Data Classification Market size was valued at USD 1664.66 Million in 2024 and is projected to reach USD 9486.25 Million by 2032, growing at a CAGR of 24.3% during the forecast period 2026-2032.

Global Data Classification Market Drivers

The market drivers for the Data Classification Market can be influenced by various factors. These may include:

Increasing Data Volume: In order to maintain data security, compliance, and effective use, there is an increasing requirement to manage and classify the data produced by enterprises in an exponentially growing amount. Regulatory Compliance: Organizations must categorize their data based on the sensitivity levels required by strict data protection laws like the GDPR, CCPA, HIPAA, and others. Adoption of data classification solutions is driven by compliance requirements, which guarantee adherence to regulatory standards and prevent heavy penalties.

Data Security Concerns: Organizations are concentrating on strengthening their data security procedures due to the increase in cyber threats and data breaches. Classifying data makes it easier to find sensitive information and implement the right security measures to keep it safe from theft or unwanted access.

Growing Adoption of Cloud Services: As cloud computing services become more widely used, strong data classification techniques are required to guarantee data security and compliance, particularly when data is transferred between different cloud environments and storage locations. Increasing Awareness of Data Privacy: The need for solutions that allow for better management and protection of sensitive data through classification and encryption is being driven by heightened awareness of data privacy issues among consumers and enterprises. Combining Data Loss Prevention (DLP) Systems: Through the identification, monitoring, and prevention of sensitive information leakage or unlawful transfer, data categorization integrated with DLP systems improves data protection capabilities. Emergence of AI and Machine Learning Technologies: By incorporating these technologies into data categorization systems, data may be identified and classified more automatically and accurately, saving labor and increasing efficiency. Demand for Data Governance and Lifecycle Management: In order to maintain data quality, integrity, and compliance throughout its lifecycle, organizations are realizing more and more how important it is to have effective data governance and lifecycle management. A key component of putting into practice efficient data governance procedures is data classification.

Data Classification Market Outlook

The global data classification market size was valued at approximately USD 700 million in 2023, with a projected CAGR of 24% over the forecast period, leading to an estimated market size of USD 4.5 billion by 2032. The growth of this market is primarily driven by the increasing need for businesses to manage and protect sensitive information, amid a rapidly expanding volume of data and ever-evolving regulatory requirements. The advent of new data protection regulations, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), has compelled organizations across industries to invest in robust data classification solutions. These solutions enable businesses to effectively categorize, label, and protect their data, ensuring compliance with stringent data security standards.

One significant growth factor of the data classification market is the digital transformation wave sweeping across industries. As organizations increasingly adopt digital technologies to drive operational efficiencies and enhance customer experiences, the amount of data generated and processed has grown exponentially. This surge in data volume necessitates efficient data management and protection solutions, fuelling demand for data classification systems. Moreover, as businesses strive to derive actionable insights from their data assets, data classification becomes crucial in organizing and prioritizing information for analysis, thereby enhancing decision-making processes and business outcomes.

Cybersecurity threats and data breaches have become pervasive, posing a significant risk to organizations' data assets. Consequently, there is an increasing focus on data security, which is driving the demand for data classification solutions. These solutions offer organizations the ability to identify and classify sensitive information, thereby enabling the implementation of effective security controls. In addition, the rise of remote working and cloud computing has broadened the attack surface, making it imperative for organizations to have robust data classification strategies in place to safeguard their data in a decentralized environment. The need for enhanced data security measures is expected to continue driving the growth of the data classification market throughout the forecast period.

The proliferation of artificial intelligence (AI) and machine learning (ML) technologies presents another compelling growth factor for the data classification market. AI and ML technologies augment traditional data classification methods by automating the process and offering improved accuracy and efficiency in data categorization. These advanced technologies enable organizations to handle large volumes of data more effectively and make data-driven decisions with greater precision. As organizations increasingly recognize the potential of AI and ML in enhancing data classification capabilities, the adoption of AI-powered data classification solutions is anticipated to witness significant growth, further propelling the market.

Regionally, North America is anticipated to hold the largest share of the data classification market, driven by the early adoption of advanced technologies and stringent data protection regulations. The presence of major technology players and a highly developed IT infrastructure further support the growth of the market in the region. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, attributed to the increasing digitalization initiatives, growing awareness about data security, and a burgeoning number of small and medium enterprises (SMEs) seeking cost-effective data classification solutions. Europe is also witnessing a notable demand for data classification systems, propelled by compliance requirements and a growing emphasis on data privacy.

