New Language Identification and Sentiment Analysis Modules for Social Media Communication
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | International Conference on Text, Speech, and Dialogue |
| MU Faculty or unit | |
| Citation | |
| Web | https://link.springer.com/chapter/10.1007/978-3-031-16270-1_8 |
| Doi | http://dx.doi.org/10.1007/978-3-031-16270-1_8 |
| Keywords | social media communication; language identification; sentiment analysis |
| Description | 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. |
| Related projects: |