Text Document Clustering Approach by Improved Sine Cosine Algorithm

Unstructured text data is very important in many applications because it reflects the thought of the people who create this data. However, it is difficult to realize the latent information as it was hidden on the unstructured text data. This paper proposes a latent learning method to construct the lexical structure to constitute the relations between the latent meaning and words. The established lexical structure derived the useful information from unstructured text data and this information and this information can be used for various application. This paper describes how to predict a rating from user-written reviews which is one of unstructured text data. And it also provides visualization information of the semantic lexical structures as the result of analysis. As a result, the proposed method easily quantifies the semantic relations of words and it shows good performance on prediction of ratings from unstructured text data. The proposed method can contribute to analyzing the unstructured text data in various perspectives on latent meaning of words.

This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on structured tabular data with models trained only on unstructured clinical text data, and on mixed data. The structured data was used to train fourteen classical Machine Learning models including advanced ensemble trees, neural networks and k-nearest neighbors. The unstructured data was used to fine-tune a pre-trained Bio Clinical BERT Transformer Deep Learning model. The structured and unstructured data were then merged into a tabular dataset after vectorization of the clinical text and a dimensional reduction through Latent Dirichlet Allocation. The study used the free and publicly available Medical Information Mart for Intensive Care (MIMIC) III database, on the open AutoML Library AutoGluon. Performance is evaluated with respect to two types of random classifiers, used as baselines. The best model from structured data demonstrates high performance (ROC AUC = 0.944, PRC AUC = 0.655) with limited interpretability, where the most important predictors of prolonged LOS are the level of blood urea nitrogen and of platelets. The Transformer model displays a good but lower performance (ROC AUC = 0.842, PRC AUC = 0.375) with a richer array of interpretability by providing more specific in-hospital factors including procedures, conditions, and medical history. The best model trained on mixed data satisfies both a high level of performance (ROC AUC = 0.963, PRC AUC = 0.746) and a much larger scope in interpretability including pathologies of the intestine, the colon, and the blood; infectious diseases, respiratory problems, procedures involving sedation and intubation, and vascular surgery. Our results outperform most of the state-of-the-art models in LOS prediction both in terms of performance and of interpretability. Data fusion between structured and unstructured text data may significantly improve performance and interpretability.

The evaluation of the learning process is an effective way to realize personalized online learning. Real-time evaluation of learners’ cognitive level during online learning helps to monitor learners’ cognitive state and adjust learning strategies to improve the quality of online learning. However, most of the existing cognitive level evaluation methods use manual coding or traditional machine learning methods, which are time-consuming and laborious. They cannot fully mine the implicit cognitive semantic information in unstructured text data, making the cognitive level evaluation inefficient. Therefore, this study proposed the bidirectional gated recurrent convolutional neural network combined with an attention mechanism (AM-BiGRU-CNN) deep neural network cognitive level evaluation method, and based on Bloom’s taxonomy of cognition objectives, taking the unstructured interactive text data released by 9167 learners in the massive open online course (MOOC) forum as an empirical study to support the method. The study found that the AM-BiGRU-CNN method has the best evaluation effect, with the overall accuracy of the evaluation of the six cognitive levels reaching 84.21%, of which the F1-Score at the creating level is 91.77%. The experimental results show that the deep neural network method can effectively identify the cognitive features implicit in the text and can be better applied to the automatic evaluation of the cognitive level of online learners. This study provides a technical reference for the evaluation of the cognitive level of the students in the online learning environment, and automatic evaluation in the realization of personalized learning strategies, teaching intervention, and resources recommended have higher application value.

Advancement in digital technology has led to an increase in the text data exponentially. A field called ‘text mining’ turns the massive amount of text data into high quality or actionable knowledge so that it can help in making the optimal decision, reduces the time and human effort to analyze it. We can perform several tasks on text data including part of speech tagging, parsing text, extract the relevant information, classify the text data, clustering, etc. Text representation is a necessary step to do all these tasks and its effect, especially on the end results of text classification or clustering is highly considerable. The aim of this paper is to highlight the prerequisite procedures to represent text data, different text representation methods, the role of dimensionality reduction, different proximity measures and their evaluation methods to assess results of text clustering or classification.

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.

