Many existing complex space systems have a significant amount of historical maintenance and problem data bases that are stored in unstructured text forms. The problem that we address in this paper is the discovery of recurring anomalies and relationships between problem reports that may indicate larger systemic problems. We will illustrate our techniques on data from discrepancy reports regarding software anomalies in the Space Shuttle. These free text reports are written by a number of different people, thus the emphasis and wording vary considerably. With Mehran Sahami from Stanford University, I'm putting together a book on text mining called "Text Mining: Theory and Applications" to be published by Taylor and Francis.

Missing data is an inevitable aspect of every empirical research. Researchers developed several techniques to handle missing data to avoid information loss and biases. Over the past 50 years, these methods have become more and more efficient and also more complex. Building on previous review studies, this paper aims to analyze what kind of missing data handling methods are used among various scientific disciplines. For the analysis, we used nearly 50.000 scientific articles that were published between 1999 and 2016. JSTOR provided the data in text format. Furthermore, we utilized a text-mining approach to extract the necessary information from our corpus. Our results show that the usage of advanced missing data handling methods such as Multiple Imputation or Full Information Maximum Likelihood estimation is steadily growing in the examination period. Additionally, simpler methods, like listwise and pairwise deletion, are still in widespread use.

Emotional classification (valence) in textual data has proved to be central to human experience analysis and natural language processing (NLP). This study implements a text mining model and algorithm - TM-EV (Text Mining for Emotional Valence Analysis) - that determines the impact of emotional valence (EV) shown by undergraduate students in their feedback (n=665860) during the program (pre- and post-course to determine its relationship with the learning outcome and performance.

Text Mining Market size was USD 4.8 Billion in 2022 and is expected to reach USD 24.77 Billion in 2034, and register a rapid revenue CAGR of 20% during the forecast period.

The Text Analytics Market report segments the industry into By Deployment (On-premise, Cloud), By Application (Risk Management, Fraud Management, Business Intelligence, Social Media Analysis, Customer Care Services), By End-User Industry (BFSI, Healthcare, Energy and Utility, Retail and E-commerce, Other End User Industries), and By Geography (North America, Europe, Asia, and more).

Assess risk in publically traded companies with LSEG's StarMine Text Mining Credit Risk Model (TMCR), scoring over 38,000 companies.

The Text Mining Market size is expected to reach a valuation of USD 28.3 billion in 2033 growing at a CAGR of 20.20% . The Text Mining Market research report classifies market by share, trend, demand, forecast and based on segmentation.

Market Overview The global Text Mining market is estimated to expand at a robust CAGR of XX% over the forecast period of 2025-2033, reaching a value of million in 2033. The growing volume of unstructured text data across industries, such as customer feedback, social media posts, and business documents, is driving the demand for text mining solutions. Businesses are increasingly seeking to extract meaningful insights from this data to improve decision-making, optimize operations, and enhance customer experiences. Key Market Trends and Market Segments Key market drivers include the rise of data science and analytics, the need for competitive advantage, and the increasing adoption of cloud-based text mining solutions. Trends include the integration of artificial intelligence (AI) and machine learning (ML) capabilities for advanced sentiment analysis and predictive modeling. The cloud-based segment is expected to dominate the market due to its cost-effectiveness, scalability, and ease of deployment. By application, data analysis and forecasting dominates the market, with customer relationship management (CRM) emerging as a significant growth segment. Major companies in the market include IBM, Microsoft, SAS Institute, and SAP SE. Regionally, North America currently holds the largest market share, followed by Europe and Asia Pacific. Text Mining Market Report This comprehensive report analyzes the dynamic text mining market, providing insights into its current trends, growth drivers, challenges, and opportunities. The market is anticipated to reach $100 million by 2028, exhibiting a CAGR of 15.2% during the forecast period (2022-2028).

