Artificial Neural Network for Opinion Target Identification in Czech
| Thesis title in Czech: | Umělá neuronová síť pro identifikaci cílů hodnocení v češtině |
|---|---|
| Thesis title in English: | Artificial Neural Network for Opinion Target Identification in Czech |
| Key words: | neuronová síť, identifikace cílů hodnocení, postojová analýza |
| English key words: | neural network, opinion target identification, sentiment analysis |
| Academic year of topic announcement: | 2015/2016 |
| Thesis type: | Bachelor's thesis |
| Thesis language: | angličtina |
| Department: | Institute of Formal and Applied Linguistics (32-UFAL) |
| Supervisor: | doc. RNDr. Vladislav Kuboň, Ph.D. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 14.06.2016 |
| Date of assignment: | 14.06.2016 |
| Confirmed by Study dept. on: | 23.06.2016 |
| Date and time of defence: | 08.09.2016 00:00 |
| Date of electronic submission: | 27.07.2016 |
| Date of submission of printed version: | 28.07.2016 |
| Date of proceeded defence: | 08.09.2016 |
| Opponents: | RNDr. Jiří Mírovský, Ph.D. |
| Advisors: | Mgr. Kateřina Lesch, Ph.D. |
| Guidelines |
| This thesis surveys a use of artificial neural networks in automatic opinion target identification, i.e. the task in which the evaluated entities need to be identified in natural language texts. Opinion target identification is in the long term one of the widely-discussed problems in the field of sentiment analysis. Just lately, it has been explored using different approaches from the area of deep learning.
The thesis makes use of the existing dataset for opinion target identification in Czech. The author compares the results with the results achieved by employing probabilistic models and, taking into account the relative language-independency of the given approach, with the state-of-the-art results achieved for other languages. The method can be further exploitable in other NLP tasks, such as e.g. automatic summarization. |
| References |
| dos Santos, C. N., & Gatti, M. (2014). Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. In COLING (pp. 69-78).
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2), 1-135. Tamchyna A., Fiala O. & Veselovská, K. (2015). Czech Aspect-Based Sentiment Analysis: A New Dataset and Preliminary Results. In: Proceedings of ITAT 2015 (pp. 95-99). Tamchyna, A. & Veselovská, K. (2016). Recurrent Neural Networks for Sentence Classification. In: Proceedings of SemEval 2016. |