A Systematic Study on Textual Data Processing in Text Mining

Text clustering is an important method for organising the increasing volume of digital content, aiding in the structuring and discovery of hidden patterns in uncategorised data. The effectiveness of text clustering largely depends on the selection of textual embeddings and clustering algorithms. This study argues that recent advancements in large language models (LLMs) have the potential to enhance this task. The research investigates how different textual embeddings, particularly those utilised in LLMs, and various clustering algorithms influence the clustering of text datasets. A series of experiments were conducted to evaluate the impact of embeddings on clustering results, the role of dimensionality reduction through summarisation, and the adjustment of model size. The findings indicate that LLM embeddings are superior at capturing subtleties in structured language. OpenAI’s GPT-3.5 Turbo model yields better results in three out of five clustering metrics across most tested datasets. Most LLM embeddings show improvements in cluster purity and provide a more informative silhouette score, reflecting a refined structural understanding of text data compared to traditional methods. Among the more lightweight models, BERT demonstrates leading performance. Additionally, it was observed that increasing model dimensionality and employing summarisation techniques do not consistently enhance clustering efficiency, suggesting that these strategies require careful consideration for practical application. These results highlight a complex balance between the need for refined text representation and computational feasibility in text clustering applications. This study extends traditional text clustering frameworks by integrating embeddings from LLMs, offering improved methodologies and suggesting new avenues for future research in various types of textual analysis.

Phrase2Vec: Phrase embedding based on parsing

Recent advances in text mining have provided new methods for capitalizing on the voluminous natural language text data created by organizations, their employees, and their customers. Although often overlooked, decisions made during text preprocessing affect whether the content and/or style of language are captured, the statistical power of subsequent analyses, and the validity of insights derived from text mining. Past methodological articles have described the general process of obtaining and analyzing text data, but recommendations for preprocessing text data were inconsistent. Furthermore, primary studies use and report different preprocessing techniques. To address this, we conduct two complementary reviews of computational linguistics and organizational text mining research to provide empirically grounded text preprocessing decision-making recommendations that account for the type of text mining conducted (i.e., open or closed vocabulary), the research question under investigation, and the data set’s characteristics (i.e., corpus size and average document length). Notably, deviations from these recommendations will be appropriate and, at times, necessary due to the unique characteristics of one’s text data. We also provide recommendations for reporting text mining to promote transparency and reproducibility.

In today's computerized and information-based society, text data is rich but messy. People are soaked with vast amounts of natural-language text data, ranging from news articles, social media post, advertisements, to a wide range of textual information from various domains (medical records, corporate reports). To turn such massive unstructured text data into actionable knowledge, one of the grand challenges is to gain an understanding of the factual information (e.g., entities, attributes, relations, events) in the text. In this tutorial, we introduce data-driven methods to construct structured information networks (where nodes are different types of entities attached with attributes, and edges are different relations between entities) for text corpora of different kinds (especially for massive, domain-specific text corpora) to rep- resent their factual information. We focus on methods that are minimally-supervised, domain-independent, and language-independent for fast network construction across various application domains (news, web, biomedical, reviews). We demonstrate on real datasets including news articles, scientific publications, tweets and reviews how these constructed networks aid in text analytics and knowledge discovery at a large scale.

As text data continues to grow quickly, it is increasingly important to develop intelligent systems to help people manage and make use of vast amounts of text data (big text data''). As a new family of effective general approaches to text data retrieval and analysis, probabilistic topic models---notably Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocations (LDA), and their many extensions---have been studied actively in the past decade with widespread applications. These topic models are powerful tools for extracting and analyzing latent topics contained in text data; they also provide a general and robust latent semantic representation of text data, thus improving many applications in information retrieval and text mining. Since they are general and robust, they can be applied to text data in any natural language and about any topics. This tutorial systematically reviews the major research progress in probabilistic topic models and discuss their applications in text retrieval and text mining. The tutorial provides (1) an in-depth explanation of the basic concepts, underlying principles, and the two basic topic models (i.e., PLSA and LDA) that have widespread applications, (2) an introduction to EM algorithms and Bayesian inference algorithms for topic models, (3) a hands-on exercise to allow the tutorial attendants to learn how to use the topic models implemented in the MeTA Open Source Toolkit and experiment with provided data sets, (4) a broad overview of all the major representative topic models that extend PLSA or LDA, and (5) a discussion of major challenges and future research directions.

