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New Techniques of Machine Learning and Deep Learning in Text Classification

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 17071

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Dr. Duy-Tai Dinh

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Guest Editor

The Kyoto College of Graduate Studies for Informatics, Kyoto 606-8225, Japan
Interests: data mining; machine learning; pattern recognition; deep learning

Dr. Uday Kiran Rage

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Guest Editor

Institute of Industrial Science, University of Tokyo, Bunkyo City, Tokyo 113-8654, Japan
Interests: data mining; big data analytics; icts in agriculture
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Dear Colleagues,

Text classification is at the heart of a variety of software systems that process text data at a large scale. It performs fundamental tasks in natural language processing and is applied broadly for sentiment analysis, spam detection, and topic labeling. Nowadays, data can be easily collected from various sources with diverse formats, with text being one of the most common types of unstructured data. However, as data size grows exponentially, traditional technologies have become unable to handle such massive and unstructured data, leading to the emergence of big data in text classification and cutting-edge machine learning and deep learning techniques that can perform high-accuracy lower-level engineering and computation functions.

This Special Issue aims to bring together cutting-edge research from both academia and the industry focusing on new techniques for machine learning and deep learning for text classification. To this aim, we are calling for researchers with broad expertise in various fields to present their cutting-edge work as well as perspectives on future directions in this exciting field. Submissions covering all theoretical and practical aspects, technologies, and systems in this research area are welcome.

Dr. Duy-Tai Dinh
Dr. Uday Kiran RAGE
Guest Editors

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Keywords

data mining and knowledge discovery
dialogue and interactive systems
information extraction
information retrieval and text mining
interpretability and analysis of models for NLP
machine learning for NLP
machine translation and multilinguality
NLP applications
question answering
real-time segmentation, clustering, and classification
resources and evaluation
semantics: lexical, sentence level, textual inference, and other areas
sentiment analysis, stylistic analysis, and argument mining
summarization

Published Papers (9 papers)

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Research

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19 pages, 454 KiB

Open AccessArticle

Social Network Sentiment Analysis Using Hybrid Deep Learning Models

by Noemí Merayo, Jesús Vegas, César Llamas and Patricia Fernández

Appl. Sci. 2023, 13(20), 11608; https://doi.org/10.3390/app132011608 - 23 Oct 2023

Cited by 1 | Viewed by 1329

Abstract

The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative). Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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14 pages, 1772 KiB

Open AccessArticle

Prompt-Based Graph Convolution Adversarial Meta-Learning for Few-Shot Text Classification

by Ruwei Gong, Xizhong Qin and Wensheng Ran

Appl. Sci. 2023, 13(16), 9093; https://doi.org/10.3390/app13169093 - 09 Aug 2023

Cited by 1 | Viewed by 1071

Abstract

Deep learning techniques have demonstrated significant advancements in the task of text classification. Regrettably, the majority of these techniques necessitate a substantial corpus of annotated data to achieve optimal performance. Meta-learning has yielded intriguing outcomes in few-shot learning tasks, showcasing its potential in advancing the field. However, the current meta-learning methodologies are susceptible to overfitting due to the mismatch between a small number of samples and the complexity of the model. To mitigate this concern, we propose a Prompt-based Graph Convolutional Adversarial (PGCA) meta-learning framework, aiming to improve the adaptability of complex models in a few-shot scenario. Firstly, leveraging prompt learning, we generate embedding representations that bridge the downstream tasks. Then, we design a meta-knowledge extractor based on a graph convolutional neural network (GCN) to capture inter-class dependencies through instance-level interactions. We also integrate the adversarial network architecture into a meta-learning framework to extend sample diversity through adversarial training and improve the ability of the model to adapt to new tasks. Specifically, we mitigate the impact of extreme samples by introducing external knowledge to construct a list of class prototype extensions. Finally, we conduct a series of experiments on four public datasets to demonstrate the effectiveness of our proposed method. Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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18 pages, 528 KiB

