  Opinion mining mainly involves three elements: feature and feature-of
relations, opinion expressions and the related opinion attributes (e.g.
Polarity), and feature-opinion relations. Although many works have emerged to
achieve its aim of gaining information, the previous researches typically
handled each of the three elements in isolation, which cannot give sufficient
information extraction results; hence, the complexity and the running time of
information extraction is increased. In this paper, we propose an opinion
mining extraction algorithm to jointly discover the main opinion mining
elements. Specifically, the algorithm automatically builds kernels to combine
closely related words into new terms from word level to phrase level based on
dependency relations; and we ensure the accuracy of opinion expressions and
polarity based on: fuzzy measurements, opinion degree intensifiers, and opinion
patterns. The 3458 analyzed reviews show that the proposed algorithm can
effectively identify the main elements simultaneously and outperform the
baseline methods. The proposed algorithm is used to analyze the features among
heterogeneous products in the same category. The feature-by-feature comparison
can help to select the weaker features and recommend the correct specifications
from the beginning life of a product. From this comparison, some interesting
observations are revealed. For example, the negative polarity of video
dimension is higher than the product usability dimension for a product. Yet,
enhancing the dimension of product usability can more effectively improve the
product (C) 2015 Elsevier Ltd. All rights reserved.
