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Interpretable Sentiment Analysis and Text Segmentation for Chinese Language. / Hou, Zhenghao; Колонин, Антон Германович.

в: Optical Memory and Neural Networks (Information Optics), Том 33, № Suppl 3, 23.01.2025, стр. S483-S489.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Hou, Z & Колонин, АГ 2025, 'Interpretable Sentiment Analysis and Text Segmentation for Chinese Language', Optical Memory and Neural Networks (Information Optics), Том. 33, № Suppl 3, стр. S483-S489. https://doi.org/10.3103/S1060992X24700759

APA

Hou, Z., & Колонин, А. Г. (2025). Interpretable Sentiment Analysis and Text Segmentation for Chinese Language. Optical Memory and Neural Networks (Information Optics), 33(Suppl 3), S483-S489. https://doi.org/10.3103/S1060992X24700759

Vancouver

Hou Z, Колонин АГ. Interpretable Sentiment Analysis and Text Segmentation for Chinese Language. Optical Memory and Neural Networks (Information Optics). 2025 янв. 23;33(Suppl 3):S483-S489. doi: 10.3103/S1060992X24700759

Author

Hou, Zhenghao ; Колонин, Антон Германович. / Interpretable Sentiment Analysis and Text Segmentation for Chinese Language. в: Optical Memory and Neural Networks (Information Optics). 2025 ; Том 33, № Suppl 3. стр. S483-S489.

BibTeX

@article{4a2688795da340b087228edf2778efa5,
title = "Interpretable Sentiment Analysis and Text Segmentation for Chinese Language",
abstract = "Abstract: In this paper, we explored the performance of interpretable sentiment analysis models of different combinations for the Chinese text in social media. We made experiment to study how performance varies with the change of combination of different segmentation strategies and dictionary of words or n-grams. We found that with some good combination of segmentation strategies and dictionary of words or n-grams, the result can be improved and overtake the performance of ordinary sentiment analysis model of Chinese language. This way we show the importance of selection of segmentation strategies and dictionary for the sentiment analysis of Chinese text.",
keywords = "Chinese text, interpretable artificial intelligence, nature language processing, sentiment analysis, social media",
author = "Zhenghao Hou and Колонин, {Антон Германович}",
year = "2025",
month = jan,
day = "23",
doi = "10.3103/S1060992X24700759",
language = "English",
volume = "33",
pages = "S483--S489",
journal = "Optical Memory and Neural Networks (Information Optics)",
issn = "1934-7898",
publisher = "Pleiades Publishing",
number = "Suppl 3",

}

RIS

TY - JOUR

T1 - Interpretable Sentiment Analysis and Text Segmentation for Chinese Language

AU - Hou, Zhenghao

AU - Колонин, Антон Германович

PY - 2025/1/23

Y1 - 2025/1/23

N2 - Abstract: In this paper, we explored the performance of interpretable sentiment analysis models of different combinations for the Chinese text in social media. We made experiment to study how performance varies with the change of combination of different segmentation strategies and dictionary of words or n-grams. We found that with some good combination of segmentation strategies and dictionary of words or n-grams, the result can be improved and overtake the performance of ordinary sentiment analysis model of Chinese language. This way we show the importance of selection of segmentation strategies and dictionary for the sentiment analysis of Chinese text.

AB - Abstract: In this paper, we explored the performance of interpretable sentiment analysis models of different combinations for the Chinese text in social media. We made experiment to study how performance varies with the change of combination of different segmentation strategies and dictionary of words or n-grams. We found that with some good combination of segmentation strategies and dictionary of words or n-grams, the result can be improved and overtake the performance of ordinary sentiment analysis model of Chinese language. This way we show the importance of selection of segmentation strategies and dictionary for the sentiment analysis of Chinese text.

KW - Chinese text

KW - interpretable artificial intelligence

KW - nature language processing

KW - sentiment analysis

KW - social media

UR - https://www.mendeley.com/catalogue/c7ed44a5-e415-31c3-b508-032add9e9efc/

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85217439106&origin=inward&txGid=a5c6a22019c7107c914687759aff5473

U2 - 10.3103/S1060992X24700759

DO - 10.3103/S1060992X24700759

M3 - Article

VL - 33

SP - S483-S489

JO - Optical Memory and Neural Networks (Information Optics)

JF - Optical Memory and Neural Networks (Information Optics)

SN - 1934-7898

IS - Suppl 3

ER -

ID: 64717185