Abstract
Humans like to express their opinions and crave the opinions of others. Mining and detecting opinions from various sources are beneficial to individuals, organisations, and even governments. One such organisation is news media, where a general norm is not to showcase opinions from their side. Anchors are the face of the digital media, and it is required for them not to be opinionated. However, at times, they diverge from the accepted norm and insert their opinions into otherwise straightforward news reports, either purposefully or unintentionally. This is primarily seen in debates as it requires the anchors to be spontaneous, thus making them vulnerable to add their opinions. The consequence of such mishappening might lead to biased news or even supporting a certain agenda at the worst. To this end, we propose a novel task of anchors’ opinion detection in debates. We curate code-mixed news debates and develop the ODIN dataset. A total of 2054 anchors’ utterances in the dataset are marked as opinionated or non-opinionated. Lastly, we propose DetONADe – an interactive attention-based framework for classifying anchors’ utterances and obtain the best weighted-F1 score of 0.703. A thorough analysis and evaluation show many interesting patterns in the dataset and predictions.
S. Sadhwani and N. Grover: Equal Contribution
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Notes
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A personal sentiment, which describes the anchor’s feeling on the topic [11].
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Henceforth, we will use debate, dialogue, and conversation interchangeably to signify a sequence of utterances.
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BJP and Congress are two major political parties in India.
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Acknowledgement
The authors would like to acknowledge the support of the Ramanujan Fellowship (SERB, India), Infosys Centre for AI (CAI) at IIIT-Delhi, and ihub-Anubhuti-iiitd Foundation set up under the NM-ICPS scheme of the Department of Science and Technology, India.
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Sadhwani, S., Grover, N., Akhtar, M.S., Chakraborty, T. (2022). Detecting Anchors’ Opinion in Hinglish News Delivery. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_45
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