Morality Classification in Natural Language Text

Pavan, Matheus Camasmie; Santos, Vitor Garcia dos; Lan, Alex Gwo Jen; Martins, J. A. C.; Santos, Wesley Ramos dos; Deutsch, Caio; Costa, Pedro Jorge Cavalcante; Hsieh, Fernando; Paraboni, Ivandré

doi:10.1109/taffc.2020.3034050

Cited by 16 publications

(11 citation statements)

References 21 publications

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“…Each of these labels supports a ternary classification problems (from basic to superior education, from left to right political orientation and from no religious at all to highly religious.) For details regarding the BRmoral corpus and its annotation scheme, we refer to Pavan et al (2020).…”

Section: Datamentioning

confidence: 99%

Authorship attribution using author profiling classifiers

Deutsch

Paraboni

2022

Nat. Lang. Eng.

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Authorship attribution – the computational task of identifying the author of a given text document within a set of possible candidates – has been attracting interest in Natural Language Processing research for many years. At the same time, significant advances have also been observed in the related field of author profiling, that is, the computational task of learning author demographics from text such as gender, age and others. The close relation between the two topics – both of which focused on gaining knowledge about the individual who wrote a piece of text – suggests that research in these fields may benefit from each other. To illustrate this, this work addresses the issue of author identification with the aid of author profiling methods, adding demographics predictions to an authorship attribution architecture that may be particularly suitable to extensions of this kind, namely, a stack of classifiers devoted to different aspects of the input text (words, characters and text distortion patterns.) The enriched model is evaluated across a range of text domains, languages and author profiling estimators, and its results are shown to compare favourably to those obtained by a standard authorship attribution method that does not have access to author demographics predictions.

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Section: Datamentioning

confidence: 99%

Authorship attribution using author profiling classifiers

Deutsch

Paraboni

2022

Nat. Lang. Eng.

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“…Sentiment analysis has widespread commercial applications and practical values in various domains, such as decision analysis [16] and topic detection [17]. There are two main models to analyze the sentiment of a text: lexicon-based method [2] and deep learning-based method [9,18,19]. A labelled corpus with moral foundation scores was introduced to illustrate how morality-related information be inferred from text by shallow and deep learning models [2].…”

Section: Multimodal Sentiment Analysismentioning

confidence: 99%

“…There are two main models to analyze the sentiment of a text: lexicon-based method [2] and deep learning-based method [9,18,19]. A labelled corpus with moral foundation scores was introduced to illustrate how morality-related information be inferred from text by shallow and deep learning models [2]. Residual memory networks can be used to comprehend multimodal human sentiment [20].…”

Section: Multimodal Sentiment Analysismentioning

confidence: 99%

“…Three college students manually annotated 3200 texts as a training set. NLPCC2013 2,3,4 are the public corpus of the Chinese Weibo sentiment analysis competition with an average length of sentences of 38.33 Chinese characters, in which each document is combined with a Weibo topic and its comments. In NLPCC2012, 5 we treat one Weibo topic as a document.…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

“…People express their emotions in many ways, such as text [1,2], voice [3], intonation [4], facial expressions [5,6] and the multimodal combination [7]. With the popularity of online media, emojis have become one of the main ways people express their emotions intuitively.…”

Section: Introductionmentioning

confidence: 99%

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A multi-modal and multi-scale emotion-enhanced inference model based on fuzzy recognition

Qiu

Yan

2021

Complex Intell. Syst.

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Only the label corresponding to the maximum value of the fully connected layer is used as the output category when a neural network performs classification tasks. When the maximum value of the fully connected layer is close to the sub-maximum value, the classification obtained by considering only the maximum value and ignoring the sub-maximum value is not completely accurate. To reduce the noise and improve classification accuracy, combining the principles of fuzzy reasoning, this paper integrates all the output results of the fully connected layer with the emotional tendency of the text based on the dictionary to establish a multi-modal fuzzy recognition emotion enhancement model. The provided model considers the enhancement effect of negative words, degree adverbs, exclamation marks, and question marks based on the smallest subtree on the emotion of emotional words, and defines the global emotional membership function of emojis based on the corpus. Through comparing the results of CNN, LSTM, BiLSTM and GRU on Weibo and Douyin, it is shown that the provided model can effectively improve the text emotion recognition when the neural network output result is not clear, especially for long texts.

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