Span-based fine-grained sentiment analysis method

A sentiment analysis, fine-grained technology, applied in the field of sentiment analysis, can solve problems such as the inability to guarantee emotional consistency, the extraction of aspect items, and the inability to use global information

Active Publication Date: 2020-12-11
东北大学秦皇岛分校
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Problems solved by technology

When the aspect is composed of multiple words, the existing methods predict the labels of the words separately, which cannot make use of the global information, resulting in incorrect extraction of aspect i

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  • Span-based fine-grained sentiment analysis method
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  • Span-based fine-grained sentiment analysis method

Examples

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[0075] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The following examples are intended to illustrate the present invention, but not to limit the scope of the present invention.

[0076] The technical solution adopted by the present invention is a span-based fine-grained sentiment analysis method, comprising the following steps:

[0077] Step 1. Select the dataset to be subjected to sentiment analysis, given an input sentence s={w 1 ,w 2 ,...,w n }, where w n is the word, n is the length of the sentence;

[0078] This example uses three datasets. The first dataset, LAPTOP, contains reviews from SemEval2014 on the notebook domain. The second dataset REST comes from SemEval2014, SemEval2015 and SemEval2016, and contains reviews about restaurants. By merging the three-year training dataset and testing dataset, a new training dataset and testing dataset are obtained. ...

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Abstract

The invention provides a span-based fine-grained sentiment analysis method, and relates to the technical field of sentiment analysis. According to the invention, a sentiment analysis model based on aspects is established by selecting a sentiment analysis data set, the sentiment analysis model based on aspects is trained through a loss function and a training data set, and sentiment analysis of thetext is achieved by making the test data set to be subjected to sentiment analysis pass through the trained sentiment analysis model based on aspects. The network model provided by the invention is used for extracting aspects and corresponding sentiment polarities, and the model is a simple and effective joint model for extracting sentence aspects and corresponding sentiment polarities for sentiment analysis tasks. The model uses BERT as word embedding, then uses a loop control unit to extract the representation of each subtask, uses an interaction layer to consider the relationship between them, and finally performs aspect item extraction and sentiment analysis.

Description

technical field [0001] The invention relates to the technical field of sentiment analysis, in particular to a span-based fine-grained sentiment analysis method. Background technique [0002] With the development of the Internet age, social networking based on network platforms has become an indispensable part of people's lives. Users are no longer satisfied with obtaining information unilaterally, but actively create information. More and more users are keen to express their emotions, opinions and attitudes on the Internet, such as sharing their viewing experience of a certain movie on a movie website, expressing their opinions on something on a social network, and sharing their views on a certain thing on a shopping website. Post a review on a product, etc. For a large amount of comment data, it is difficult for people to quickly extract information that is effective for them. Therefore, sentiment analysis (Sentiment analysis) came into being. Sentiment analysis, also k...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06N3/04G06N3/08
CPCG06F16/353G06N3/08G06N3/048G06N3/045
Inventor 吕艳霞魏方娜郑莹
Owner 东北大学秦皇岛分校
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