The global text mining system market is experiencing robust growth, driven by the increasing volume of unstructured text data generated across various sectors and the need for efficient data analysis and insights extraction. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching a market value exceeding $15 billion by 2033. This growth is fueled by several key factors. Firstly, the proliferation of big data, including social media content, customer reviews, and scientific publications, necessitates sophisticated tools for analysis. Secondly, advancements in natural language processing (NLP) and machine learning (ML) are significantly enhancing the accuracy and efficiency of text mining systems. Businesses are leveraging these advancements for improved customer relationship management (CRM), market research, risk management, and competitive intelligence gathering. The healthcare and finance sectors are particularly strong adopters, utilizing text mining for clinical trial analysis, fraud detection, and regulatory compliance. However, challenges remain, including data security concerns, the need for skilled professionals to manage and interpret results, and the potential for algorithmic bias in NLP models. These challenges are progressively being addressed through ongoing technological innovations and the development of more robust and ethical AI-driven solutions. The competitive landscape is highly fragmented, with both established technology giants like IBM, Microsoft, and Google, and specialized niche players like Lexalytics and Knime vying for market share. The market is further segmented based on deployment (cloud, on-premises), application (healthcare, finance, marketing), and organization size. Future growth will depend on ongoing research and development in NLP and ML, the development of user-friendly interfaces, and the integration of text mining solutions with other business intelligence tools. The increasing focus on data privacy and regulations will also influence market development, prompting the need for secure and compliant text mining solutions. The overall outlook remains positive, indicating continued and accelerated expansion of the text mining system market in the coming years.

please cite this dataset by : Nicolas Turenne, Ziwei Chen, Guitao Fan, Jianlong Li, Yiwen Li, Siyuan Wang, Jiaqi Zhou (2021) Mining an English-Chinese parallel Corpus of Financial News, BNU HKBU UIC, technical report The dataset comes from Financial Times news website (https://www.ft.com/) news are written in both languages Chinese and English. FTIE.zip contains all documents in a file individually FT-en-zh.rar contains all documents in one file Below is a sample document in the dataset defined by these fields and syntax : id;time;english_title;chinese_title;integer;english_body;chinese_body 1021892;2008-09-10T00:00:00Z;FLAW IN TWIN TOWERS REVEALED;科学家发现纽约双子塔倒塌的根本原因;1;Scientists have discovered the fundamental reason the Twin Towers collapsed on September 11 2001. The steel used in the buildings softened fatally at 500?C – far below its melting point – as a result of a magnetic change in the metal. @ The finding, announced at the BA Festival of Science in Liverpool yesterday, should lead to a new generation of steels capable of retaining strength at much higher temperatures.;科学家发现了纽约世贸双子大厦(Twin Towers)在2001年9月11日倒塌的根本原因。由于磁性变化,大厦使用的钢在500摄氏度——远远低于其熔点——时变软,从而产生致命后果。 @ 这一发现在昨日利物浦举行的BA科学节(BA Festival of Science)上公布。这应会推动能够在更高温度下保持强度的新一代钢铁的问世。 The dataset contains 60,473 bilingual documents. Time range is from 2007 and 2020. This dataset has been used for parallel bilingual news mining in Finance domain. {"references": ["Turenne N et al (2021) Mining an English-Chinese parallel Corpus of nancial News"]} Turenne N et al (2021) Mining an English-Chinese parallel Corpus of nancial News

