Twitter viewpoint classification-oriented sentiment-enriched word embedding learning method

Abstract

The invention provides a Twitter viewpoint classification-oriented sentiment-enriched word embedding learning method, and relates to the technical field of computers. When word level n-gram and polarity information are modeled at the same time, sentiment polarity information of tweet document level is modeled, sentiment information of the word level is integrated, and the word level is naturally input to convolution to serve as an input of the tweet level. When learnt words are embedded in a Twitter viewpoint polarity classification task, an experimental result on a standard data set shows that the method provided by the invention is superior to existing similar methods.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to an emotion-enhanced word embedding learning method for Twitter opinion classification. Background technique [0002] Twitter, one of the largest microblogging sites on the Internet, has become one of the important sources of opinion and sentiment online. Due to its massive, diverse, and steadily rising user base, opinion information contained in Twitter has been successfully applied to a variety of tasks, such as stock market trend forecasting, information monitoring of political leaders, and inferring public opinion on public events, etc. Therefore, efficient positive, negative and neutral viewpoint recognition performance is fundamental to the application task. [0003] Scholars have proposed various methods to improve the performance of opinion analysis on Twitter. In particular, the development of deep neural networks in recent years has proved the importance of text repr...

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Problems solved by technology

Therefore, it is not possible to directly use existing multi-task deep learning frameworks to deal with this problem

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