This comprehensive dataset is a meticulously curated collection of mental health statuses tagged from various statements. The dataset amalgamates raw data from multiple sources, cleaned and compiled to create a robust resource for developing chatbots and performing sentiment analysis.

Data Source:

The dataset integrates information from the following Kaggle datasets:

• 3k Conversations Dataset for Chatbot
• Depression Reddit Cleaned
• Human Stress Prediction
• Predicting Anxiety in Mental Health Data
• Mental Health Dataset Bipolar
• Reddit Mental Health Data
• Students Anxiety and Depression Dataset
• Suicidal Mental Health Dataset
• Suicidal Tweet Detection Dataset

Data Overview:

The dataset consists of statements tagged with one of the following seven mental health statuses: - Normal - Depression - Suicidal - Anxiety - Stress - Bi-Polar - Personality Disorder

Data Collection:

The data is sourced from diverse platforms including social media posts, Reddit posts, Twitter posts, and more. Each entry is tagged with a specific mental health status, making it an invaluable asset for:

• Developing intelligent mental health chatbots.
• Performing in-depth sentiment analysis.
• Research and studies related to mental health trends.

Features:

• unique_id: A unique identifier for each entry.
• Statement: The textual data or post.
• Mental Health Status: The tagged mental health status of the statement.

Usage:

This dataset is ideal for training machine learning models aimed at understanding and predicting mental health conditions based on textual data. It can be used in various applications such as:

• Chatbot development for mental health support.
• Sentiment analysis to gauge mental health trends.
• Academic research on mental health patterns.

Acknowledgments:

This dataset was created by aggregating and cleaning data from various publicly available datasets on Kaggle. Special thanks to the original dataset creators for their contributions.

Product Review Datasets: Uncover user sentiment

Harness the power of Product Review Datasets to understand user sentiment and insights deeply. These datasets are designed to elevate your brand and product feature analysis, help you evaluate your competitive stance, and assess investment risks.

Data sources:

• Trustpilot: datasets encompassing general consumer reviews and ratings across various businesses, products, and services.

Leave the data collection challenges to us and dive straight into market insights with clean, structured, and actionable data, including:

• Product name;
• Product category;
• Number of ratings;
• Ratings average;
• Review title;
• Review body;

Choose from multiple data delivery options to suit your needs:

1. Receive data in easy-to-read formats like spreadsheets or structured JSON files.
2. Select your preferred data storage solutions, including SFTP, Webhooks, Google Cloud Storage, AWS S3, and Microsoft Azure Storage.
3. Tailor data delivery frequencies, whether on-demand or per your agreed schedule.

Why choose Oxylabs?

1. Fresh and accurate data: Access organized, structured, and comprehensive data collected by our leading web scraping professionals.

2. Time and resource savings: Concentrate on your core business goals while we efficiently handle the data extraction process at an affordable cost.

3. Adaptable solutions: Share your specific data requirements, and we'll craft a customized data collection approach to meet your objectives.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA standards.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Join the ranks of satisfied customers who appreciate our meticulous attention to detail and personalized support. Experience the power of Product Review Datasets today to uncover valuable insights and enhance decision-making.

Sentiment Trading Signals AI Market Outlook

The global Sentiment Trading Signals AI market size reached USD 1.42 billion in 2024, according to our latest research, and is projected to grow at a robust CAGR of 22.8% during the forecast period, reaching approximately USD 10.80 billion by 2033. This significant expansion is primarily driven by the increasing adoption of artificial intelligence and machine learning technologies in financial trading, the proliferation of alternative data sources, and the rising demand for real-time, data-driven insights among both retail and institutional investors.

The growth trajectory of the Sentiment Trading Signals AI market is underpinned by several transformative factors. Foremost among these is the exponential increase in unstructured data generated across social media, news platforms, and financial forums, which has created a fertile ground for AI-driven sentiment analysis. Financial institutions and individual traders alike are seeking advanced tools to parse this data, extract actionable insights, and gain a competitive edge in dynamic markets. The capability of AI to process vast datasets in real time, identify subtle shifts in market sentiment, and generate predictive trading signals is revolutionizing investment strategies. As a result, the integration of sentiment analysis into algorithmic trading platforms is becoming a standard practice, enabling market participants to make more informed, timely, and profitable decisions.

