Enhancing Text Analysis via Dimensionality Reduction

Embedding the original high dimensional data in a low dimensional space helps to overcome the curse of dimensionality and removes noise. The aim of this work is to evaluate the performance of three different linear dimensionality reduction techniques (DR) techniques namely principal component analysis (PCA), multi dimensional scaling (MDS) and linear discriminant analysis (LDA) on classification of cardiac arrhythmias using probabilistic neural network classifier (PNN). The design phase of classification model comprises of the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through daubechies wavelet transform, dimensionality reduction through linear DR techniques specified, and arrhythmia classification using PNN. Linear dimensionality reduction techniques have simple geometric representations and simple computational properties. Entire MIT-BIH arrhythmia database is used for experimentation. The experimental results demonstrates that combination of PNN classifier (spread parameter, σ = 0.08) and PCA DR technique exhibits highest sensitivity and F score of 78.84% and 78.82% respectively with a minimum of 8 dimensions.

Data for automated prediction comes from many sources. In this chapter we expand our horizons to encompass both text and structured numerical data. Initially, we review the ideal data representations for prediction using either numerical or text data. We consider numerous sources of data including databases, the web, and hybrid forms of text and numerical data. Prototypical examples of blended numerical and text data are given. Using the web as a source of data for prediction is examined. Among the examples presented of web-sourced data are downloaded scientific publications formatted in XML, stock price data and related newswire headlines. Sentiment and opinion analysis are considered with examples from online product reviews and Twitter data. Predictive mining of electronic medical records mining is presented as an example of mixed-data mining.

CT perfusion imaging (CTP) plays an important role in decision making for the treatment of acute ischemic stroke with large vessel occlusion. Since the CT perfusion scan time is approximately one minute, the patient is exposed to a non-negligible dose of ionizing radiation. However, further dose reduction increases the level of noise in the data and the resulting perfusion maps. We present a method for reducing noise in perfusion data based on dimension reduction of time attenuation curves. For dimension reduction, we use either the fit of the first five terms of the trigonometric polynomial or the first five terms of the SVD decomposition of the time attenuation profiles. CTP data from four patients with large vessel occlusion and three control subjects were studied. To compare the noise level in the perfusion maps, we use the wavelet estimation of the noise standard deviation implemented in the scikit-image package. We show that both methods significantly reduce noise in the data while preserving important information about the perfusion deficits. These methods can be used to further reduce the dose in CT perfusion protocols or in perfusion studies using C-arm CT, which are burdened by high noise levels.

Machine Learning is a predominantly developing field in the current scenario due to spontaneous growth in size of data. Dimensionality Reduction is significant step used in machine learning. The Imprecation of dimensionality refers to the difficulty that comes up when working with high dimensional data. The Proposed work developed to reduce the risk of high dimensional data representation in the form of low dimensional data representation. This paper presents the dimensionality reduction practices such as Principal Component Analysis (PCA), Kernel PCA and Locally Linear Embedded (LLE).

The Growing Curvilinear Component Analysis (GCCA) neural network

The bag of words (BOW) represents a corpus in a matrix whose elements are the frequency of words. However, each row in the matrix is a very high-dimensional sparse vector. Dimension reduction (DR) is a popular method to address sparsity and high-dimensionality issues. Among different strategies to develop DR method, Unsupervised Feature Transformation (UFT) is a popular strategy to map all words on a new basis to represent BOW. The recent increase of text data and its challenges imply that DR area still needs new perspectives. Although a wide range of methods based on the UFT strategy has been developed, the fuzzy approach has not been considered for DR based on this strategy. This research investigates the application of fuzzy clustering as a DR method based on the UFT strategy to collapse BOW matrix to provide a lower-dimensional representation of documents instead of the words in a corpus. The quantitative evaluation shows that fuzzy clustering produces superior performance and features to Principal Components Analysis (PCA) and Singular Value Decomposition (SVD), two popular DR methods based on the UFT strategy.

Stock markets are at the heart of market economies, and stock market prediction is a hot topic in research. There are many methods of prediction, and they often use different types of data. The two main types are technical and fundamental data. This paper explores company-specific prediction through predicting the direction of the Apple stock. Three neural networks that use different input data are compared. The Tech model uses technical data, the Comp model uses numerical fundamental data with an emphasis on company data, and the Text model uses textual news data. Several metrics, including ones useful for preventing bias when dealing with imbalanced data, were used to compare the models. The Comp and Text models showed prediction bias, and two more models, W-Comp and W-Text, were trained to attempt to mitigate that bias. The Comp model performed the best out of the original models, and W-Comp also showed good performance. W-Text performed the best out of all the models. This suggests that numerical fundamental data is useful in predicting the market, and textual data also has potential. Future research could be used to improve the performance of all the models and more thoroughly compare the types of data used.

