In our study, we conduct a comparative analysis between dictionary-based sentiment analysis and entailment zero-shot text classification for German sentiment analysis. We evaluate the performance of a selection of dictionaries on eleven data sets, including four domain-specific data sets with a focus on historic German language. Our results demonstrate that, in the majority of cases, zero-shot text classification outperforms general-purpose dictionary-based approaches but falls short of the performance achieved by specifically fine-tuned models. Notably, the zero-shot approach exhibits superior performance, particularly in historic German cases, surpassing both general-purpose dictionaries and even a broadly trained sentiment model. These findings indicate that zero-shot text classification holds significant promise as an alternative, reducing the necessity for domain-specific sentiment dictionaries and narrowing the availability gap of off-the-shelf methods for German sentiment analysis. Additionally, we thoroughly discuss the inherent trade-offs associated with the application of these approaches.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:92503 |
| Date | 04 July 2024 |
| Creators | Borst, Janos, Burghardt, Manuel, Klähn, Jannis |
| Publisher | CEUR-WS.org |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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