Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention
| Autor: | Saroj Kaushik, Ritvik Shrivastava, Kuntal Dey |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Subjectivity
0209 industrial biotechnology Artificial neural network business.industry Computer science Deep learning 02 engineering and technology computer.software_genre Phase (combat) SemEval Task (project management) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Macro business computer Natural language processing Stance detection |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319769400 ECIR |
| DOI: | 10.1007/978-3-319-76941-7_40 |
| Popis: | The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in favor of (positive), is against (negative), or is none (neutral) towards the given topic. Using the concept of attention, we develop a two-phase solution. In the first phase, we classify subjectivity - whether a given tweet is neutral or subjective with respect to the given topic. In the second phase, we classify sentiment of the subjective tweets (ignoring the neutral tweets) - whether a given subjective tweet has a favor or against stance towards the topic. We propose a Long Short-Term memory (LSTM) based deep neural network for each phase, and embed attention at each of the phases. On the SemEval 2016 stance detection Twitter task dataset [7], we obtain a best-case macro F-score of 68.84% and a best-case accuracy of 60.2%, outperforming the existing deep learning based solutions. Our framework, T-PAN, is the first in the topical stance detection literature, that uses deep learning within a two-phase architecture. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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