Open AccessArticle

From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies

by Karol Draszawka and Julian Szymański

Appl. Sci. 2023, 13(13), 7591; https://doi.org/10.3390/app13137591 - 27 Jun 2023

Viewed by 1117

Abstract

In this paper, we propose a novel approach for obtaining predictions from per-class scores to improve the accuracy of multi-label classification systems. In a multi-label classification task, the expected output is a set of predicted labels per each testing sample. Typically, these predictions are calculated by implicit or explicit thresholding of per-class real-valued scores: classes with scores exceeding a given threshold value are added to a prediction set. In our work, we propose a neural network-based thresholding phase for multi-label classification systems and examine its influence on the overall classification performance measured by micro- and macro-averaged F1 scores on synthetic and real datasets. In contrast to classic thresholding methods, our approach has the unique property of being able to recover from scoring errors, because each decision about a given label prediction depends on the corresponding class score, as well as on all the other class scores for a given sample at once. The method can be used in combination with any classification system that outputs real-valued class scores. The proposed thresholding methods are trained offline, after the completion of the scoring phase. As such, it can be considered a universal fine-tuning step that can be employed in any multi-label classification system that seeks to find the best multi-label predictions based on class scores. In our experiments on real datasets, the input class scores were obtained from two third-party baseline classification systems. We show that our approach outperforms the traditional thresholding methods, which results in the improved performance of all tested multi-label classification tasks. In terms of relative improvement, on real datasets, the micro-F1 score is higher by up to 40.6%, the macro-F1 score is higher by up to 3.6%, and the averaged micro–macro-F1 score is higher by up to 30.1%, considering single models only. We show that ensembles and hybrid models give even better results. We show examples of successful extreme recoveries, where the system, equipped with our method, was able to correctly predict labels, which were highly underscored after the scoring phase. Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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11 pages, 590 KiB

Open AccessArticle

Classification of Research Papers on Radio Frequency Electromagnetic Field (RF-EMF) Using Graph Neural Networks (GNN)

by Youngsun Jang, Kwanghee Won, Hyung-do Choi and Sung Y. Shin

Appl. Sci. 2023, 13(7), 4614; https://doi.org/10.3390/app13074614 - 05 Apr 2023

Cited by 3 | Viewed by 1628

Abstract

This study compares the performance of graph convolutional neural network (GCN) models with conventional natural language processing (NLP) models for classifying scientific literature related to radio frequency electromagnetic field (RF-EMF). Specifically, the study examines two GCN models: BertGCN and the citation-based GCN. The study concludes that the model achieves consistently good performance when the input text is long enough, based on the attention mechanism of BERT. When the input sequence is short, the composition parameter

λ

, which combines output values of the two subnetworks of BertGCN, plays a crucial role in achieving high classification accuracy. As the value of

λ

increases, the classification accuracy also increases. The study also proposes and tests a simplified variant of BertGCN, revealing performance differences among the models under two different data conditions by the existence of keywords. This study has two main contributions: (1) the implementation and testing of a variant of BertGCN and citation-based GCN for document classification tasks related to radio frequency electromagnetic fields publications, and (2) the confirmation of the impact of model conditions, such as the existence of keywords and input sequence length, in the original BertGCN. Although this study focused on a specific domain, our approaches have broader implications that extend beyond scientific publications to general text classification. Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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21 pages, 1079 KiB

Open AccessArticle

Multilabel Text Classification with Label-Dependent Representation

by Rodrigo Alfaro, Héctor Allende-Cid and Héctor Allende

Appl. Sci. 2023, 13(6), 3594; https://doi.org/10.3390/app13063594 - 11 Mar 2023

Cited by 1 | Viewed by 2416

Abstract

Assigning predefined classes to natural language texts, based on their content, is a necessary component in many tasks in organizations. This task is carried out by classifying documents within a set of predefined categories using models and computational methods. Text representation for classification purposes has traditionally been performed using a vector space model due to its good performance and simplicity. Moreover, the classification of texts via multilabeling has typically been approached by using simple label classification methods, which require the transformation of the problem studied to apply binary techniques, or by adapting binary algorithms. Over the previous decade, text classification has been extended using deep learning models. Compared to traditional machine learning methods, deep learning avoids rule design and feature selection by humans, and automatically provides semantically meaningful representations for text analysis. However, deep learning-based text classification is data-intensive and computationally complex. Interest in deep learning models does not rule out techniques and models based on shallow learning. This situation is true when the set of training cases is smaller, and when the set of features is small. White box approaches have advantages over black box approaches, where the feasibility of working with relatively small sets of data and the interpretability of the results stand out. This research evaluates a weighting function of the words in texts to modify the representation of the texts during multilabel classification, using a combination of two approaches: problem transformation and model adaptation. This weighting function was tested in 10 referential textual data sets, and compared with alternative techniques based on three performance measures: Hamming Loss, Accuracy, and macro-

F_{1}

. The best improvement occurs on the macro-

F_{1}

when the data sets have fewer labels, fewer documents, and smaller vocabulary sizes. In addition, the performance improves in data sets with higher cardinality, density, and diversity of labels. This proves the usefulness of the function on smaller data sets. The results show improvements of more than 10% in terms of macro-

F_{1}

in classifiers based on our method in almost all of the cases analyzed. Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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14 pages, 5595 KiB

