Text Clustering Techniques Research Articles

Toward an ecosystem of non-fungible tokens from mapping public opinions on social media

As blockchain technology advances, non-fungible tokens (NFTs) are emerging as unconventional assets in the commercial market. However, it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailing public concerns. This study aimed to bridge this gap by analyzing user-generated content on prominent social media platforms such as Twitter, Weibo, and Reddit. Employing text clustering and topic modeling techniques, such as Latent Dirichlet Allocation, we constructed an analytical framework to delve into the intricacies of the NFT ecosystem. Our investigation revealed seven distinct topics from Twitter and Reddit data and eight topics from Weibo data. Weibo users predominantly engaged in reviews and critiques, whereas Twitter and Reddit users emphasized personal experiences and perceptions. The NFT ecosystem encompasses several crucial elements, including transactions, customers, infrastructure, products, environments, and perceptions. By identifying the prevailing trends and common issues, this study offers valuable guidance for the development of NFT ecosystems.

Teachings on Writing Narrative Texts Through Teaching Clustering to Tenth Grade Students Nurul Huda Sukaraja High School

Applying clustering methodologies to instruct narrative texts to tenth grade students at SMK Nurul Huda Sukaraja. The proof for this assertion is substantiated by the results of the Independent Sample t-test performed on the post-test scores of both the experimental and control groups. The calculated t-values were 5.954 and 5.984, with a significance (two-tailed) value of 0.000. Thus, it is feasible for the t-value to surpass the critical t-value of 2.021 when the degrees of freedom (df) are 49, given that the sample size (n) is 51. Furthermore, the two-tailed significance (Sig) value is below the preset significance limit (α) of 0.05. The Null Hypothesis is rejected and the Alternative Hypothesis (Ha) is upheld. There is a significant difference between students who are educated using the Write Narrative Text Clustering Technique and students who are guided using Write Narrative Text utilising the Clustering Technique. The results further demonstrated that the use of the Clustering Technique in the students' class resulted in a decrease in their writing challenges. By using the Clustering methodology, students may pinpoint the underlying source of writing problems and successfully address them. This strategy is very efficacious in augmenting the understanding of the topic matter.

A comprehensive and analytical review of text clustering techniques

A comprehensive and analytical review of text clustering techniques

Beyond content: discriminatory power of function words in text type classification

Abstract Our work aims to evaluate the strength of the association between function words and several text types: novels, poems, academic articles, reviews, and blog posts, and the accuracy of their classification to these categories, through machine-learning and statistical methods. The principal conclusion is that the types of texts are distinguishable based only on the function words, either by vocabulary or vocabulary diversity. Such findings may impact the techniques of authorship attribution based on function words and text clustering techniques since some function words add information about the text types/genres, in addition to content words.

Employing Machine Learning to Deduce a Causal Link Between Corporate Social Responsibility and Financial Performance

Incorporating Corporate Social Responsibility (CSR) into business strategy has become noteworthy, as that pertains to the discretionary initiatives a business entity undertakes to enhance its operational sphere’s societal and ecological circumstances. The evaluation of a company’s success is closely tied to its financial performance, which pertains to its capacity to produce earnings and enhance shareholder worth. The correlation between CSR and financial performance has been a topic of significant discourse, with numerous studies examining this subject from diverse perspectives. Nevertheless, establishing a causal association between the concepts above poses a formidable challenge, owing to the intricate nature of the association and the likelihood of extraneous variables exerting an effect. This study suggests a model for CSR based on Machine Learning (ML), referred to as the Machine Learning-based Corporate Social Responsibility Model (ML-CSRM). This model aims to establish a causal relationship between CSR and financial performance. The paper commences with a comprehensive exposition of CSR and its correlation with financial performance. The discourse delves into the intricacies of ascertaining the causal relationship between the two. The method under consideration utilizes Latent Dirichlet Allocation (LDA) and text clustering techniques to examine extant legal documents about CSR. The study presents the proposed method’s simulation results [Pearson’s Correlation Coefficient, precision, recall, Root-Mean-Squared Error (RMSE), and R-squared] and discusses the proposed approach’s outcomes. The outcomes of this research carry significant ramifications for both commercial enterprises and the broader community, given that they offer discernment into the correlation between CSR and economic outcomes.

