scholarly journals Hate Speech Detection using Modified Principal Component Analysis and Enhanced Convolution Neural Network on Twitter Dataset

2021 ◽  
Author(s):  
Majed Alowaidi
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Social Networking ◽  
Hate Speech ◽  
Principal Component ◽  
Extraction Process ◽  
Online Social Media ◽  
The Given

Abstract Online social media are increasingly catching people’s eye among users of the Internet. Services provided by social networking vendors like Twitter and Facebook are very attractive, with widespread proliferation among internet users. As a downside of their predominance in the domain of social networking, Twitter and Facebook are frequently pestered with the problem of handling offensive, threat, fake, hate words. One of the major problems, apparent in online social media, is the toxic online content. In the existing system, the methods are not dealt with large dataset. Also the feature extraction method is not efficient to extract important features in the given dataset. To overcome the above mentioned issues, in this work, Modified Principal Component Analysis (MPCA) and Enhanced Convolution Neural Network (ECNN) is proposed. Natural Language Processing (NLP) is implemented to build an automatic system through the inclusion of syntactic and semantic analysis. This work contains main phases are such as pre-processing, feature extraction and classification process. The pre-processing is done by using normalization method which is used to remove the white spaces, replace the consecutive exclamation and question marks, and eliminate stop words. These preprocessed features are taken into feature extraction process. MPCA algorithm is applied to perform feature extraction process. It uses set of correlated features and extracts more informative features for the given dataset. Then the classification algorithm is proposed to detect the hate speech or abusive languages. ECNN is proposed to classify hate and non-hate from the online content more accurately. It takes many inputs and generates output with minimum amount of time with higher accuracy for larger dataset. Thus, the result concludes that the proposed MPCA+ECNN algorithm provides higher accuracy, precision, recall and F-measure values rather than the existing methods.

scholarly journals Incremental Matrix-Based Subspace Method for Matrix-Based Feature Extraction

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhaoyang Zhang ◽  
Shijie Sun ◽  
Wei Wang
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Spatial Statistics ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Subspace Method ◽  
Training Set ◽  
Incremental Method ◽  
The Matrix ◽  
The Given

The matrix-based features can provide valid and interpretable information for matrix-based data such as image. Matrix-based kernel principal component analysis (MKPCA) is a way for extracting matrix-based features. The extracted matrix-based feature is useful to both dimension reduction and spatial statistics analysis for an image. In contrast, the efficiency of MKPCA is highly restricted by the dimension of the given matrix data and the size of the training set. In this paper, an incremental method to extract features of a matrix-based dataset is proposed. The method is methodologically consistent with MKPCA and can improve efficiency through incrementally selecting the proper projection matrix of the MKPCA by rotating the current subspace. The performance of the proposed method is evaluated by performing several experiments on both point and image datasets.

scholarly journals Neural Network and Adaptive Feature Extraction Technique for Pattern Recognition

2011 ◽  
Vol 8 (1) ◽  
pp. 01-06
Author(s):  
Wael M. Khedr ◽  
Qamar A. Awad
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Back Propagation ◽  
Feature Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Adaptive Feature Extraction

In this paper, we propose adaptive K-means algorithm upon the principal component analysis PCA feature extraction to pattern recognition by using a neural network model. Adaptive k-means to discriminate among objects belonging to different groups based upon the principal component analysis PCA implemented for statistical feature extraction. The features extracted by PCA consistently reduction dimensional algorithm, thus demonstrating that the suite of structure detectors effectively performs generalized feature extraction. The classification accuracies achieved using feature learning process of back propagation neural network . A comparison of the proposed adaptive and previous non-adaptive ensemble is the primary goal of the experiments. We evaluated the performance of the clustering ensemble algorithms by matching the detected and the known partitions of the iris dataset. The best possible matching of clusters provides a measure of performance expressed as the misassignment rate.

A Survey of Computational Methods for Online Mental State Assessment on Social Media

2021 ◽  
Vol 2 (2) ◽  
pp. 1-31
Author(s):  
Esteban A. Ríssola ◽  
David E. Losada ◽  
Fabio Crestani
Keyword(s):  
Social Media ◽  
Mental Disorders ◽  
Mental State ◽  
Extraction Process ◽  
State Assessment ◽  
Future Research ◽  
Linguistic Markers ◽  
Online Social Media ◽  
Suicide Behaviour ◽  
Social Media Platforms

Mental state assessment by analysing user-generated content is a field that has recently attracted considerable attention. Today, many people are increasingly utilising online social media platforms to share their feelings and moods. This provides a unique opportunity for researchers and health practitioners to proactively identify linguistic markers or patterns that correlate with mental disorders such as depression, schizophrenia or suicide behaviour. This survey describes and reviews the approaches that have been proposed for mental state assessment and identification of disorders using online digital records. The presented studies are organised according to the assessment technology and the feature extraction process conducted. We also present a series of studies which explore different aspects of the language and behaviour of individuals suffering from mental disorders, and discuss various aspects related to the development of experimental frameworks. Furthermore, ethical considerations regarding the treatment of individuals’ data are outlined. The main contributions of this survey are a comprehensive analysis of the proposed approaches for online mental state assessment on social media, a structured categorisation of the methods according to their design principles, lessons learnt over the years and a discussion on possible avenues for future research.

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