Open AccessArticle

Research on Intelligent Perception Algorithm for Sensitive Information

by Lin Huo and Juncong Jiang

Appl. Sci. 2023, 13(6), 3383; https://doi.org/10.3390/app13063383 - 07 Mar 2023

Cited by 2 | Viewed by 1335

Abstract

In the big data era, a tremendous volume of electronic documents is transmitted via the network, many of which include sensitive information about the country and businesses. There is a pressing need to be able to perform intelligent sensing of sensitive information on these documents in order to be able to discover and guarantee the security of sensitive information in this enormous volume of documents. Although the low effectiveness of manual detection is resolved by the current method of handling sensitive information, there are still downsides, such as poor processing effects and slow speed. This study creatively proposes the Text Sensitive Information Intelligent Perception algorithm (TSIIP), which detects sensitive words at the word level and sensitive statements at the statement level to obtain the final assessment score of the text. We experimentally compare this algorithm with other methods on an existing dataset of sensitive Chinese information. We use the metrics measuring the accuracy of the binary classification model, where the F1 score reaches 0.938 (+0.6%), and the F2 score reaches 0.946 (+1%), and the experimental results fully demonstrate the superiority of this algorithm. Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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16 pages, 368 KiB

Open AccessArticle

PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages

by Dimitrios Zaikis, Nikolaos Stylianou and Ioannis Vlahavas

Appl. Sci. 2023, 13(5), 3265; https://doi.org/10.3390/app13053265 - 03 Mar 2023

Cited by 2 | Viewed by 1592

Abstract

Media analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, through the computational treatment of opinions, sentiments, and subjectivity on mostly highly subjective text types. These texts can come from social media, the internet, and news articles with clearly defined and unique targets. Additionally, MA-related fields include emotion, irony, and hate speech detection, which are usually tackled independently from one another without leveraging the contextual similarity between them, mainly attributed to the lack of annotated datasets. In this paper, we present a unified framework to the complete intelligent media analysis, where we propose a shared parameter layer architecture with a joint learning approach that takes advantage of each separate task for the classification of sentiments, emotions, irony, and hate speech in texts. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, and internet articles such as blog posts and opinion pieces. The results show that this joint classification approach improves the classification effectiveness of each task in terms of the micro-averaged F1-score. Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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22 pages, 9316 KiB

Open AccessArticle

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

by Ram Krishn Mishra, Harshit Raj, Siddhaling Urolagin, J. Angel Arul Jothi and Nishad Nawaz

Appl. Sci. 2022, 12(16), 8105; https://doi.org/10.3390/app12168105 - 12 Aug 2022

Cited by 12 | Viewed by 3546

Abstract

The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpful for consumers and governments. Natural language processing (NLP) techniques have traditionally been used to tackle the issues of structuring of unprocessed data, and the representation of the data in a knowledge-based system. NLP is able to capture the full richness of the text by extracting the entity and relationship from the processed data, which is gathered from various social media platforms, webpages, blogs, and other online sources, while successfully taking into consideration the semantics of the text. With the purpose of detecting connections between tourism and economy, the research aims to present a visual representation of the refined data using knowledge graphs. In this research, the data has been gathered from Twitter using keyword extraction techniques with an emphasis on tourism and economy. The research uses TextBlob to convert the tweets to numeric vector representations and further uses clustering techniques to group similar entities. A cluster-wise knowledge graph has been constructed, which comprises a large number of relationships among various factors, that visualize entities and their relationships connecting tourism and economy. Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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Review

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26 pages, 1840 KiB

Open AccessReview

Research Practice and Progress of Models and Algorithms Applied in Topic Identification and Prediction Based on the Analysis of CNKI

by Sicheng Guo, Li Si and Xianrui Liu

Appl. Sci. 2023, 13(13), 7545; https://doi.org/10.3390/app13137545 - 26 Jun 2023

Viewed by 1558

Abstract

As a hot topic in the field of library and information, the research on topic recognition and trend prediction has been paid close attention by academic circles. This paper uses a systematic literature review, bibliometric analyses and classification methods. Through a systematic literature review, 96 studies about topic identification and evolution prediction models are selected from the CNKI database. By using VOSviewer to conduct bibliometric analyses, the key research content and themes are revealed. Through the classification method, EXCEL is used to summarize models and algorithms used in the literature comprehensively. It is found that topic identification models and algorithms can be divided into four categories: ① Topic model based on LDA and related derivative models. ② Machine learning and deep learning methods. ③ Methods based on reference relation. ④ Text mining methods. Trend prediction models and algorithms mainly cover two categories: ① deep learning or machine learning models and algorithms based on time sequence; ② link prediction algorithms based on complex network. At the same time, we have also summarized the common index system involved in each study and the way to evaluate the effectiveness of the method, thus this paper comprehensively reveals the application progress in academic circles of topic identification and prediction models and algorithms from the last 10 years and beyond, based on the CNKI database. The purpose is to determine the most popular models and algorithms applied in research, generalize the corresponding indicator systems and validation methods, and finally provide references for model choice or evaluation when identifying and predicting topics in the future. Thus, this paper can help us to understand the overall progress made in text analysis research, and provides a useful reference for selecting and applying the appropriate models, algorithms and indicators. Full article

(This article belongs to the Special Issue New Techniques of Machine Learning and Deep Learning in Text Classification)

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