  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.
