Sentiment Analysis of Persian-English Code-mixed Texts
| Autor: | Behnam Bahrak, Nazanin Sabri, Ali Edalat |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language business.industry Computer science Sentiment analysis Perspective (graphical) InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL computer.software_genre Code (semiotics) Random forest Naive Bayes classifier The Internet Social media Artificial intelligence business Baseline (configuration management) computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | CSICC |
| DOI: | 10.48550/arxiv.2102.12700 |
| Popis: | The rapid production of data on the internet and the need to understand how users are feeling from a business and research perspective has prompted the creation of numerous automatic monolingual sentiment detection systems. More recently however, due to the unstructured nature of data on social media, we are observing more instances of multilingual and code-mixed texts. This development in content type has created a new demand for code-mixed sentiment analysis systems. In this study we collect, label and thus create a dataset of Persian-English code-mixed tweets. We then proceed to introduce a model which uses BERT pretrained embeddings as well as translation models to automatically learn the polarity scores of these Tweets. Our model outperforms the baseline models that use Na\"ive Bayes and Random Forest methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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