A Construction Method of Hyperbolic Representation Lexicon Oriented to Chinese Ironic Text

Abstract

Irony detection aims to analyze and identify language expressions containing irony in texts, which can assist text sentiment analysis and opinion mining. Hyperbolic expressions are the unique language characteristics in Chinese ironic texts that often highlight the critics’ ironic intentions. Existing methods of irony domain lexicon construction ignore the hyperbolic expressions in the construction process, resulting in incomplete semantic information contained in the lexicon. Considering this problem, this paper proposes a method for constructing a hyperbolic representation lexicon to mine the language feature and improve the accuracy of irony detection. Firstly, we select the candidate words and word pairs of hyperbolic representation by Word2Vec combined with the K-means++ algorithm. Then, the information entropy is calculated to measure the correlation between candidate words and texts, so the seed word set with a high correlation with texts is obtained. Finally, we expand the seed word set using WoBERT to capture the text’s deep semantic information. Thus, the generalization ability of the hyperbolic representation lexicon is improved. The experimental results show that combining the lexicon constructed in this paper can effectively mine the language features of ironic texts, thereby improving the accuracy of irony detection. The experimental indicators Acc and F1-score have an average improvement of about 2.38% and 2.29%, respectively.

Keywords

• Chinese ironic detection
• hyperbolic representation lexicon
• semantic information
• WoBERT