A Study on Convergence of Text Analysis Techniques for Social Science Research in the Age of Big Data

In this paper, we do not only propose appropriate application methods of the semantic network analysis technique, topic modeling technique and text clustering technique, which are techniques that can analyze text document sets but also the convergence application methods between each techniques. Appropriate application methods for the text analysis techniques were presented by dividing exploratory analysis process and modeling process for analyzing the text document set. Semantic network analysis would be not only suitable for exploratory analysis of the entire text document set, but also applicable to performance improvement of topic modeling and text clustering. The meaning of the topic could be interpreted more clearly by applying semantic network analysis secondarily to the document set of each topic derived by topic modeling. It can be used for text cluster interpretation by applying semantic network analysis to the document set of each cluster, which is the result of text cluster analysis. When deriving an appropriate number of topics, a more accurate number of topics could be derived by adding a silhouette analysis along with the degree of perplexity and coherence. New social science research can be expected to be revitalized by enabling more accurate analysis of online text documents using the method proposed in this paper.

Analyzing Emergency News Comments to Assess Online Public Opinions: A Case Study of COVID-19

For the purpose of enhancing the online public opinion intervention mechanism, fostering a positive public opinion environment, it is crucial to examine the rules and traits of online public opinion dissemination from multiple perspectives. The paper proposes a fresh approach to measuring network public opinion by analyzing emergency news comments. In order to develop risk assessment indicators, we first employ the multi-round Delphi method. Then, we organize the "COVID-19" news comments using natural language processing and text clustering techniques, correlating the risk assessment indicators with the risk evolution of emergency events. Finally, we analyze the time evolution trend of users’ participation in network public opinion. Results show that the use of news commentary can effectively predict the tendency of social risk. Therefore, the risk assessment method in this paper can judge and warn the network public opinion in time, which is of great value in assisting major emergency management decisions.

WEClustering: word embeddings based text clustering technique for large datasets

A massive amount of textual data now exists in digital repositories in the form of research articles, news articles, reviews, Wikipedia articles, and books, etc. Text clustering is a fundamental data mining technique to perform categorization, topic extraction, and information retrieval. Textual datasets, especially which contain a large number of documents are sparse and have high dimensionality. Hence, traditional clustering techniques such as K-means, Agglomerative clustering, and DBSCAN cannot perform well. In this paper, a clustering technique especially suitable to large text datasets is proposed that overcome these limitations. The proposed technique is based on word embeddings derived from a recent deep learning model named “Bidirectional Encoders Representations using Transformers”. The proposed technique is named as WEClustering. The proposed technique deals with the problem of high dimensionality in an effective manner, hence, more accurate clusters are formed. The technique is validated on several datasets of varying sizes and its performance is compared with other widely used and state of the art clustering techniques. The experimental comparison shows that the proposed clustering technique gives a significant improvement over other techniques as measured by metrics such Purity and Adjusted Rand Index.

A Survey of Text Clustering Techniques: Algorithms, Applications, and Challenges

A Survey of Text Clustering Techniques: Algorithms, Applications, and Challenges

What is the impact of service quality on customers’ satisfaction during COVID-19 outbreak? New findings from online reviews analysis

The COVID-19 pandemic has caused major global changes both in the areas of healthcare and economics. This pandemic has led, mainly due to conditions related to confinement, to major changes in consumer habits and behaviors. Although there have been several studies on the analysis of customers’ satisfaction through survey-based and online customers’ reviews, the impact of COVID-19 on customers' satisfaction has not been investigated so far. It is important to investigate dimensions of satisfaction from the online customers’ reviews to reveal their preferences on the hotels' services during the COVID-19 outbreak. This study aims to reveal the travelers’ satisfaction in Malaysian hotels during the COVID-19 outbreak through online customers’ reviews. In addition, this study investigates whether service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. Accordingly, we develop a new method through machine learning approaches. The method is developed using text mining, clustering, and prediction learning techniques. We use Latent Dirichlet Allocation (LDA) for big data analysis to identify the voice-of-the-customer, Expectation-Maximization (EM) for clustering, and ANFIS for satisfaction level prediction. In addition, we use Higher-Order Singular Value Decomposition (HOSVD) for missing value imputation. The data was collected from TripAdvisor regarding the travelers’ concerns in the form of online reviews on the COVID-19 outbreak and numerical ratings on hotel services from different perspectives. The results from the analysis of online customers’ reviews revealed that service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. In addition, the results showed that although the customers are always seeking hotels with better performance, they are also concerned with the quality of related services in the COVID-19 outbreak.

