Abstract
This paper proposes deep context by word vectors for deceptive review identification. The basic idea is that since deceptive reviews and truthful reviews are composed by writers without and with real experience, respectively, there should be different contexts of words used by them. Unlike previous work using the whole text collection to learn the word vectors, we produce two numerical vectors for each word by embedding contexts of words in deceptive and truthful reviews separately. Specifically, we propose a representation method called DCWord (Deep Context representation by Word vectors) to use average word vectors derived from deceptive and truthful contexts, respectively, to represent reviews for further classification. Then, we investigate three classifiers as support vector machine (SVM), simple logistic regression (LR) and back propagation neural network (BPNN) to identify the deceptive reviews. Experimental results on the Spam dataset demonstrate that by using the DCWord representation, SVM and LR have produced comparable performance and they outperform BPNN in deceptive review identification. The outcome of this study provides potential implications for online business intelligence in identifying deceptive reviews.
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Notes
- 1.
USPTO stop words, online: https://www.uspto.gov/patft//help/stopword.htm.
- 2.
QTag for English part-of-speech, online: http://www.english.bham.ac.uk/staff/omason/software/qtag.html.
- 3.
Porter stemming algorithm, online: http://tartarus.org/martin/PorterStemmer/.
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Acknowledgment
This research was supported in part by National Natural Science Foundation of China under Grant Nos. 71101138, 61379046, 91218301, 91318302 and 61432001; Beijing Natural Science Fund under Grant No. 4122087; the Fundamental Research Funds for the Central Universities (buctrc201504).
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Zhang, W., Jiang, Y., Yoshida, T. (2016). Deep Context Identification of Deceptive Reviews Using Word Vectors. In: Chen, J., Nakamori, Y., Yue, W., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2016. Communications in Computer and Information Science, vol 660. Springer, Singapore. https://doi.org/10.1007/978-981-10-2857-1_19
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