Sentiment analysis is becoming increasing important with the rise in the amount of content on social media. However, sentiment analysis remains challenging for under-resourced languages such as Kreol Morisien (KM), the native language of Mauritius. In fact, it has been observed that in Mauritius, social media comments often consist of more than one language among English, French and Kreol Morisien. In this paper, we first create an annotated dataset of 1300 sentences and then outline a framework through which sentiment analysis can be performed on social media comments. We propose a KM sentiment analyzer using two algorithms namely Support Vector Machine (SVM) and Multinomial Naïve Bayes (MNB). Our results show that SVM outperforms MNB for sentiment analysis in Kreol Morisien, achieving an accuracy of 66.15% after pre-processing techniques stopwords removal and spell checking are applied. This paper highlights the need to develop further tools in order to enable natural language processing of Kreol Morisien.

This paper presents a scalable machine learning pipeline for extracting actionable, product-related insights from user-generated social media comments. Leveraging sentence embeddings from SBERT and unsupervised clustering (k-Means and agglomerative), the approach structures informal and noisy comments from Instagram and YouTube into topic groups intended to support thematic analysis. A case study on feedback regarding BMW vehicles, comprising more than 26,000 comments, illustrates how the pipeline can reveal recurring user concerns, such as design critiques, usability issues, and technology-related expectations, even in short and unstructured social media comments. The proposed pipeline operates without labeled data or manual annotation, enabling scalable application and transferability across product categories and industries. By transforming large-scale, unstructured consumer feedback into interpretable themes, the pipeline provides product teams with an efficient and structured basis for data-driven product development and improvement.

Text clustering serves as a fundamental technique for organizing and interpreting unstructured textual data, particularly in contexts where manual annotation is prohibitively costly. With the rapid advancement of Large Language Models (LLMs) and their demonstrated effectiveness across a broad spectrum of NLP tasks, an emerging body of research has begun to explore their potential in the domain of text clustering. However, existing LLM-based approaches still rely on fine-tuned embedding models and sophisticated similarity metrics, rendering them computationally intensive and necessitating domain-specific adaptation. To address these limitations, we propose a novel framework that reframes text clustering as a classification task by harnessing the in-context learning capabilities of LLMs. Our framework eliminates the need for fine-tuning embedding models or intricate clustering algorithms. It comprises two key steps: first, the LLM is prompted to generate a set of candidate labels based on the dataset and then merges semantically similar labels; second, it assigns the most appropriate label to each text sample. By leveraging the advanced natural language understanding and generalization capabilities of LLMs, the proposed approach enables effective clustering with minimal human intervention. Experimental results on diverse datasets demonstrate that our framework achieves comparable or superior performance to state-of-the-art embedding-based clustering techniques, while significantly reducing computational complexity and resource requirements. These findings underscore the transformative potential of LLMs in simplifying and enhancing text clustering tasks. We make our code available to the public for utilization at https://github.com/ECNU-Text-Computing/Text-Clustering-via-LLM.

Text clustering is used to extract specific information from textual data and even categorizes text based on topic and sentiment. Due to inherent overlapping in textual documents, overlapping clustering algorithms have become a suitable approach for text analysing. However, state-of-the-art algorithms are not fast enough to analyse a large volume of textual data within tolerable time limits. In this research, we propose our text clustering algorithm, FOCT, which is a fast overlapping extension of SOM, one of the best algorithms for clustering textual data. We apply some heuristics to extract special characteristics presented in textual data and establish a very fast overlapping clustering algorithm. We use fast methods to represent the vectors of documents, compute the similarity of documents and neurons and update the weights of neurons. In our algorithm, each document can belong to one or more neurons and this is in line with what many documents have in their essence. We analyse the efficiency of the proposed algorithm over k-means, OKM, SOM and OSOM clustering approaches and experimentally demonstrate that it runs 12 to 690 times faster, and the overlap size of FOCT clusters is closer to the overlap size of the original data. The quality of clusters is also measured by four different internal and external evaluation criteria where FOCT clusters represent up to 64% better quality.

