Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nevertheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, which may be useful for future applications. We also discuss the results of multiple analyses run on the corpus. Firstly, we find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment. Secondly, we observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not in a subset of our corpus text examples. We attribute the disagreement to a variety of potential reasons, including if the PET was a commonly accepted term (CAT).

The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions. Combating the outcomes of this infodemic is not only a question of identifying false claims, but also reasoning about the decisions individuals make. In this work we propose a holistic analysis framework connecting stance and reason analysis, and fine-grained entity level moral sentiment analysis. We study how to model the dependencies between the different level of analysis and incorporate human insights into the learning process. Experiments show that our framework provides reliable predictions even in the low-supervision settings.

Transformers are one of the most exciting concepts in Natural Language Processing. This article is a guide on working with pre-trained models that use transformers. A transformer model is a neural…

Aspect-level sentiment analysis aims to determine the sentiment polarity towards a specific target in a sentence. The main challenge of this task is to effectively model the relation between targets and sentiments so as to filter out noisy opinion words from irrelevant targets. Most recent efforts capture relations through target-sentiment pairs or opinion spans from a word-level or phrase-level perspective. Based on the observation that targets and sentiments essentially establish relations following the grammatical hierarchy of phrase-clause-sentence structure, it is hopeful to exploit comprehensive syntactic information for better guiding the learning process. Therefore, we introduce the concept of Scope, which outlines a structural text region related to a specific target. To jointly learn structural Scope and predict the sentiment polarity, we propose a hybrid graph convolutional network (HGCN) to synthesize information from constituency tree and dependency tree, exploring the potential of linking two syntax parsing methods to enrich the representation. Experimental results on four public datasets illustrate that our HGCN model outperforms current state-of-the-art baselines.

By now, most of Twitter's 217 million daily active users have probably heard the news: Elon Musk -- the world's richest person, CEO of Tesla and SpaceX and a prolific Internet poster -- has reached an agreement to buy the social network for about $44 billion.

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Researchers from the UPV/EHU-University of the Basque Country, the UNED (National Distance Education University) and Elhuyar have created the VIGICOVID system, thanks to Supera COVID-19 (Overcoming COVID-19) funding by the CRUE (Association of Spanish Universities). This system addresses the need to search for answers in the avalanche of information generated by all the research conducted across the world relating to the pandemic. By means of artificial intelligence, the system displays the answers found in a set of scientific articles in an orderly fashion, and uses natural language questions and answers. The global bio-health research community is making a tremendous effort to generate knowledge relating to COVID-19 and SARS-CoV-2. In practice, this effort means a huge, very rapid production of scientific publications, which makes it difficult to consult and analyze all the information. That is why experts and decision-making bodies need to be provided with information systems to enable them to acquire the knowledge they need.

Big data enables businesses to thrive and grow by finding hidden patterns in data. Brands are now getting smarter by taking actions based on customer sentiments. Not only brands but also political parties and governments are looking at social sentiments as a valuable resource for growth. With big data, real-time customer sentiment analysis has become possible. Social media has completely changed how people express themselves.

Communication software platform maker Arena – a provider of a Slack-like chat or bot conversation column to the right side of your screen when you're on an ecommerce site – is endeavoring to bring more human understanding to online marketing and sales. That, in turn, works to establish better rapport with potential customers for ecommerce businesses. The San Francisco-based startup's group chat and messaging application framework for B2C enterprises, having earned the attention of investors, yesterday announced a $13.6 million Series A round led by CRV with Craft Ventures, Artisanal Ventures and Vela Partners also participating. A key marketing trend in 2022 is for consumer companies to find ways to move beyond social media and third-party cookies as a way of gaining better direct insights into their users and customers. Five-year-old Arena recognized this early and built a SaaS platform to replace the need for third-party referrals and social networks, CEO and founder Paolo Martins told VentureBeat.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Communication software platform maker Arena – a provider of a Slack-like chat or bot conversation column to the right side of your screen when you're on an ecommerce site – is endeavoring to bring more human understanding to online marketing and sales. That, in turn, works to establish better rapport with potential customers for ecommerce businesses. The San Francisco-based startup's group chat and messaging application framework for B2C enterprises, having earned the attention of investors, yesterday announced a $13.6 million Series A round led by CRV with Craft Ventures, Artisanal Ventures and Vela Partners also participating. A key marketing trend in 2022 is for consumer companies to find ways to move beyond social media and third-party cookies as a way of gaining better direct insights into their users and customers.