Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

Abstract

PurposeHate speech is an expression of intense hatred. Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors. Hate speech detection with social media data has witnessed special research attention in recent studies, hence, the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech detection.Design/methodology/approachThis study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter data. The proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency (TF-IDF) for word-level feature extraction and Long Short Term Memory (LSTM) which is a variant of recurrent neural networks architecture for sentence-level feature extraction. The extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech, offensive language or neither.FindingsThe proposed method showed better results when tested on the collected Twitter datasets compared to other related methods. In order to validate the performances of the proposed method, t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech detection. Furthermore, Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.Research limitations/implicationsFinally, the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.Originality/valueThe main novelty of this study is the use of an automatic topic spotting measure based on naïve Bayes model to improve features representation.

Probabilistic Hesitant Fuzzy Methods for Prioritizing Distributed Stream Processing Frameworks for IoT Applications

“…Even worse, data are produced continuously at a high speed [5]. e distributed stream processing frameworks (DSPFs) [6] are the practicable technique solution, which can be used to fulfil such large-scale data processing and analytics for IoT applications in realtime [7,8]. e DSPFs have become a vital component of each IoT solution stack [9].…”

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

“…The two modes of data were analyzed in combination and separately with machine learning and deep learning algorithms to find sentiments from Twitter-based airline data using several features such as TF-IDF, N-gram and emoticon lexicons. On the other hand, Ayo et al [46] adopted an approach that proposes an improved hybrid integration with a topic inference method and an improved neural network for hate speech detection in Twitter data. The www.ijacsa.thesai.org proposed method uses a hybrid nesting technique that includes TF-IDF for word-level feature extraction and LSTM longterm memory for sentence-level feature extraction.…”

Idea plagiarism detection with recurrent neural networks and vector space model

“…But with the addition of neurons in these hidden layers, the number of parameters that need to be learned grows exponentially, making the training process power and memory hungry. This hampered research in this field for a very long time, referred to as the winter of artificial intelligence, but from the past few years introduction of cost-effective GPUs and established Cloud vendors providing almost infinite resources to the research community has accelerated research in this field (Ayo et al, 2020;Naili et al, 2016). Convolutional neural networks, RNNs, Autoencoders have revolutionized the field with enormous applications in image and text processing.…”