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Orthographic features for emotion classification in Chinese in informal short texts

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Abstract

Informal short texts on the web are rich in emotions as they often reflect unfiltered immediate reactions to breaking news events. The emotion density, however, stands in contrast to its poverty of linguistic contexts and features for emotion classification. This paper tackles that challenge by proposing orthographic features based on orthographic code mixing and code-switching for both non-ML and ML approaches. Our results show that orthographic features routinely outperform grammatical features for emotion classification for short texts in all approaches as expected. Orthographic features were also shown to make more significant contributions, especially in terms of precision and in formal texts when state of the art deep learning algorithms are applied. This result confirms the effectiveness of the orthographic change feature to the task of emotion classification. These results are argued to be applicable to all languages because of the common code-shifting in languages with non-Latin orthographies, and the use of non-letter symbols in all languages.

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Notes

  1. Source website: https://www.bilibili.com/ and the datasets included in the: https://github.com/yunfeilongpoly/ource_for_Orthographic_features_for_Emotion_Classification_in_Chinese.

  2. First, we segment each text file into words (for English splitting by space), and then we count times for each word that occurs in each document. Afterwards, we assign each word an integer id. Each unique word in our dictionary corresponds to a feature (descriptive feature) that we can use tf-idf to further reduce the weight of high-frequency words such as the, is, an, which occur in all documents.

  3. https://www.ltp-cloud.com/demo/.

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Authors and Affiliations

  1. Chinese and Bilingual Studies, Hong Kong Polytechnic University, Hung Hom, China

    I-Hsuan Chen & Chu-Ren Huang

  2. Department of Computing, Hong Kong Polytechnic University, Hung Hom, China

    Yunfei Long

  3. School of Computer Science and Electronic Engineering, University of Essex, Hung Hom, China

    Yunfei Long

  4. Department of Computing, Hong Kong Polytechnic University, Hung Hom, China

    Qin Lu

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  1. I-Hsuan Chen
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Chen, IH., Long, Y., Lu, Q. et al. Orthographic features for emotion classification in Chinese in informal short texts. Lang Resources & Evaluation 55, 329–352 (2021). https://doi.org/10.1007/s10579-020-09515-3

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