Large data nowadays can be used for analysis; thus, it can obtain important/valuable knowledge in various domains. Text analysis can be carried out by utilizing text mining using computational methods so that knowledge extraction can be carried out on large text data, including processing related to unstructured text data, which is written in natural language. Classification in text mining is a type of work with the searching process for a set of models or functions that describe and differentiate text data classes with the aim that the model can be used to predict the class of an object (text data) whose class is unknown. Text mining was carried out in this research to analyze text data through the Text Classification System using a classification method, namely the XGBoost (eXtreme Gradient Boosting) Method. A text classification system was developed to classify text in the form of articles. The highest accuracy obtained from the test is 77%.

Automatic keywords extraction and classification tasks are important research directions in the domains of NLP (natural language processing), information retrieval, and text mining. As the fine granularity abstracted from text data, keywords are also the most important feature of text data, which has great practical and potential value in document classification, topic modeling, information retrieval, and other aspects. The compact representation of documents can be achieved through keywords, which contains massive significant information. Therefore, it may be quite advantageous to realize text classification with high-dimensional feature space. For this reason, this study designed a supervised keyword classification method based on TextRank keyword automatic extraction technology and optimize the model with the genetic algorithm to contribute to modeling the keywords of the topic for text classification.

Text mining is the procedure of exploring large unorganized text data. Due to the availability of numerous amounts of text data through online blogs, newspapers and other media, text classification and categorization is the hot topic nowadays. Many researches have been done on this topic on English and other western languages. However, very few notable researches have been on Bangla language. Unavailability of a notable dataset in Bangla language is another burden to develop a highperformance text classification tool. In this paper, we have presented a Bangla news tags classification approach. The classification has been done entirely based on news titles only with parallel Convolutional Neural Network (CNN) which is a category of deep neural networks utilizing word-level data augmentation approach. Due to the unavailability of a proper and updated dataset on Bangla news titles and tags, we have developed our own dataset which consists of 88,968 news titles and tags by scrapping online newspapers. According to the classification result, our approach shows an accuracy of 93.47% which is the highest amongst the similar works.

Huge digital data is typical nowadays. Consequently, text clustering has become a crucial text mining technique nowadays. Organizing search information into groups (or clusters) not only makes the search results more meaningful, but makes the system more efficient since the user views only the relevant information and ignores the rest. An example is clustering web search engine result into meaningful results. Many text clustering approaches and their corresponding algorithms exist, but none has been found to be sufficient. There is also insufficient understanding of the algorithms as well as lack of agreed formal classification of the algorithms. There is thus, need for an in-depth study of the various algorithms. In this paper, we describe the Vector Space Model (VSM) method of text representation. We also give an overview of the text clustering approaches that apply the VSM .These include distance based approach, feature extraction approach, density-based approach, grid-based approach, and neural networks approach. We describe the characteristics of each accompanied by a representative algorithm. The paper thus informs researchers of text mining concerning the current state of affairs of text clustering algorithm. Keywords: clusters, text clustering, text mining, vector space model.

Incorporating textual information in customer churn prediction models based on a convolutional neural network

Our goal is to support users who want to discover or create knowledge from a large amount of text data. Text mining is a process that extracts novel knowledge from unstructured data. A variety of applications for text mining, such as Total Environment for Text Data Mining, have been proposed. However, to the best of our knowledge, these methods are not effective at conducting text mining tasks aimed at finding novel knowledge. In this paper, we discuss characteristics of the text mining process and propose a design principle for building text mining applications based on two concepts: (1) text mining is an exploratory search task, and (2) text mining is a process for creative knowledge work.

