Department of Psychology, Aligarh Muslim University, Aligarh 202001, India.
IT Department, Maharaja Surajmal Institute of Technology, New Delhi 110058, India.
Comput Intell Neurosci. 2022 Apr 10;2022:8153791. doi: 10.1155/2022/8153791. eCollection 2022.
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.
推特在过去几年中迅速走红，成为使用最广泛的社交媒体网站之一。由于这一发展，本研究中描述的策略现在更加有益。此外，越来越多的人以贬低他人的方式表达自己的观点。因此，仇恨言论引起了人们对情感分析主题的兴趣，情感分析已经开发出了各种使用直观方法在社交网络中检测情感的算法。本文提出了深度学习模型，以在两个单独的分析中对情绪进行分类。在第一次分析中，根据仇恨言论针对移民和女性的情况对推文进行分类。在第二次分析中，使用深度学习模型来组织仇恨言论是由单个用户还是一组用户发布的。在文本分析中，使用 BiLSTM、CNN 和 MLP 等深度学习模型的组合来实现词嵌入。这些模型与词嵌入方法（如逆手套（全局向量）、文档频率（TF-IDF）和基于转换器的嵌入）相结合。
