SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features
Gangeshwar Krishnamurthy | Raj Kumar Gupta | Yinping Yang
Proceedings of the Fourteenth Workshop on Semantic Evaluation
This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the “loaded language” and “slogan” techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, “flag waving” and “appeal to fear-prejudice” have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting “loaded language” (F1 = 0.772), “name calling and labeling” (F1 = 0.673), “doubt” (F1 = 0.604) and “flag waving” (F1 = 0.543).
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis
Devamanyu Hazarika | Soujanya Poria | Prateek Vij | Gangeshwar Krishnamurthy | Erik Cambria | Roger Zimmermann
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Aspect-based Sentiment Analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. Present neural-based models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.
- Raj Kumar Gupta 1
- Yinping Yang 1
- Devamanyu Hazarika 1
- Soujanya Poria 1
- Prateek Vij 1
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