Abstract
The style and vocabulary of social media communication, such as chats, discussions or comments, differ vastly from standard languages. Specifically in internal business communication, the texts contain large amounts of language mixins, professional jargon and occupational slang, or colloquial expressions. Standard natural language processing tools thus mostly fail to detect basic text processing attributes such as the prevalent language of a message or communication or their sentiment.
In the presented paper, we describe the development and evaluation of new modules specifically designed for language identification and sentiment analysis of informal business communication inside a large international company. Besides the details of the module architectures, we offer a detailed comparison with other state-of-the-art tools for the same purpose and achieve an improvement of 10–13 % in accuracy with selected problematic datasets.
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Notes
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The language used in social media communications.
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cs, csd, da, de, en, es, fi, fr, hu, it, jp, nl, no, pl, ru, se, sk, skd, sw, and zh.
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Acknowledgments
This work has been partly supported by the Ministry of Education of CR within the LINDAT-CLARIAH-CZ project LM2018101. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
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Sabol, R., Horák, A. (2022). New Language Identification and Sentiment Analysis Modules for Social Media Communication. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2022. Lecture Notes in Computer Science(), vol 13502. Springer, Cham. https://doi.org/10.1007/978-3-031-16270-1_8
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