Component Analysis

The data classification market by component is segmented into software and services. The software segment is the largest and fastest-growing component of the market, owing to the increasing demand for sophisticated data classification tools that offer advanced features such as automation, real-time data analytics, and integration with other enterprise applications. Organizations are increasingly investing in software solutions to enhance their data management capabilities and ensure compliance with data protection regulations. The software segment's growth is further supported by the rising adoption of cloud-based solutions, which offer scalability, flexibility, and cost-effectiveness, making them an attractive option for businesses of al

This policy document, approved by the City's Data Governance Committee in December 2019, outlines the organization's data classification levels and process for determining the level of risk posed by the contents of a dataset. The purpose of this document and associated process is to ensure that staff are using a consistent process that protects the City and its residents from risk of accidentally making public or otherwise sharing with inappropriate parties data that may cause harm to a specific organization or individual.The City has four levels of data classification: public; internal use; sensitive; and restricted. The definitions and examples of these levels are provided in the document.This document also provides a workflow diagram staff can use when classifying data. This may be helpful for members of the public to better understand how classification decisions are made by staff and the Data Governance Committee.

According to Cognitive Market Research, the global Data Classification market size will be USD 1842.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 25.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 736.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 23.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 552.66 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 423.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 27.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 92.11 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 36.84 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.9% from 2024 to 2031.
The Solutions is the fastest growing segment of the Data Classification industry

Market Dynamics of Data Classification Market

Key Drivers for Data Classification Market

Increasing Data Privacy and Security Regulations to Boost Market Growth

The growing emphasis on statistics privateness and protection rules is using boom inside the records type marketplace. As businesses face stricter compliance requirements and heightened scrutiny over facts managing practices, there's an increasing need for sturdy data category solutions. These answers assist in categorizing and managing records based on their sensitivity and compliance desires, thereby mitigating risks related to records breaches and non-compliance consequences. Enhanced rules, which include GDPR and CCPA, are prompting corporations to spend money on superior records-type technology to shield touchy statistics and make certain adherence to prison standards, for that reason, fueling marketplace enlargement. For instance, In order to assist Indian businesses in consolidating all facets of risk under one roof via integrated risk management technology, Rotiviti India partnered with Riskconnect.

Expansion of the Data Breaches and Cyberattacks to Drive Market Growth

The surge in statistics breaches and cyberattacks is significantly boosting the facts category market. As cyber threats become more sophisticated and common, businesses are more and more adopting information classification answers to protect sensitive records. These technologies assist in figuring out, categorizing, and securing facts in line with their sensitivity, thereby minimizing the impact of ability breaches. With cyberattacks concentrated on valuable information and regulatory pressures mounting, agencies are investing in information-type systems to decorate their safety posture and ensure compliance. This developing demand for sturdy facts safety measures is riding the growth of the data category market.

Restraint Factor for the Data Classification Market

Complexity and Cost, will Limit Market Growth

The complexity and cost related to records classification are hindering the market boom. Implementing complete information classification solutions often calls for sizeable investment in advanced technology and professional personnel. The complexity of integrating those systems with present IT infrastructure and ensuring correct classification throughout various records assets provides to the mission. Additionally, ongoing maintenance and updates to hold pace with evolving threats and regulatory adjustments contribute to excessive prices. These factors can be especially burdensome for small and medium-sized organizations, limiting their capability to undertake powerful records class answers and thereby restraining usual marketplace enlargement.

Impact of Covid-19 on the Data Classification Market

The COVID-19 pandemic has had a combined effect on the statistics classification market. On the one hand, the improved shift too far-off work and expanded reliance on virtual systems heightened the want for robust statistics classification answers to stable, sensitive records and make sure compliance with data protection policies. On the other hand, economic uncertainties and price range constraints in the course of t...

The Data Classification Software market is experiencing robust growth, driven by increasing concerns around data privacy regulations (like GDPR and CCPA), rising cyber threats, and the exponential growth of unstructured data. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This growth is fueled by the widespread adoption of cloud-based solutions, which offer scalability, flexibility, and cost-effectiveness compared to on-premises deployments. Large enterprises are currently the primary adopters, but the market is witnessing significant expansion among SMEs due to increasing awareness of data security and compliance requirements. Key trends include the integration of AI and machine learning for automated classification, the rise of data loss prevention (DLP) solutions integrated with data classification, and a growing emphasis on granular access control based on classified data sensitivity. However, market growth is constrained by factors such as the complexity of implementing data classification solutions, the high initial investment costs for large-scale deployments, and the ongoing need for skilled professionals to manage and maintain these systems. The competitive landscape is highly fragmented, with established players like Microsoft, IBM, and Amazon competing against specialized vendors like Netwrix and Varonis Systems. The market is witnessing increased innovation in areas like automated classification and integration with other security tools, leading to greater efficiency and cost savings for organizations. The geographical distribution shows strong growth in North America and Europe, with Asia Pacific emerging as a rapidly expanding region due to increasing digitalization and stringent data governance regulations. The on-premises segment is gradually declining in favor of the more agile and scalable cloud-based solutions.

