2 citations · 3 across the 3 of their papers we have counts for
6 papers
The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition
Svetlana Kiritchenko, Saif M. Mohammad
Negators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work h…
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Svetlana Kiritchenko, Saif M. Mohammad
Rating scales are a widely used method for data annotation; however, they present several challenges, such as difficulty in maintaining inter- and intra-annotator consistency. Best…
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling
Svetlana Kiritchenko, Saif M. Mohammad
Access to word-sentiment associations is useful for many applications, including sentiment analysis, stance detection, and linguistic analysis. However, manually assigning fine-gra…
WASSA-2017 Shared Task on Emotion Intensity
Saif M. Mohammad, Felipe Bravo-Marquez
We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sa…
Emotion Intensities in Tweets
Saif M. Mohammad, Felipe Bravo-Marquez
This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a t…
Stance and Sentiment in Tweets
Saif M. Mohammad, Parinaz Sobhani, Svetlana Kiritchenko
We can often detect from a person's utterances whether he/she is in favor of or against a given target entity -- their stance towards the target. However, a person may express the…