The text mining market is experiencing robust growth, driven by the increasing volume of unstructured textual data generated across various sectors. The market's expansion is fueled by the rising need for businesses to extract valuable insights from this data for improved decision-making, enhanced customer understanding, and optimized operational efficiency. Key applications include sentiment analysis for brand monitoring, topic modeling for market research, and entity recognition for risk management. Technological advancements, such as the development of more sophisticated natural language processing (NLP) algorithms and machine learning (ML) models, are further propelling market growth. The competitive landscape is marked by a blend of established players like IBM, Microsoft, and SAS Institute, alongside innovative startups offering specialized solutions. While data security and privacy concerns pose challenges, the overall market outlook remains positive, with a projected Compound Annual Growth Rate (CAGR) of approximately 15% over the forecast period (2025-2033). This growth will likely be distributed across various segments, with significant contributions from industries like finance, healthcare, and marketing. The substantial growth in the text mining market is anticipated to continue, driven by increasing adoption across various sectors including customer relationship management (CRM), market intelligence, and regulatory compliance. The rising availability of big data and advancements in cloud computing infrastructure enable efficient processing and analysis of vast text datasets. Furthermore, the increasing demand for real-time insights from textual data is stimulating the development of advanced analytics tools and platforms, facilitating faster processing and improved accuracy in text mining applications. Despite potential restraints such as the need for high-quality data and skilled professionals, the market is expected to remain lucrative, with significant opportunities for businesses leveraging text mining for competitive advantage. The ongoing development of hybrid solutions integrating on-premise and cloud-based deployments will likely further drive market penetration and adoption. Regional variations in growth rates will be influenced by factors such as technological adoption rates, data regulations, and industry-specific needs.

Global Text Mining market size is expected to reach $16.81 billion by 2029 at 18.7%, segmented as by on-premise, local deployment, customization options, data security and privacy control

Text Mining Market Outlook

As of 2023, the global text mining market size stands at approximately USD 4.1 billion and is projected to reach USD 13.4 billion by 2032, reflecting a robust Compound Annual Growth Rate (CAGR) of 14.1% throughout the forecast period. This substantial growth is driven by the increasing demand for text analytics solutions capable of handling vast volumes of unstructured data generated across various sectors daily. The surge in big data analytics, the rise of artificial intelligence, and the adoption across diverse industries are key factors propelling the expansion of the text mining market.

The evolution of data analytics technologies has fundamentally shifted the business landscape, creating a fertile ground for the text mining market's growth. Organizations are increasingly recognizing the value of transforming unstructured text data into actionable insights. This transformation facilitates informed decision-making, enhances customer engagement, and improves organizational efficiency. Additionally, the rise of e-commerce and social media platforms generates colossal textual data, necessitating efficient text mining solutions to gain competitive advantages. With businesses aiming to harness these insights for strategic initiatives, the demand for sophisticated text mining tools continues to escalate.

Moreover, the integration of artificial intelligence and machine learning technologies into text mining solutions has significantly contributed to the market's growth. These technologies enhance text mining capabilities by improving data processing speed, accuracy, and contextual understanding. AI-driven text mining solutions can decipher intricate patterns and predict trends, making them indispensable for sectors like finance, healthcare, and retail. Additionally, the continuous innovation in Natural Language Processing (NLP) technologies boosts the efficiency of text mining applications, further fueling market expansion.

The worldwide digital transformation initiatives across industries are another notable growth factor for the text mining market. Enterprises are increasingly digitizing their operations, leading to a surge in digital documents, emails, and online customer interactions. Text mining technologies are critical in analyzing this wealth of data, providing businesses with deep insights into consumer behavior, market trends, and operational bottlenecks. This transformation is particularly pronounced in sectors such as BFSI and healthcare, where the need for precise data analysis to enhance customer experience and optimize healthcare delivery is paramount.

Regionally, the growth dynamics of the text mining market are diverse, with North America holding a significant share due to the early adoption of technology and the presence of major market players. However, the Asia Pacific region is expected to exhibit the highest CAGR during the forecast period, driven by the rapid adoption of digital technologies, increasing investments in AI and machine learning, and the expansion of the IT and telecommunications sector. Europe, with its strong regulatory framework and emphasis on data protection, also presents a lucrative market for text mining solutions, primarily in the BFSI and healthcare sectors.

Component Analysis

The text mining market is broadly segmented into software and services. The software segment comprises advanced text analytics platforms and tools, which are at the core of any text mining application. These tools are essential for processing, analyzing, and extracting meaningful insights from unstructured data. Companies are increasingly investing in software solutions that offer robust analytics capabilities, real-time data processing, and integration with existing IT infrastructure. The demand for customizable and scalable software solutions is rising, as enterprises are looking for tools that can adapt to their specific needs and handle varying volumes of data efficiently.