The size of the Data Classification market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 24.00% during the forecast period.Sorting out data based on their levels of sensitivity, value, and other regulation requirements is referred to as classification. Sensitive information shall be identified, labeled, and protected for confidentiality, integrity, and availability. Encoded, access controls, DLP policies, and protection measure will be in place correctly based on the classification of data levels.Data classification is very important in several reasons. It means organizations meet most of the data protection regulations, including GDPR and HIPAA, that clearly specify how they must treat and store sensitive data.This means that it allows organizations to give priority to the most important data. Thus, an organization identifies its sensitive data and categorizes them appropriately so that proper resource allocation is done to avoid such risks and prevent a breach. Moreover, data classification improves data governance and informed decision-making by analyzing data usage patterns and potential vulnerabilities. Key drivers for this market are: , Government Regulations and Compliance for Privacy & Data Security; Concern for Data Theft due to Mismanagement; Surge in Analytics Applications with Stored Data. Potential restraints include: Limited Efficiency Compared to Shared Hosting, Dedicated Hosting, and Cloud Servers. Notable trends are: Surge in Data Security Solutions for Increased Malware Infection Rates in Computers.

The Governance, Risk Management, and Compliance (GRC) Data Classification market is experiencing robust growth, driven by increasing regulatory scrutiny, the rising volume of sensitive data, and the growing need for robust data security measures across diverse sectors. The market is projected to reach a value of $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by the widespread adoption of cloud computing and the increasing reliance on data analytics, necessitating effective data classification to ensure compliance and minimize risks. Key segments, including Banking, Financial Services, and Insurance (BFSI), and Government and Defence are leading the market expansion due to their stringent regulatory frameworks and sensitive data handling requirements. The shift towards content-based and context-based classification methodologies, offering more granular control and automated processes, further contributes to market growth. However, challenges like data silos, integration complexities, and the lack of skilled professionals to manage GRC data classification initiatives present restraints to the market's expansion. Further fueling market expansion is the increasing adoption of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) for automated data classification and risk assessment. This automation enhances efficiency and reduces the manual effort required for data classification, making it more cost-effective for organizations of all sizes. The growing demand for user-based classification, providing personalized access controls, also contributes to market growth. While challenges exist, technological advancements and stringent regulatory compliance requirements are expected to drive significant market expansion in the coming years, with particular focus on regions like North America and Europe, which are early adopters of advanced data classification technologies. The market is highly competitive, with established players such as IBM, Microsoft, and RSA Security vying for market share alongside emerging innovative technology providers.

There are four datasets:

1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value.

2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language.

3. Blanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements.

We have used a publicly available dataset, COVID-19 Tweets Dataset, consisting of an extensive collection of 1,091,515,074 tweet IDs, and continuously expanding. The dataset was compiled by tracking over 90 distinct keywords and hashtags commonly associated with discussions about the COVID-19 pandemic. From this massive dataset, we focused on a specific time frame, encompassing data from August 05, 2020, to August 26, 2020, to meet our research objectives. As this dataset contains only tweet IDs, we have used the Twitter developer API to retrieve the corresponding tweets from Twitter. This retrieval process involved searching for tweet IDs and extracting the associated tweet texts, and it was implemented using the Twython library. In total, we successfully collected 21,890 tweets during this data extraction phase.

Following guidelines set by the CDC and WHO, we categorized tweets into five distinct classes for classification: health risks, prevention, symptoms, transmission, and treatment. Specifically, individuals aged over sixty, or those with pre-existing health conditions such as heart disease, lung problems, weakened immune systems, or diabetes, are at higher risk of severe COVID-19 complications. Therefore, tweets categorized as ‘health risks’ pertain to the elevated risks associated with COVID-19 due to age or specific health conditions. ‘Prevention’ related tweets encompass discussions on preventive and precautionary measures regarding the COVID-19 pandemic. Tweets discussing common COVID-19 symptoms, including cough, congestion, breathing issues, fever, body aches, and more, are classified as ‘symptoms’ related tweets. Conversations pertaining to the spread of COVID-19 between individuals, between animals and humans, and contact with virus-contaminated objects or surfaces are categorized as ‘transmission’ related tweets. Lastly, tweets indicating vaccine development and drugs used for COVID-19 treatment fall under the ‘treatment’ related category.

