Natural Language Processing techniques have gained popularity for the analysis of social media content. A number of techniques have been proposed to analyze various aspects such as public opinion, sentiments, and emotions expressed, opinion leaders, and extreme views. However, existing approaches take a retrospective approach that studies opinions after the occurrence of events. With the buildup of negative sentiments and extreme public opinion potentially leading to violent actions and civil disobedience, there is a need for a proactive and predictive approach that can offer early warning signs to government officials to intervene. In this work, we propose such an approach by combining two natural language processing techniques: Named entity recognition (NER) and emotions analysis. By tagging important entities within posts, such as prominent figures and important locations, and analyzing whether the tweets mentioning important entities carry negative emotions such as anger or violenc
Researchers in Canada, India, China and Australia have collaborated to produce a freely-available Python package that can effectively be used to spot and replace 'unfair language' in news copy. The system, titled Dbias, uses various machine learning technologies and databases to develop a three-s
Copenhagen, May 31, 2022 (GLOBE NEWSWIRE) Announcement no. 8-2022 Inside Information The SaaS company Hypefactors, which delivers a unified solution for media intelligence, reputation and trust
Named entity recognition (NER) is a widely used natural language processing technique; it plays a key role in information extraction from sentences. To be able to test the correctness of NER systems is important, but it is expensive because an automated test oracle is normally unavailable. To address the oracle problem, this study proposes to apply metamorphic testing (MT). The authors conduct a case study with Litigant, an industrial NER system of the Ant Group, and show that MT can effectively detect real-life bugs in the absence of an ideal oracle. The authors further investigate the causes for a series of entity recognition failures detected. Outcomes of this research further justify the application of MT to the natural language processing domain as well as provide hints for practitioners to improve the quality process of their NER systems.