CRFs (conditional random fields) and SVM (support vector machine) based method for extracting fine-granularity sentiment elements in product reviews

A technology of element extraction and product reviews, applied in natural language data processing, special data processing applications, instruments, etc., can solve problems such as the gap of comprehensive effects, improve the accuracy rate and recall rate, improve the accuracy of sentiment classification, and improve generalization. Effects of Capability and Robustness

Active Publication Date: 2014-03-19
青岛类认知人工智能有限公司
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Problems solved by technology

Although many scholars have conducted some research on the analysis of sentiment orientation and achieved a lot of results, in terms of fine

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  • CRFs (conditional random fields) and SVM (support vector machine) based method for extracting fine-granularity sentiment elements in product reviews
  • CRFs (conditional random fields) and SVM (support vector machine) based method for extracting fine-granularity sentiment elements in product reviews
  • CRFs (conditional random fields) and SVM (support vector machine) based method for extracting fine-granularity sentiment elements in product reviews

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[0132] Experiments were conducted on two different datasets using the proposed method. A data set is obtained by grabbing the latest product reviews from Tmall Mall, 20 electronic products, a total of 3146 review data, 500 of which are used as a training set, and the rest are used as a test set, represented by Dataset1. The other data set comes from the data of COAE2013 task 3. 2000 pieces of data are randomly selected from task 3 for manual labeling, 500 of which are used as the training set, and the rest are used as the test set, represented by DataSet2. For both datasets, cross-validation was used for parameter tuning. Table 2 shows some emotional objects and emotional words extracted by the system from the data set, and Table 1 shows the statistics of the results extracted from the open test.

[0133] Table 1 Comment object-comment word pair

[0134]

[0135] Table 2 Review Objects - Review Words and Words Extraction Results

[0136]

[0137]As can be see...

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Abstract

The invention discloses a CRFs (conditional random fields) and SVM (support vector machine) based method for extracting fine-granularity sentiment elements in product reviews. The method comprises the steps as follows: a, a CRFs model is adopted, review language characteristics are taken as sequences, then position labelling is performed on review languages according to the sequences, corresponding rules are adopted to perform stratified filtering on error labels, and extraction for sentiment subjects and sentiment words is finished; and b, an SVM model is adopted to perform sentiment orientation analysis on word pairs according to the extracted sentiment subjects and sentiment words as well as introduced sentence structure features. According to the invention, the sentiment subjects and the sentiment words in review sentences are extracted together, further, sentiment classification accuracy in the sentiment orientation analysis is improved, so that the sentiment element extraction and sentiment judgment are improved, and F value is up to 76.3%; due to introduction of word meaning codes, the generalization ability and the robustness of a system are improved by virtue of the word meaning codes, and the accurate rate and recall rate of review result analysis are greatly improved.

Description

technical field [0001] The invention belongs to the technical field of computer information mining and relates to the extraction of emotional elements of products, in particular to the extraction of fine-grained emotional elements of product reviews based on CRFs and SVM. Background technique [0002] With the rapid growth of Internet users and the continuous popularity of online shopping, e-commerce has experienced explosive development. Although traditional shopping cannot be banned, online shopping has gradually become people's preferred way, because in the online shopping environment, users can Through online product reviews, you can get more and more comprehensive shopping reference information at any time, and have a more comprehensive understanding of product quality. At the same time, users often participate in product evaluation after purchasing products, making product review data become Getting bigger and bigger. Compared with business promotion, review data can ...

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/36G06F40/211G06F40/253
Inventor 孙晓唐陈意叶嘉麒李承程任福继
Owner 青岛类认知人工智能有限公司
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