With the meteoric development of the big data, internet of things (IoT), the very first question is about how to scout the massive volume of data from heterogeneous data sources. The modern tools and techniques commonly used in successful transmuting of well-structured data into business intelligence (BI) clearly don't work when started on unlabeled data. It is needed to have effective and efficient mapping processes to transform unstructured text data to structured text data with assigned categories to make rapid decisions. With the support of modern automatic text mining tools, the decision-making process can now be done efficiently and cost-effectively. The deep learning concept has attained state-of-the-art results. The mapping operation of unstructured text data to classified structured text data will symbolize unstructured data as renewable assets that are well arranged, useful, and meaningful to serve organizational operations. This chapter presented a comparison of conventional and deep learning methods for unstructured text data classification and its challenges.

Network text analysis: A two-way classification approach

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

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2015 SIAM International Conference on Data Mining (SDM)DIAS: A Disassemble-Assemble Framework for Highly Sparse Text ClusteringHongfu Liu, Junjie Wu, Dacheng Tao, Yuchao Zhang, and Yun FuHongfu Liu, Junjie Wu, Dacheng Tao, Yuchao Zhang, and Yun Fupp.766 - 774Chapter DOI:https://doi.org/10.1137/1.9781611974010.86PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Upon extensive studies, text clustering remains a critical challenge in data mining community. Even by various techniques proposed to overcome some of these challenges, there still exist problems when dealing with weakly related or even noisy features. In response to this, we propose a DIssemble-ASsemble (DIAS) framework for text clustering. DIAS employs simple random feature sampling to disassemble high-dimensional text data and gains diverse structural knowledge. This also does good to avoiding the bulk of noisy features. Then the multi-view knowledge is assembled by weighted Information-theoretic Consensus Clustering (ICC) in order to gain a high-quality consensus partitioning. Extensive experiments on eight real-world text data sets demonstrate the advantages of DIAS over other widely used methods. In particular, DIAS shows strengths in learning from very weak basic partitionings. In addition, it is the natural suitability to distributed computing that makes DIAS become a promising candidate for big text clustering. Previous chapter Next chapter RelatedDetails Published:2015eISBN:978-1-61197-401-0 https://doi.org/10.1137/1.9781611974010Book Series Name:ProceedingsBook Code:PRDT145Book Pages:1-976

Background: The operation state evaluation and fault location of the transformer is one of the technical bottlenecks restricting the safe power grid operation. Methods: A hybrid intelligent method based on the Improved Sine Cosine Algorithm and BP neural network (ISCA-BP) is developed to improve the accuracy of transformer fault diagnosis. First, the cloud model is introduced into the Sine Cosine Algorithm (SCA) to determine the conversion parameter of each individual to balance the global search and local exploitation capabilities. After that, six popular benchmark functions are used to evaluate the effectiveness of the proposed algorithm. Finally, based on the dissolved gas analysis technology, the improved SCA algorithm is employed to find the optimal weight and threshold parameters of the BP neural network, and the transformer fault classification model is established. Results: Simulation results indicate that the improved SCA algorithm exhibits strong competitiveness. Furthermore, compared with the BP neural network optimized by the Sine Cosine Algorithm (SCA-BP) and BP neural network, the ISCA-BP method can significantly improve the diagnostic accuracy of transformer faults. Conclusion: The proposed intelligent method can provide a valuable reference idea for transformer fault classification.

Text clustering is one of the fundamental tasks in natural language processing which involves grouping similar documents together based on their content thus enabling efficient organization and analysis of textual data. In this paper we provide a comprehensive survey of text clustering techniques, its applications, challenges, and future directions. We begin by discussing the fundamentals of text clustering, including key concepts such as similarity measures, text feature representations, and clustering algorithms. We also explore popular text clustering algorithms such as K-means, hierarchical clustering, density- based clustering, spectral clustering, affinity propagation, and Latent Dirichlet Allocation (LDA) popularly used for topic modelling. For every algorithm we discuss its methodology, strengths, limitations, and parameter tuning considerations. We also dive deep into the real-world applications of text clustering across diverse domains, including document organization, information retrieval, text summarization, sentiment analysis, and recommendation systems and highlight their effectiveness with case studies and examples. We also identify several challenges and open research questions in text clustering, such as scalability, handling high-dimensional data, incorporating domain specific knowledge in clustering, evaluation metrics, and integration with other NLP tasks such NER, classification, etc. Finally, we propose potential future directions for research to address these challenges in order to advance the field of text clustering. In conclusion, text clustering continues to be an interesting area of research with immense potential for applications in various domains which helps drive innovation in natural language processing.