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Multi-granularity viewpoint mining method based on personal computer e-commerce comments

An opinion mining and computer technology, which is applied in the field of multi-granularity opinion mining based on personal computer e-commerce reviews, and can solve the problem of not being able to mine review feedback information automatically.

Inactive Publication Date: 2019-12-10
XIAN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology helps research individuals who have been buying products online or through other channels like social media about their experiences with them more easily by analyzing how they reacted towards these things over time. It can help companies better predict future sales trends based upon factors such as customer preferences and purchasing behavior patterns.

Problems solved by technology

This patented technical problem addressed by this patents relates to improving understanding of how well electronic commerce websites provide users with valuable insights into products they purchase through reviewings made manually. However current systems lack sufficient accuracy due to factors like commentary bias caused by human error during evaluations, which makes them difficult to interpret complex opinions that contain many different aspects related to each other. Therefore there remains room for improvement.

Method used

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  • Multi-granularity viewpoint mining method based on personal computer e-commerce comments
  • Multi-granularity viewpoint mining method based on personal computer e-commerce comments
  • Multi-granularity viewpoint mining method based on personal computer e-commerce comments

Examples

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Embodiment

[0075] Firstly, we crawled Lenovo laptop reviews from JD.com’s website, and took one of them as a corpus sample: “The appearance is beautiful, the speed is fast, the most important thing is that it is super convenient to carry, and the battery life is strong! But the delivery is a bit slow. ".

[0076] According to the forward maximum matching algorithm based on the dictionary, a string to be segmented is taken from the original text for word segmentation, at this time s 1 = "The appearance is beautiful, the speed is fast, the most important thing is that it is super convenient to carry, and the battery life is strong! The delivery is a bit slow." According to the constructed dictionary, maxlen is determined to be = 10, s 2 Initialized to nothing, the dictionary is constructed as a hash table.

[0077] from s 1 Select the substring w with a length not greater than maxlen on the left side of , and judge whether w is empty or not, and judge whether w is a word in the hash tabl...

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Abstract

The invention discloses a multi-granularity viewpoint mining method based on personal computer e-commerce comments, and the method comprises the steps: firstly obtaining the e-commerce comments from awebpage, and carrying out the automatic marking of the comments; preprocessing the e-commerce comments; recognizing 'aspect-viewpoint' pairs of single sentences in the preprocessed corpus, and distinguishing viewpoint words from aspect words; carrying out phrase-level sentiment classification on the context phrases of the aspect; and finally, carrying out text-level sentiment classification on the whole comments, so as to obtain viewpoint mining results of the whole commodity and all aspects. The problem that in the prior art, feedback information of all attribute aspects contained in comments cannot be automatically mined is solved.

Description

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Claims

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Application Information

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Owner XIAN UNIV OF TECH
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