We determined specific keywords for each of the five classes (health risks, prevention, symptoms, transmission, and treatment) based on the definitions provided by the CDC and WHO on their official websites. These definitions, along with their associated keywords, are detailed in Table 1. For instance, the CDC and WHO indicate that individuals over the age of sixty with conditions like heart disease, lung problems, weak immune systems, or diabetes face a higher risk of severe COVID-19 complications. In accordance with this definition, we selected relevant keywords such as “lung disease”, “heart disease”, “diabetes”, “weak immunity”, and others to identify tweets related to health risks within the larger tweet dataset. This approach was consistently applied to define keywords for the remaining four classes. Subsequently, we filtered the initial dataset of 21,890 tweets to extract tweets relevant to our predefined classes, resulting in a total of 6,667 tweets based on the selected keywords.

To ensure the accuracy of our dataset, two separate annotators individually assigned the 6,667 tweets to the five classes. A third annotator, a natural language expert, meticulously cross-checked the dataset and provided necessary corrections. Subsequently, the two annotators resolved any discrepancies through mutual agreement, resulting in the final annotated dataset. Our dataset comprises a total of 6,667 data points categorized into five classes: 978, 2046, 1402, 802, and 1439 tweets annotated as ‘health risk’, ‘prevention’, ‘symptoms’, ‘transmission’, and ‘treatment’, respectively

Data Classification Software Market Outlook

The global data classification software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 5.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 14.8% during the forecast period. The significant growth factor driving this market is the increasing need for data security and compliance across various industries, fueled by stringent regulatory requirements and the rising volume of data generated globally.

Several factors are contributing to the robust growth of the data classification software market. First and foremost, the proliferation of digital data across organizations of all sizes is generating a critical need for effective data management solutions. As companies strive to safeguard sensitive information against breaches and unauthorized access, data classification software offers an efficient means to categorize and secure data based on its sensitivity and importance. This is particularly relevant in highly regulated sectors such as BFSI (Banking, Financial Services, and Insurance), healthcare, and government, where compliance with data protection laws is paramount.

Another driving force behind the market's expansion is the increasing adoption of cloud computing and the consequential rise in cyber threats. As more enterprises migrate their data to cloud environments, the risk of data breaches and loss escalates, prompting organizations to invest in robust data classification tools. These tools help in identifying, categorizing, and protecting data, thereby mitigating risks and ensuring regulatory compliance. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) technologies are enhancing the capabilities of data classification software, making it more accurate and efficient in identifying and categorizing data.

The rise of remote work, spurred by the COVID-19 pandemic, has also played a significant role in driving the demand for data classification solutions. With employees accessing corporate networks from various locations, the risk of data leaks and breaches has heightened, necessitating the implementation of robust security measures. Data classification software helps organizations to maintain data integrity and confidentiality, ensuring that sensitive information is accessed and shared securely. Additionally, the growing awareness about the importance of data privacy among consumers is urging companies to adopt stringent data protection measures, further propelling market growth.

Regionally, North America is anticipated to hold the largest market share throughout the forecast period, driven by the presence of major market players and stringent data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Europe is also expected to witness substantial growth due to similar regulatory frameworks and the increasing digitization of businesses. Meanwhile, the Asia Pacific region is projected to experience the highest CAGR, fueled by rapid technological advancements, increased adoption of cloud services, and growing awareness about data security and compliance.

Component Analysis

The data classification software market is segmented into software and services based on components. The software segment encompasses various types of data classification tools designed to identify, categorize, and protect data according to predefined criteria. This segment is witnessing significant growth due to the increasing need for automated data management solutions that can handle large volumes of data with high accuracy. Advanced software solutions leverage AI and ML technologies to enhance data classification processes, making them more efficient and reliable. As organizations continue to generate vast amounts of data, the demand for sophisticated software solutions is expected to rise further.