Aspect‐based requirements mining technique to improve prioritisation process: multi‐stakeholder perspective

Requirement prioritisation and selection is an essential activity in modern-day large software development. Optimal prioritisation process is critical for successful implementation and release planning in a software development project. Requirement prioritisation becomes more challenging in projects having large sets of requirements and stakeholders, having diverse perspectives resulting in irrelevancy and ambiguity during features extraction. This study aims to improve requirement prioritisation process using text mining and clustering techniques for accurate extraction of features and requirement prioritisation in multi-stakeholder context. The proposed framework developed to avoid incompleteness in requirements and disagreement among development teams and stakeholders. Thus, the proposed framework compared with other requirement prioritisation techniques (i.e. Analytical Heretical Process, Commutative Voting and Wiegers) to highlight the significance of the proposed framework while conducting an experimental study. The results show that the proposed framework outperformed the traditional techniques and enhanced the prioritisation process with complete semantic information of extracted features and taking into account the diverse perspective of stakeholders.

Corporate image or social engagement: Twitter discourse on corporate social responsibility (CSR) in public relations strategies in the energy sector

Social media have opened up new opportunities for the creation of innovative public relations strategies focused on establishing and cultivating relationships with stakeholders on the basis of meaningful dialogue. Consideration of the interrelation between corporate social responsibility (CSR) and public relations highlights new areas for exploration and engagement. Both the dialogical and semantic perspectives reveal the performative and conversational aspects of social media. In general, both the linguistic panorama of CSR and digital media as part of a PR strategy open new possibilities for a dialogical, interactive, meaningful relationship strategy for corporate image management. Based on the linguistic approach to CSR and the Communication Management Approach, this paper explores the linguistic use of Twitter as a primary dialogical strategy to effectively enhance interactive dialogue-based relationships with the stakeholders of the top 50 companies in the energy sector based on tweet data from 2016. Semantic analysis was conducted by advanced text mining and clustering techniques on 3042 tweets monitored in 2017 that contained the leading CSR-related hashtags and keywords. The results demonstrated that the top energy companies apply a defensive and symbolic perspective, mainly for branding purposes. The corporate discourse dominates over a meaningful conversational strategy to foster interaction with stakeholders around sustainability issues on Twitter. The study reveals a homogenized interrelation between CSR, social media, and public relations. The results reveal a tendency for isomorphy in the communication models applied by the companies in the energy sector. Furthermore, similarities in semantics and thus strong tendencies to mutually mimic dialogical strategies are also observed. The semantic narrative built around the brand indicates a limited orientation towards CSR and sustainability. As such, it does not contribute to the creation of a dialogical interaction and meaningful relationships with multiple stakeholders on Twitter, in the high-risk sector represented by the energy industry.

ML-EC2

A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work. ML-EC2 is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping technique. It is an example of classification using text clustering technique. It studies the problem where each email cluster represents a single class label while it is associated with set of cluster labels. It is multi-label text-clustering-based classification algorithm in which an email cluster can be mapped to more than one email category when cluster label matches with more than one category term. The algorithm will be helpful when there is a vague idea of topic. The performance parameters Entropy and Davies-Bouldin Index are used to evaluate the designed algorithm.

Analysis of K-means, DBSCAN and OPTICS Cluster Algorithms on Al-Quran Verses

Chapter Al-Baqarah is the longest chapter in the Holy Quran, and it covers various topics. Al-Quran is the primary text of Islamic faith and practice. Millions of Muslims worldwide use Al - Quran as their reference book, and it, therefore, helps Muslims and Islamic scholars as guidance of the law life. Text clustering (unsupervised learning) is a process of separation that has to be divided text into the same section of similar documents. There are many text clustering algorithms and techniques used to make clusters, such as partitioning and density-based methods. In this paper, k-means preferred as a partitioning method and DBSCAN, OPTICS as a density-based method. This study aims to investigate and find which algorithm produced as the best accurate performance cluster for Al-Baqarah’s English Tafseer chapter. Data preprocessing and feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) have applied for the dataset. The result shows k-means outperformed even has the smallest of Silhouette Coefficient (SC) score compared to others due to less implementation time with no noise production for seven clusters of Al-Baqarah chapter. OPTICS has no noise with the medium of SC score but has the longest implementation time due to its complexity.

