Tripathy, Abhishek Anand & Santanu Kumar Rath

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  1. Abinash Tripathy
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  2. Abhishek Anand
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  3. Santanu Kumar Rath
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Correspondence to Abinash Tripathy.

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Tripathy, A., Anand, A. & Rath, S.K. Document-level sentiment classification using hybrid machine learning approach. Knowl Inf Syst 53, 805–831 (2017). https://doi.org/10.1007/s10115-017-1055-z

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  • DOI: https://doi.org/10.1007/s10115-017-1055-z

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