PurposeThis study aimed to present the methodology of the text data analysis to establish marketing strategies for fintech companies in a practical way. Specifically, the methodology was presented to convert customers' review data, which consisted of the text data (unstructured data), to the numerical data (structured data) by using a text mining algorithm “Global Vectors for Word Representation,” abbreviated as “GloVe”; additionally, the authors presented the methodology to deploy the numerical data for marketing strategies with eliminate-reduce-raise-create (ERRC) value factor analytics.Design/methodology/approachFirst, the authors defined the background, features and contents of fintech services based on a review of related literature review. Additionally, they examined business strategies, the importance of social media for fintech services and fintech technology trends based on the literature review. Next, they analyzed the similarity between fintech-related keywords, which represent the trends in fintech services, and the text data related to fintech corporations and their services posted on Facebook and Twitter, which are two of the most popular social media globally, during the period 2017–2019. The similarity was then quantified and categorized in terms of the representative global fintech companies and the status of each fintech service sector. Furthermore, the similarity was visualized, and value elements were rebuilt using ERRC strategy analytics.FindingsThis study is meaningful in that it quantifies the degree of similarity between customers' responses, experiences and expectations regarding the rapidly growing global fintech firms' services and trends in fintech services.Originality/valueThis study suggests a practical way to apply in business by providing a method for transforming unstructured text data into structured numerical data it is measurable. It is expected that this study can be used as the basis for exploring sustainable development strategies for the fintech industry.

Online social networks (OSNs) have reduced global boundaries, with Twitter enabling perspective sharing. Bot profile‐propagated false information misuse raises serious concerns. Considering this issue, we present our research on classifying Twitter accounts as “human” or “bot” using deep neural networks and transfer learning. Our proposed approach, TL‐PBot, stands for bot profile detection using transfer learning. The TL‐PBot framework utilizes Twitter account metadata such as follower count. Our TL‐PBot also incorporates text data from the Twitter description field as a feature. Word representation of the text data is achieved using Global Vectors (GloVe), a pre‐trained model. By employing user profile‐based features, we significantly reduce the overhead of feature engineering. The hybrid nature of the model enables it to effectively handle mixed‐type features, including text, binary, and numerical data. We design the network using long‐short‐term memory (LSTM) units. DNN model layers were trained, and the weights of the pre‐trained model layers were frozen to apply the transfer learning, resulting in reduced training time and improved bot profile detection accuracy. The performance of the proposed TL‐PBot is evaluated using publicly available datasets. The proposed approach is trained and tested on the same datasets and further evaluated on the validation datasets that were not used in the training phase, which is also a novelty in our approach. Comparative analysis with state‐of‐the‐art approaches demonstrates that the TL‐PBot approach achieves a higher accuracy of 98.07%, while excelling in precision of 99%, recall of 98%, f measure of 98.32%, and AUC of 0.99. Employing the transfer learning strategy resulted in an accelerated detection rate of 5.04 milliseconds, attesting to the effectiveness of this approach in enhancing computational efficiency.

Natural Language Processing (NLP) has seen a surge in recent years, especially with the introduction of transformer architectures, relying on the now famous self-attention mechanism. Especially, with the rise of Large Language Models (LLM), propelled by the appearance of ChatGPT in 2022, a new hope of extracting relevant information from text has emerged. In the meantime, natural language data have not often been used in risk, resilience, and reliability tasks. However, text data containing reliability-related information, that can be used to monitor health information regarding complex systems, are available in several and diverse shapes. Indeed, text data can either contain theoretical expert knowledge (technical reports, documentation, Failure Modes and Effects Analysis (FMEA)), or in-practice expert knowledge (incident reports, maintenance work orders), or in-practice non-expert knowledge (customer feedback, news articles). Critical infrastructures, such as nuclear powerplants, railway networks, or electrical power grids, are complex systems for which any failure would induce severe consequences affecting many people. Such systems have the advantage of serving many users, thus having many possible text sources from which technical information and past incident data can be mined for anticipating future failures and generating responses to catastrophic scenarios. The goal of this work is to develop methods and apply state-of-the-art NLP techniques to text data relating to critical infrastructures and failures, to (1) mine information from unstructured language data, and (2) structure the extracted information. Preliminary experiments were conducted on customer review data and incident reports, and show promising performance for failure detection from text data with transformers, as well as incident-related information extraction using LLMs.