A new feature selection method to improve the document clustering using particle swarm optimization algorithm

The large amount of text information on the Internet and in modern applications makes dealing with this volume of information complicated. The text clustering technique is an appropriate tool to deal with an enormous amount of text documents by grouping these documents into coherent groups. The document size decreases the effectiveness of the text clustering technique. Subsequently, text documents contain sparse and uninformative features (i.e., noisy, irrelevant, and unnecessary features), which affect the effectiveness of the text clustering technique. The feature selection technique is a primary unsupervised learning method employed to select the informative text features to create a new subset of a document's features. This method is used to increase the effectiveness of the underlying clustering algorithm. Recently, several complex optimization problems have been successfully solved using metaheuristic algorithms. This paper proposes a novel feature selection method, namely, feature selection method using the particle swarm optimization (PSO) algorithm (FSPSOTC) to solve the feature selection problem by creating a new subset of informative text features. This new subset of features can improve the performance of the text clustering technique and reduce the computational time. Experiments were conducted using six standard text datasets with several characteristics. These datasets are commonly used in the domain of the text clustering. The results revealed that the proposed method (FSPSOTC) enhanced the effectiveness of the text clustering technique by dealing with a new subset of informative features. The proposed method is compared with the other well-known algorithms i.e., feature selection method using a genetic algorithm to improve the text clustering (FSGATC), and feature selection method using the harmony search algorithm to improve the text clustering (FSHSTC) in the text feature selection.

Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering

The text clustering technique is an appropriate method used to partition a huge amount of text documents into groups. The documents size affects the text clustering by decreasing its performance. Subsequently, text documents contain sparse and uninformative features, which reduce the performance of the underlying text clustering algorithm and increase the computational time. Feature selection is a fundamental unsupervised learning technique used to select a new subset of informative text features to improve the performance of the text clustering and reduce the computational time. This paper proposes a hybrid of particle swarm optimization algorithm with genetic operators for the feature selection problem. The k-means clustering is used to evaluate the effectiveness of the obtained features subsets. The experiments were conducted using eight common text datasets with variant characteristics. The results show that the proposed algorithm hybrid algorithm (H-FSPSOTC) improved the performance of the clustering algorithm by generating a new subset of more informative features. The proposed algorithm is compared with the other comparative algorithms published in the literature. Finally, the feature selection technique encourages the clustering algorithm to obtain accurate clusters.

An Improved Similarity Matching based Clustering Framework for Short and Sentence Level Text

Text clustering plays a key role in navigation and browsing process. For an efficient text clustering, the large amount of information is grouped into meaningful clusters. Multiple text clustering techniques do not address the issues such as, high time and space complexity, inability to understand the relational and contextual attributes of the word, less robustness, risks related to privacy exposure, etc. To address these issues, an efficient text based clustering framework is proposed. The Reuters dataset is chosen as the input dataset. Once the input dataset is preprocessed, the similarity between the words are computed using the cosine similarity. The similarities between the components are compared and the vector data is created. From the vector data the clustering particle is computed. To optimize the clustering results, mutation is applied to the vector data. The performance the proposed text based clustering framework is analyzed using the metrics such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Processing time. From the experimental results, it is found that, the proposed text based clustering framework produced optimal MSE, PSNR and processing time when compared to the existing Fuzzy C-Means (FCM) and Pairwise Random Swap (PRS) methods.

SMS spam filtering and thread identification using bi-level text classification and clustering techniques

SMS spam detection is an important task where spam SMS messages are identified and filtered. As greater numbers of SMS messages are communicated every day, it is very difficult for a user to remember and correlate the newer SMS messages received in context to previously received SMS. SMS threads provide a solution to this problem. In this work the problem of SMS spam detection and thread identification is discussed and a state of the art clustering-based algorithm is presented. The work is planned in two stages. In the first stage the binary classification technique is applied to categorize SMS messages into two categories namely, spam and non-spam SMS; then, in the second stage, SMS clusters are created for non-spam SMS messages using non-negative matrix factorization and K-means clustering techniques. A threading-based similarity feature, that is, time between consecutive communications, is described for the identification of SMS threads, and the impact of the time threshold in thread identification is also analysed experimentally. Performance parameters like accuracy, precision, recall and F-measure are also evaluated. The SMS threads identified in this proposed work can be used in applications like SMS thread summarization, SMS folder classification and other SMS management-related tasks.

Text Mining, its Utilities, Challenges and Clustering Techniques

Text analysis is an interdisciplinary field of data mining in which person try to extract meaningful results from the unstructured textual data. In this paper the focus will be on different text mining application, the problems that we face while doing text mining and different text clustering approaches and try to figure out what next can be done for better performance of clustering algorithms.

Extraction of Hidden Social Networks from Wiki-Environment Involved in Information Conflict

Social network analysis is a widely used technique to analyze relationships among wiki-users in Wikipedia. In this paper the method to identify hidden social networks participating in information conflicts in wiki-environment is proposed. In particular, we describe how text clustering techniques can be used for extraction of hidden social networks of wiki-users caused information conflict. By clustering unstructured text articles caused information conflict we create social network of wiki-users. For clustering of the conflict articles a hybrid weighted fuzzy-c-means method is proposed.