Proceedings ArticleDOI
Semantic Textual Similarity of Sentences with Emojis
Alok Debnath,Nikhil Pinnaparaju,Manish Shrivastava,Vasudeva Varma,Isabelle Augenstein +4 more
- pp 426-430
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TLDR
The amount of semantic information lost by discounting emojis is qualitatively ascertained, as well as a mechanism of accounting for emojiis in a semantic task is shown.Abstract:
In this paper, we extend the task of semantic textual similarity to include sentences which contain emojis. Emojis are ubiquitous on social media today, but are often removed in the pre-processing stage of curating datasets for NLP tasks. In this paper, we qualitatively ascertain the amount of semantic information lost by discounting emojis, as well as show a mechanism of accounting for emojis in a semantic task. We create a sentence similarity dataset of 4000 pairs of tweets with emojis, which have been annotated for relatedness. The corpus contains tweets curated based on common topic as well as by replacement of emojis. The latter was done to analyze the difference in semantics associated with different emojis. We aim to provide an understanding of the information lost by removing emojis by providing a qualitative analysis of the dataset. We also aim to present a method of using both emojis and words for downstream NLP tasks beyond sentiment analysis.read more
Citations
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Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets
TL;DR: Deep long short-term memory models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve state-of-the-art accuracy on the sentiment140 dataset and the use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter.
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Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning - a Case Study on COVID-19.
TL;DR: This study tends to detect and analyze sentiment polarity and emotions demonstrated during the initial phase of the pandemic and the lockdown period employing natural language processing (NLP) and deep learning techniques on Twitter posts.
Book ChapterDOI
Social Media Sentiment Analysis Related to COVID-19 Vaccines: Case Studies in English and Greek Language
TL;DR: In this article , a supervised learning approach was applied to monitor the dynamics of public opinion on COVID-19 vaccines using Twitter data, which revealed that overall negative, neutral, and positive sentiments were at 36.5, 39.9, and 23.6% in the English language dataset, respectively, whereas overall negative and nonnegative sentiments were on 60.1% and 39.1%, respectively, in the Greek language dataset.
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Towards Explainable Fact Checking
TL;DR: In this article, the authors propose a model for explainable fact checking using natural language processing methods, which in turn utilize deep neural networks to learn higher-order features from text in order to make predictions.
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