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.

Most of the time, you deal with text, numeric, and date data types, and they are not terribly difficult to handle. These fields are all variants of either text or numeric data. However, from time to time you have to deal with special kinds of data, such as stored images, sound files, and other binary objects (sequences of un-interpreted bytes). These are generally referred to in the database as BLOB fields (“Binary Large OBject”).KeywordsAccess DatabaseFile TypeSound FileJPEG FileIrregular DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Modern manufacturing paradigms have incorporated Prognostics and Health Management (PHM) to implement data-driven methods for fault detection, failure prediction, and assessment of system health. The maintenance operation has similarly benefitted from these advancements, and predictive maintenance is now being used across the industry. Despite these developments, most of the approaches in maintenance rely on numerical data from sensors and field devices for any sort of analysis. Text data from Maintenance Work Orders (MWOs) contain some of the most crucial information pertaining to the functioning of systems and components, but are still regarded as ‘black holes’, i.e., they store valuable data without being used in decision-making. The analysis of this data can help save time and costs in maintenance. While Natural Language Processing (NLP) methods have been very successful in understanding and examining text data from non-technical sources, progress in the analysis of technical text data has been limited. Non-technical text data are usually structured and consist of standardized vocabularies allowing the use of out-of-the-box language processing methods in their analysis. On the other hand, records from MWOs are often semi-structured or unstructured; and consist of complicated terminologies, technical jargon, and industry-specific abbreviations. Deploying traditional NLP to such data can result in an imprecise and flawed analysis which can be very costly. Owing to these challenges, we propose a Technical Language Processing (TLP) framework for PHM. To illustrate its capabilities, we use text data from MWOs of aircraft to address two scenarios. First, we predict corrective actions for new maintenance problems by comparing them with existing problems using syntactic and semantic textual similarity matching and evaluate the results with cosine similarity scores. In the second scenario, we identify and extract the most dominant topics and salient terms from the data using Latent Dirichlet Allocation (LDA). Using the results, we are able to successfully link maintenance problems to standardized maintenance codes used in the aviation industry.

The text data contain rich semantic information. This paper proposed a semantics based on method of text dimension reduction based on ontology. This method utilized Dictionary Library WordNet to discovery semantic relationships in the feature graph of text data and constructed text feature ontologies for each text dataset, including concepts, semantic relations, attributes. Then for text data dimension reduction, we set hierarchy operations on text feature ontologies were given. Finally, an example is demonstrated to show the efficiency and accuracy of the proposed method. KEYWORD: ontology; hierarchy calculation; dimensionality reduction International Conference on Industrial Technology and Management Science (ITMS 2015) © 2015. The authors Published by Atlantis Press 897 Definition 2.3: Set ' E without consideration into the type of the relationship between the concepts. ' ' 1 2 ' { , ,...} E e e  (3) ' ' e E   , ' ( . _1, . _ 2) e e c e c  . Definition 2.4: Set E without consideration into the relationship weight between the concepts. 1 2 { , ,...} E e e  (4) e E   , ( . _1, . _ 2, . _ ) e e c e c e asso Type  . Definition 2.5: text feature ontology O.

In recent years, the data tends to be very large and complex and it becomes very difficult and tedious to work with large datasets containing huge number of features. That's where Dimensionality Reduction comes into play. Dimensionality Reduction is a pre-processing step in various fields such as machine learning, data mining, statistics etc. and is effective in removing irrelevant and highly redundant data. In this paper, the author's performed a vast literature survey and aims to provide an adequate application based understanding of various dimensionality reduction techniques and to work as a guide to choose right approach of Dimensionality Reduction for better performance in different applications. Here, the authors have also performed detailed experiments on two different datasets for comparative analysis between various linear and non-linear dimensionality reduction techniques to figure out the effectiveness of the techniques used. PCA, a linear dimensionality reduction technique, outperformed all other techniques used in the experiments. In fact, almost all the linear dimensionality reduction techniques outperformed the non-linear techniques on both datasets by a huge error percentage margin.