On the services front, the market includes consulting, integration, and maintenance services that support the deployment and optimization of text mining solutions. As businesses embark on digital transformation journeys, they often require expert guidance to navigate the complexities of implementing text mining technologies. Service providers offer end-to-end support, from strategic consulting to the actual integration of text mining tools within existing systems. This segment is witnessing growth as more organizations seek to leverage external expertise to maximize the value of their text mining investments and ensure seamless operation.<

The global text mining system market is anticipated to experience substantial growth, with a market size of XXX million in 2025 and projected to reach XXX million by 2033, expanding at a CAGR of XX% during the forecast period of 2025-2033. Key drivers of this growth include the increasing volume of unstructured data, the need for businesses to extract insights from text data, and the advancements in natural language processing (NLP) technologies. The market is segmented by type (text classification tool, text clustering tool, named entity recognition tool, keyword extraction tool, sentiment analysis tools, topic modeling tools, text generation tools, text analysis platform) and application (intelligence analysis, public opinion monitoring, financial sector, medical insurance, marketing, education industry, human resource management). North America is the dominant region, followed by Europe and Asia-Pacific. Major players in the market include IBM, Microsoft, SAS, Google, Amazon, Oracle, SAP, Lexalytics, Altair Engineering, Knime, Aylien, MonkeyLearn, Basis Technology, Linguamatics, Shenzhen Tianyuan DIC Information Technology, Qualtrics, Mozenda, Semantic Web Company, BenchSci, Algolia, SPOTTER, Rossum, SciBite, KapCode, Brandwatch, Apache Lucene, and Derlte.

The global text analysis software market, valued at $1789 million in 2025, is poised for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 5.8% from 2025 to 2033. This expansion is driven by several key factors. The increasing volume of unstructured text data generated across various industries necessitates efficient tools for data analysis and insights extraction. Businesses are increasingly adopting text analysis solutions to improve customer service through sentiment analysis, enhance marketing campaigns by understanding customer preferences, and streamline operational efficiency through automated document processing. Furthermore, the rising adoption of cloud-based solutions offers scalability, cost-effectiveness, and accessibility, fueling market growth. The market is segmented by application (large enterprises and SMEs) and type (on-premises and cloud-based). Cloud-based solutions are witnessing faster adoption due to their inherent advantages. Key players such as Microsoft, IBM, Google, and others are driving innovation through advanced algorithms and feature-rich platforms, fostering competition and accelerating market development. The North American market currently holds a significant share, benefiting from early adoption and a robust technological infrastructure. However, growth in regions like Asia Pacific is expected to accelerate, driven by increasing digitalization and data generation in developing economies. While the market enjoys significant growth potential, certain restraints exist. The complexity of implementing and integrating text analysis software, along with concerns regarding data privacy and security, can hinder adoption. The need for specialized skills to effectively utilize these tools and interpret the resulting insights also presents a challenge. However, ongoing advancements in natural language processing (NLP) and machine learning (ML) technologies are continuously improving the usability and accuracy of text analysis solutions, mitigating these challenges and fostering wider adoption across diverse industries and geographies. The future of the text analysis software market appears promising, with continued growth fueled by technological innovation and the increasing importance of data-driven decision-making.

Dataset

This dataset was created by Zoumana

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Charles Liu

Contents

A BitTorrent file to download data with the title '[Coursera ] Text Mining and Analytics'

Many existing complex space systems have a significant amount of historical maintenance and problem data bases that are stored in unstructured text forms. The problem that we address in this paper is the discovery of recurring anomalies and relationships between problem reports that may indicate larger systemic problems. We will illustrate our techniques on data from discrepancy reports regarding software anomalies in the Space Shuttle. These free text reports are written by a number of different people, thus the emphasis and wording vary considerably. With Mehran Sahami from Stanford University, I'm putting together a book on text mining called "Text Mining: Theory and Applications" to be published by Taylor and Francis.