Abstract
With the opinion explosion on Web, there are growing research interests in opinion mining. In this study we focus on an important problem in opinion mining — Aspect Identification (AI), which aims to extract aspect terms in entity reviews. Previous PLSA based AI methods exploit the 2-tuples (e.g. the co-occurrence of head and modifier), where each latent topic corresponds to an aspect. Here, we notice that each review is also accompanied by an entity and its overall rating, resulting in quad-tuples joined with the previously mentioned 2-tuples. Believing that the quad-tuples contain more co-occurrence information and thus provide more ability in differentiating topics, we propose a model of Quad-tuple PLSA, which incorporates two more items — entity and its rating, into topic modeling for more accurate aspect identification. The experiments on different numbers of hotel and restaurant reviews show the consistent and significant improvements of the proposed model compared to the 2-tuple PLSA based methods.
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Luo, W., Zhuang, F., He, Q., Shi, Z. (2012). Quad-tuple PLSA: Incorporating Entity and Its Rating in Aspect Identification. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_33
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DOI: https://doi.org/10.1007/978-3-642-30217-6_33
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