On the other hand, the services segment includes professional and managed services offered by vendors to support the implementation, maintenance, and optimization of data classification solutions. Professional services typically involve consulting, integration, and training, helping organizations to tailor the software to their specific needs and ensure seamless implementation. Managed services, meanwhile, encompass ongoing support and maintenance, allowing companies to outsource the management of their data classification infrastructure. This segment is gaining traction as businesses increasingly seek expert guidance to navigate complex data protection regulations and optim

International Guidelines for certification and classification (codin) of Covid-19 as a cause of death - WHO.

These files contain classified topo/bathy lidar data. Data are classified as 1 (valid non-ground topographic data), 2 (valid ground topographic data), 23 (submerged aquatic vegetation), and 29 (valid bathymetric data). Classes 1 and 2 are defined in accordance with the American Society for Photogrammetry and Remote Sensing (ASPRS) classification standards. These data were collected by the Coast...

BackgroundWhile efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of open source benchmarks and standard expectations for reporting means that published works often claim high generalisation capabilities, but with poor practices or missing details in the paper. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interfaces.MethodsWe present an open-source benchmarking framework, BenchNIRS, to establish a best practice machine learning methodology to evaluate models applied to fNIRS data, using five open access datasets for brain-computer interface (BCI) applications. The BenchNIRS framework, using a robust methodology with nested cross-validation, enables researchers to optimise models and evaluate them without bias. The framework also enables us to produce useful metrics and figures to detail the performance of new models for comparison. To demonstrate the utility of the framework, we present a benchmarking of six baseline models [linear discriminant analysis (LDA), support-vector machine (SVM), k-nearest neighbours (kNN), artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM)] on the five datasets and investigate the influence of different factors on the classification performance, including: number of training examples and size of the time window of each fNIRS sample used for classification. We also present results with a sliding window as opposed to simple classification of epochs, and with a personalised approach (within subject data classification) as opposed to a generalised approach (unseen subject data classification).Results and discussionResults show that the performance is typically lower than the scores often reported in literature, and without great differences between models, highlighting that predicting unseen data remains a difficult task. Our benchmarking framework provides future authors, who are achieving significant high classification scores, with a tool to demonstrate the advances in a comparable way. To complement our framework, we contribute a set of recommendations for methodology decisions and writing papers, when applying machine learning to fNIRS data.

Provide the file retention period differentiation table used by the government agency to classify and manage files based on the retention period, in order to facilitate the execution of document preservation, destruction, transfer, and other management operations.

The requirements

Classifying trees from point cloud data is useful in applications such as high-quality 3D basemap creation, urban planning, and forestry workflows. Trees have a complex geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.Using the modelFollow the guide to use the model. The model can be used with the 3D Basemaps solution and ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with the attributes: X, Y, Z, and Number of Returns.Note: This model is trained to work on unclassified point clouds that are in a projected coordinate system, where the units of X, Y, and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The provided deep learning model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification.This model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time and compute resources while improving accuracy. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block, and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following 2 classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThis model is expected to work well in all regions globally, with an exception of mountainous regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. Class Precision Recall F1-score Trees / High-vegetation (5) 0.975374 0.965929 0.970628Training dataThis model is trained on a subset of UK Environment Agency's open dataset. The training data used has the following characteristics: X, Y and Z linear unit meter Z range -19.29 m to 314.23 m Number of Returns 1 to 5 Intensity 1 to 4092 Point spacing 0.6 ± 0.3 Scan angle -23 to +23 Maximum points per block 8192 Extra attributes Number of Returns Class structure [0, 5]Sample resultsHere are a few results from the model.

Data used for preparation of the paper: Identifying and understanding the emergence of classes of food security policies in Africa: Lessons for COVID-19 food security responses

6000 French user reviews from three applications on Google Play (Garmin Connect, Huawei Health, Samsung Health) are labelled manually. We selected four labels: rating, bug report, feature request and user experience.

• Ratings are simple text which express the overall evaluation to that app, including praise, criticism, or dissuasion.
• Bug reports show the problems that users have met while using the app, like loss of data, crash of app, connection error, etc.
• Feature requests reflect the demande of users on new function, new content, new interface, etc.
• In user experience, users describe their experience in relation to the functionality of the app, how does certain functions be helpful.

As we can observe from the following table, that shows examples of labelled user reviews, each review belongs to one or more categories.

New Dataset

Based on this dataset, we developed a labeled dataset containing 6,000 English and 6,000 French reviews for classification, as well as 1,200 bilingual reviews for clustering. The new dataset has been made publicly available on Zenodo at the following link: https://zenodo.org/records/11066414

No notes provided