Analysing Hate Speech against Migrants and Women through Tweets Using Ensembled Deep Learning Model.
Comput Intell Neurosci
; 2022: 8153791, 2022.
Article
in English
| MEDLINE | ID: covidwho-1794354
ABSTRACT
Twitter's popularity has exploded in the previous few years, making it one of the most widely used social media sites. As a result of this development, the strategies described in this study are now more beneficial. Additionally, there has been an increase in the number of people who express their views in demeaning ways to others. As a result, hate speech has piqued interest in the subject of sentiment analysis, which has developed various algorithms for detecting emotions in social networks using intuitive means. This paper proposes the deep learning model to classify the sentiments in two separate analyses. In the first analysis, the tweets are classified based on the hate speech against the migrants and the women. In the second analysis, the detection is performed using a deep learning model to organise whether the hate speech is performed by a single or a group of users. During the text analysis, word embedding is implemented using the combination of deep learning models such as BiLSTM, CNN, and MLP. These models are integrated with word embedding methods such as inverse glove (global vector), document frequency (TF-IDF), and transformer-based embedding.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Social Media
/
Deep Learning
Limits:
Female
/
Humans
/
Male
Language:
English
Journal:
Comput Intell Neurosci
Journal subject:
Medical Informatics
/
Neurology
Year:
2022
Document Type:
Article
Affiliation country:
2022
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