Improved Feature Weight Algorithm and Its Application to Text Classification

Shang, Songtao; Shi, Minjia; Shang, Wenqian; Hong, Zhiguo

doi:10.1155/2016/7819626

Cited by 12 publications

(4 citation statements)

References 14 publications

(11 reference statements)

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“…Controlled experiments were by application of supervised machine learning algorithms. The root of support vector machines is based on a statistical learning theory (Shang et al, 2016). It is a supervised classifier, which separates feature space into two classes based on their features (Lantz, 2013).…”

Section: Methodsmentioning

confidence: 99%

Guidelines for Detecting Cyberbullying in Social Media Data Through Text Analysis

Mkwananzi,

Smuts

2023

International Journal of Social Media and Online Communities

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The intensive use of the internet comes with negative and positive effects. Cyberbullying is one of the negative effects of using the internet. Cyberbullying has a negative effect on the victims emotionally, academically, and psychologically. Cyberbullying detection tools can help in reducing or eliminating cyberbullying on social media platforms. The aim of the study was to identify the elements that drive cyberbullying and build classification models to determine whether social media textual information contains cyberbullying text or not. The research aim was achieved through a mixed methods research design, containing qualitative and quantitative elements. The drivers of cyberbullying were identified through a literature review. These included age, gender, family structure, parental education, race, technology, anonymity, academic achievement, and awareness of cyber safety. The support vector machines and naïve Bayes models were used to classify the text dataset (Formspring.me dataset), with a 72.81% and a 99.87% classification accuracy, respectively.

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Section: Methodsmentioning

confidence: 99%

Guidelines for Detecting Cyberbullying in Social Media Data Through Text Analysis

Mkwananzi,

Smuts

2023

International Journal of Social Media and Online Communities

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“…Data classification is the supervised learning task which is used to group the given data based on the knowledge acquired from training of the data. But the classification task suffers by not reaching the accurate classification of the data items [6][7][8][9]. So it is important to find the ways to improve the accuracy and other parameters related to the classification task like kappa, precision and recall.…”

Section: Intensification Of Data Classificationmentioning

confidence: 99%

Application of Feature Weighting for the Intensification of Data Classification

2019

IJITEE

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Classification is the supervised learning technique which is applied in many of the real time applications. In this study we have considered three classifiers which are widely used and then the intensification of the classifiers are considered. Among various methods to improve the performance of the classifiers, this research concentrate on the feature weighting techniques applied for the classifiers. This analysis is done based on the results obtained from the Rapidminer tool. Here we have deployed four feature weighting techniques for the intensification of the three classifiers. It is tested with three dataset. The experimental environment and the results are discussed in detail.

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“…The result of classification is often used as the input for other tasks; thus an efficient and accurate classification algorithm is of great benefit. Traditional classification models combined with traditional representation of the text such as support vector machine base on the bag-of-word vectors or other classical methods have been able to achieve good results in some simple application scenarios [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Deep Generative Model for Text Classification and Generation

Wang

2018

Mathematical Problems in Engineering

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Text classification and generation are two important tasks in the field of natural language processing. In this paper, we deal with both tasks via Variational Autoencoder, which is a powerful deep generative model. The self-attention mechanism is introduced to the encoder. The modified encoder extracts the global feature of the input text to produce the hidden code, and we train a neural network classifier based on the hidden code to perform the classification. On the other hand, the label of the text is fed into the decoder explicitly to enhance the categorization information, which could help with text generation. The experiments have shown that our model could achieve competitive classification results and the generated text is realistic. Thus the proposed integrated deep generative model could be an alternative for both tasks.

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Improved Feature Weight Algorithm and Its Application to Text Classification

Cited by 12 publications

References 14 publications

Guidelines for Detecting Cyberbullying in Social Media Data Through Text Analysis

Guidelines for Detecting Cyberbullying in Social Media Data Through Text Analysis

Application of Feature Weighting for the Intensification of Data Classification

An Integrated Deep Generative Model for Text Classification and Generation

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