2,803 research outputs found
Killing me Softly: Creative and Cognitive Aspects of Implicitness in Abusive Language Online
[EN] Abusive language is becoming a problematic issue for our society. The spread of messages that reinforce social and cultural intolerance could have dangerous effects in victims¿ life. State-of-the-art technologies are often effective on detecting explicit forms of abuse, leaving unidentified the utterances with very weak offensive language but a strong hurtful effect. Scholars have advanced theoretical and qualitative observations on specific indirect forms of abusive language that make it hard to be recognized automatically. In
this work, we propose a battery of statistical and computational analyses able to support these considerations, with a focus on creative and cognitive aspects of the implicitness, in texts coming from different sources such as social media and news. We experiment with transformers, multi-task learning technique, and a set of linguistic features to reveal the elements involved in the implicit and explicit manifestations of abuses, providing a solid basis for computational applications.Frenda, S.; Patti, V.; Rosso, P. (2022). Killing me Softly: Creative and Cognitive Aspects of Implicitness in Abusive Language Online. Natural Language Engineering. 1-22. https://doi.org/10.1017/S135132492200031612
Computational Sarcasm Analysis on Social Media: A Systematic Review
Sarcasm can be defined as saying or writing the opposite of what one truly
wants to express, usually to insult, irritate, or amuse someone. Because of the
obscure nature of sarcasm in textual data, detecting it is difficult and of
great interest to the sentiment analysis research community. Though the
research in sarcasm detection spans more than a decade, some significant
advancements have been made recently, including employing unsupervised
pre-trained transformers in multimodal environments and integrating context to
identify sarcasm. In this study, we aim to provide a brief overview of recent
advancements and trends in computational sarcasm research for the English
language. We describe relevant datasets, methodologies, trends, issues,
challenges, and tasks relating to sarcasm that are beyond detection. Our study
provides well-summarized tables of sarcasm datasets, sarcastic features and
their extraction methods, and performance analysis of various approaches which
can help researchers in related domains understand current state-of-the-art
practices in sarcasm detection.Comment: 50 pages, 3 tables, Submitted to 'Data Mining and Knowledge
Discovery' for possible publicatio
Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN
Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets
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