Detecting misogyny in Spanish tweets. An approach based on linguistics features and word embeddings

Garcí­a-Dí­az, José Antonio; Cánovas-García, Mar; Colomo‐Palacios, Ricardo; Valencia‐García, Rafael

doi:10.1016/j.future.2020.08.032

Cited by 68 publications

(47 citation statements)

References 20 publications

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“…We will also evaluate the reliability of pretrained word embeddings like FastText, which, contrary to Word2Vec, also handles out-of-vocabulary words because word embeddings are learned from character n-grams instead of words. These Spanish word embeddings have been evaluated in other NLP tasks with promising results [ 41 , 42 , 43 ]. Moreover, the possibility to adapt non-Spanish pre-trained embeddings specifically built for domains close to the medical one such as BioBERT (‘Bidirectional Encoder Representations from Transformers for Biomedical Text Mining’, (accessed on 20 April 2021)) [ 44 ] will be analyzed.…”

Section: Discussionmentioning

confidence: 99%

Automatic Correction of Real-Word Errors in Spanish Clinical Texts

Bravo-Candel

López-Hernández

García-Díaz

et al. 2021

Sensors

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Real-word errors are characterized by being actual terms in the dictionary. By providing context, real-word errors are detected. Traditional methods to detect and correct such errors are mostly based on counting the frequency of short word sequences in a corpus. Then, the probability of a word being a real-word error is computed. On the other hand, state-of-the-art approaches make use of deep learning models to learn context by extracting semantic features from text. In this work, a deep learning model were implemented for correcting real-word errors in clinical text. Specifically, a Seq2seq Neural Machine Translation Model mapped erroneous sentences to correct them. For that, different types of error were generated in correct sentences by using rules. Different Seq2seq models were trained and evaluated on two corpora: the Wikicorpus and a collection of three clinical datasets. The medicine corpus was much smaller than the Wikicorpus due to privacy issues when dealing with patient information. Moreover, GloVe and Word2Vec pretrained word embeddings were used to study their performance. Despite the medicine corpus being much smaller than the Wikicorpus, Seq2seq models trained on the medicine corpus performed better than those models trained on the Wikicorpus. Nevertheless, a larger amount of clinical text is required to improve the results.

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Section: Discussionmentioning

confidence: 99%

Automatic Correction of Real-Word Errors in Spanish Clinical Texts

Bravo-Candel

López-Hernández

García-Díaz

et al. 2021

Sensors

Self Cite

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“…García-Díaz et al conducted an HSD study on misogyny against women on Spanish tweets [27]. They divided into 3 sub-dataset as violence against women, harassment against women, and misogyny against women.…”

Section: Related Studiesmentioning

confidence: 99%

Metaheuristic Ant Lion and Moth Flame Optimization-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks

Baydogan

Alataş

2021

IEEE Access

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In the online social networks, blogs, microblogs, social bookmarking services and sharing sites, and various web forum pages; the sharing of knowledge, opinions, ideas, etc. are spreading very quickly. This situation brings very dangerous problems in social networks. One of these problems is hate speech detection (HSD) problem which is covering issues such as insults, swearing, humiliation, discrimination, exclusion, detest, abhor, blast, damn, and intolerance. These can be reactions to a person, a group, an organization, an order, or an event. Although few machine learning methods have been used in the literature to solve this important problem in online social media, the performance of the HSD models in terms of many metrics needs to be increased. In this study, an automatic HSD system based on metaheuristic methodology was proposed for better results in this new and important problem. In the proposed optimization approach, Ant Lion Optimization (ALO) algorithm and Moth Flame Optimization (MFO) algorithm were designed for the HSD problem. This is the first attempt to use optimization algorithms as solution search strategies for automatic HSD. An efficient representation scheme and flexible fitness function were designed for this purpose. Many metrics can easily be embedded into the designed fitness function in order to be simultaneously optimized. Firstly, the basic natural language processing (NLP) steps were carried out. Feature extraction was performed using Bag of Words (BoW), Term Frequency (TF), and document vector (Word2Vec). Then, the performances of the proposed novel approaches were analyzed in detail on the three different real-world data. The obtained results were also checked against eight popular supervised machine learning algorithms, Social Spider Optimization (SSO) algorithm, and state-of-the-art Tunicate Swarm Algorithm (TSA). Considering the evaluation criteria for three sets of experiments, it was observed that the accuracy, sensitivity, precision, and f-score results of the ALO and MFO algorithms were superior to machine learning methods. As a result of the experimental studies, the highest accuracy value was 92.1% for ALO, while this value was 90.7% for MFO. Other numerical values obtained in the study were given in the experiments and results section with tables and graphics in detail. Due to the promising results of the proposed approaches, they are anticipated to be used in the solution of many social media and networking problems.INDEX TERMS Hate speech detection, metaheuristic optimization, natural language processing, social network analysis, text mining.

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“…Studies have described suggestion mining as sentence classification, which is based on predicting opinionated text into the binary forms of suggestions and non-suggestions [3][4][5]. The literature has generally defined suggestion mining as the "extraction of suggestions from the opinionated text, where suggestions keyword denotes the recommendation, advice, and tips" [3].…”

Section: Introductionmentioning

confidence: 99%

“…Such increased opinionated text has constituted the major dataset in the majority of recent research [9][10][11]. Some studies have focused on product reviews [4,5,12] related to tourism (e.g., hotel service) [10,11] and on social media data (e.g., Twitter) [13].…”

Section: Introductionmentioning

confidence: 99%

Suggestion Mining from Opinionated Text of Big Social Media Data

Alotaibi¹,

Malik²,

Khan³

et al. 2021

Computers, Materials &Amp; Continua

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Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers' reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews. F1, precision, recall, and accuracy scores are calculated. The results demonstrated that the XGBoost classifier outperforms-with an accuracy of more than 80%. Moreover, the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction. Thus, this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

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Detecting misogyny in Spanish tweets. An approach based on linguistics features and word embeddings

Cited by 68 publications

References 20 publications

Automatic Correction of Real-Word Errors in Spanish Clinical Texts

Automatic Correction of Real-Word Errors in Spanish Clinical Texts

Metaheuristic Ant Lion and Moth Flame Optimization-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks

Suggestion Mining from Opinionated Text of Big Social Media Data

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