Efficient n-gram construction for text categorization using feature selection techniques

García, Maximiliano; Maldonado, Sebastián; Vairetti, Carla

doi:10.3233/ida-205154

Cited by 15 publications

(5 citation statements)

References 43 publications

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“…Text classification methods mainly include word matching method, knowledge engineering, and statistical learning [16], of which word matching method is the earliest proposed method. e classification processing principle of word matching method is to classify according to the class name in the document.…”

Section: Feature Extraction Methods and Text Classificationmentioning

confidence: 99%

Numerical Analysis and Optimization of Feature Extraction-Oriented English Reading Corpus

2022

Mathematical Problems in Engineering

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English is a universal language in the world. It has become the consensus of society as a subject of education in primary and secondary schools and even universities. Therefore, how to improve English reading ability has also become a focus area of school education and students. The current research on English reading is mainly based on the sense of reading questions, reading patterns, answering skills, etc. and lacks the analysis of English reading corpus. In view of this, this paper used a self-built English reading corpus, adopts the feature extraction method, and combines the convolutional neural network (CNN) to build a model to carry out numerical analysis on the self-built English reading corpus, optimized the model, and compared and analyzed the results obtained. The optimal dropout rate and iteration times were obtained by updating experimental parameters. In order to make the experimental results more convincing, the W2V-SVM and W2V-CNN models that combine different feature extraction and classification methods are designed. Compared with the optimized CNN model, the accuracy rate, recall rate, and F1 value of the optimized CNN model were 89.81%, 92.39%, and 92.8%, respectively. The accuracy, recall, and F1 value of the W2V-SVM model are 81.31%, 82.09%, and 81.25%, respectively. The accuracy, recall, and F1 value of the W2V-CNN model are 85.24%, 84.98%, and 85.12%, respectively. It shows that the optimized CNN feature classification model has better feature classification effect on the self-built English reading corpus. The experimental results meet the expected value.

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Section: Feature Extraction Methods and Text Classificationmentioning

confidence: 99%

Numerical Analysis and Optimization of Feature Extraction-Oriented English Reading Corpus

2022

Mathematical Problems in Engineering

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“…Text is an unstructured or semistructured form of data organization, which means that computers are unable to directly process it [14]. Text consists of a collection of characters that have been arranged in a certain order.…”

Section: Text Representationmentioning

confidence: 99%

A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network

Yang

Zhou

Jing

2022

Computational Intelligence and Neuroscience

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The severity of mental health issues among college students has increased over the past few years, having a significant negative impact on not only their academic performance but also on their families and even society as a whole. Therefore, one of the pressing issues facing college administrators right now is finding a method that is both scientific and useful for determining the mental health of college students. In pace with the advancement of Internet technology, the Internet has become an important communication channel for contemporary college students. As one of the main forces in the huge Internet population, college students are at the stage of growing knowledge and being most enthusiastic about new things, and they like to express their opinions and views on study life and social issues and are brave to express their emotions. These subjective text data often contain some affective tendencies and psychological characteristics of college students, and it is beneficial to dig out their affective tendencies to further understand what they think and expect and to grasp their mental health as early as possible. In order to address the issue of assessing the mental health of college students, this study makes an effort to use public opinion data from the university network and suggests a college student sentiment analysis model based on the OCC affective cognitive model and Bi-LSTM neural network. In order to label three different types of positive, negative, and neutral sentiment on the microblog text of college network public opinion, we first design a sentiment rule system based on the OCC affective cognition elicitation mechanism. In order to effectively and automatically identify the sentiment state of college students in the network public opinion, this study uses a Bi-LSTM neural network to classify the preprocessed college network public opinion data. Finally, this study performs comparison experiments to confirm the validity of the Bi-LSTM neural network sentiment recognition algorithm and the accuracy of the OCC sentiment rule labeling system. The findings show that the college student sentiment recognition effect of the model is significantly enhanced when the OCC sentiment rule system is used to label the college network public opinion data set as opposed to the naturally labeled data set. In contrast to SVM and other classification models like CNN and LSTM, the Bi-LSTM neural network-based classification model achieves more satisfactory classification results in the recognition of college opinion sentiment.

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“…In [16], the authors proposed a method to detect spam comments on YouTube by using different machine learning algorithms with the n-gram approach, and they proved that this technique is effective in detecting spam comments. García et al [17] introduced a system for text classification that executes embedded feature elimination via an a priori algorithm. The aim of their study was to speed up the word sequence constructions by minimizing the explored branches' number as much as possible.…”

Section: Stance Detectionmentioning

confidence: 99%

Ternion: An Autonomous Model for Fake News Detection

et al. 2021

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In recent years, the consumption of social media content to keep up with global news and to verify its authenticity has become a considerable challenge. Social media enables us to easily access news anywhere, anytime, but it also gives rise to the spread of fake news, thereby delivering false information. This also has a negative impact on society. Therefore, it is necessary to determine whether or not news spreading over social media is real. This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development. This paper proposes a novel solution by detecting the authenticity of news through natural language processing techniques. Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity of news. In the last stage of the proposed pipeline, several machine learning techniques are applied, such as decision trees, random forest, logistic regression, and support vector machine (SVM) algorithms. For this study, the fake news dataset was taken from Kaggle. The experimental results show an accuracy of 93.15%, precision of 92.65%, recall of 95.71%, and F1-score of 94.15% for the support vector machine algorithm. The SVM is better than the second best classifier, i.e., logistic regression, by 6.82%.

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Efficient n-gram construction for text categorization using feature selection techniques

Cited by 15 publications

References 43 publications

Numerical Analysis and Optimization of Feature Extraction-Oriented English Reading Corpus

Numerical Analysis and Optimization of Feature Extraction-Oriented English Reading Corpus

A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network

Ternion: An Autonomous Model for Fake News Detection

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