Text mining refers to the process of extracting information from text. There are massive amounts of data available today due to enhanced data collection capabilities, inexpensive high capacity storage, and the proliferation of World Wide Web pages. A substantial portion of this data is in text format. The main goal of text data mining software tools is to help us learn and benefit from this wealth of text data. Humans cannot cope with the overwhelming text data resources. The information in text data needs to be filtered, summarized, analyzed, and refined for human analysts.;A semantic signature is the concept that semantic content in text has characteristic word patterns, such as frequency of words and proximity between words, which can be identified and quantified. A type of quantitative semantic signature was developed by Barnes, Eschen, Para, and Peddada in 2010. The utility and sensitivity of semantic signatures of this type in capturing semantic content in text data was demonstrated by this group via the development of a software package named Semantic Signature Mining Tool (SSMinT). SSMinT is a suite of software tools that assist a data analyst to develop semantic signatures that capture targeted content and then use these semantic signatures to categorize a corpus of text documents with unknown content or to retrieve text documents with the targeted content from a corpus of documents with arbitrary content.;Key features of SSMinT are the expert input from the human analyst and the interaction between the analyst and the software; the tool is designed to assist the analyst and does not work independently. This is a strong feature in the sense that the resulting semantic signatures are tailored by the analyst's expert knowledge of the domain. This was demonstrated by Barnes, Eschen, Para, and Peddada to be a powerful approach to text data mining.;This thesis develops an automated version of the SSMinT software package that requires minimal input from an analyst. This work includes an automated keyword group generation and refinement algorithm, automated generation of candidate semantic signatures, methods to prune irrelevant and redundant relevant semantic signatures from the semantic signature set. Relieving the analyst from the tedious and time consuming task of developing semantic signatures is not the only motivation for an automated tool. The automation is designed to discover semantic signatures in text data without human input, except for the choice of training documents. The advantage of automated semantic signature discovery

A mechanistic model was developed for brand talent demand analysis using text mining techniques in this study. Text mining is a powerful tool for extracting valuable insights from unstructured textual data, and its application in talent demand analysis offers significant potential for strategically informed decision-making in human resource management. The model integrated various technical theories related to text mining, including the Bag of Words model, Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Gibbs sampling. These techniques enable the systematic analysis of textual data to identify patterns and trends in brand talent demand. The basic procedure involved in brand talent demand analysis was constructed using text-mining methodologies. The importance of data collection and preprocessing was emphasized based on web crawlers to gather relevant textual data from online sources. By leveraging web crawlers, the model acquired high-quality datasets essential for accurate analysis. Overall, the proposed mechanistic model provides a framework for brand talent demand analysis and contributes to the advancement of talent management strategies.

With the boom of web and social networking, the amount of generated text data has increased enormously. Much of this data can be considered and modeled as a stream and the volume of such data necessitates the application of automated text classification strategies. Although streaming data classification is not new, considering text data streams for classification purposes has been extensively researched only recently. Before applying any classification method in text data streams, it is imperative that we apply them for existing well-known non-stream text data sets and evaluate their performance. One of the many characteristics of text data that has been pursued for research is its multi-labelity. A single text document may cover multiple class-labels at the same time and hence gives rise to the concept of multi-labelity. From classification perspective, an immediate drawback of such a characteristic is that traditional binary or multi-class classification techniques perform poorly on multi-label text data. In this paper, we extend our previously formulated SISC (Semi-supervised Impurity based Subspace Clustering) [1] approach and its multi-label variation SISC-ML [2]. We call this new algorithm H-SISC (Hierarchical SISC). H-SISC captures the underlying correlation that exists between each pair of class labels in a multi-label environment. Developing a robust multi-label classifier will allow us to apply such a model in classifying streaming text data more effectively. We have experimented with well known text data sets and empirical evaluation on these real world multi-label NASA ASRS (Aviation Safety Reporting System), Reuters and 20 Newsgroups data sets reveals that our proposed approach outperforms other state-of-the-art text classification as well as subspace clustering algorithms.

This paper explores the integration of text mining and data mining techniques, digital library systems, and computational and data grid technologies with the objective of developing an online classification service exemplar. We discuss the current research issues relating to the use of data mining algorithms and toolkits for textual data; the necessary changes within the Cheshire3 Information Framework to accommodate analysis workflows; the outcomes of a demonstrator based on the National Library of Medicine's Medline dataset; and the provision of comparable metrics for evaluation purposes. The prototype has resulted in extremely accurate online classification services and offers a novel method of supporting text mining and data mining within a highly scaled computational environment, integrated seamlessly into the digital library architecture.