Another critical driver is the rapid advancement in natural language processing (NLP) and machine learning algorithms, which have significantly enhanced the accuracy and reliability of sentiment trading signals. With the financial markets becoming increasingly complex and interconnected, traditional analytical methods often fall short in capturing nuanced investor sentiment. AI-powered solutions can analyze textual data from diverse sources, including tweets, news headlines, earnings call transcripts, and regulatory filings, to detect emerging trends and potential market-moving events. This technological evolution is not only improving the precision of trading signals but also reducing the latency in their delivery, which is crucial for high-frequency and quantitative trading strategies. Furthermore, the democratization of AI tools through cloud-based platforms is making sophisticated sentiment analysis accessible to a broader spectrum of market participants, from individual traders to large asset management firms.

The growing acceptance of alternative data and the increasing regulatory emphasis on transparency and risk management are also fueling the adoption of sentiment trading signals AI solutions. Institutional investors are leveraging AI-driven sentiment analysis to complement traditional fundamental and technical analyses, thereby enhancing portfolio diversification and risk-adjusted returns. Moreover, as financial markets become more volatile and susceptible to global events, the ability to rapidly gauge market sentiment and adjust trading strategies accordingly is becoming a critical differentiator. This trend is further supported by the emergence of fintech startups and established vendors offering tailored AI solutions for different asset classes, including equities, cryptocurrencies, forex, and commodities. The convergence of these factors is expected to sustain the robust growth of the sentiment trading signals AI market over the forecast period.

In this rapidly evolving landscape, the introduction of AI-Enhanced Influencer Sentiment Tracker is set to redefine how market sentiment is gauged. By leveraging advanced AI algorithms, this tool can analyze the sentiment of influential market voices across various social media platforms. This capability is crucial as influencers often sway public opinion and market trends, especially in volatile sectors like cryptocurrency and emerging markets. The AI-Enhanced Influencer Sentiment Tracker offers traders and investors a deeper understanding of market dynamics by identifying sentiment shifts that may not be immediately apparent through traditional data sources. This innovative approach not only enhances the precision of trading signals but also provides a competitive edge in anticipating market movements driven by influencer sentiment.

From a regional perspective, North Amer

Please cite the following paper when using this dataset: N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A.Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” arXiv [cs.CY], 2024. Available: http://arxiv.org/abs/2406.07693 Abstract This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.

AI-Enhanced Investor Sentiment Analysis Market Outlook

According to our latest research, the global AI-Enhanced Investor Sentiment Analysis market size reached USD 1.34 billion in 2024 and is poised to grow at a robust CAGR of 18.2% from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 6.23 billion, driven by the increasing adoption of artificial intelligence in financial analytics and the growing demand for real-time sentiment-driven insights in investment decision-making. The surging integration of AI-powered sentiment analysis tools across the financial sector is a significant growth factor, enabling institutions to gain actionable intelligence from vast unstructured data sources.

One of the primary growth factors for the AI-Enhanced Investor Sentiment Analysis market is the exponential rise in the volume and complexity of financial data. As financial markets become more volatile and interconnected, the need for advanced analytics solutions that can process and interpret massive datasets in real-time has intensified. AI-powered sentiment analysis tools are uniquely positioned to extract meaningful patterns from news articles, social media feeds, financial reports, and other unstructured data sources, providing investors and institutions with a competitive edge. The proliferation of digital communication channels and the increasing influence of retail investors have further fueled the demand for sophisticated sentiment analysis platforms, making AI-driven solutions indispensable for modern investment strategies.

Another critical driver for market expansion is the ongoing digital transformation within the banking and financial services industry. Traditional financial institutions, as well as emerging fintech players, are leveraging AI-enhanced sentiment analysis to optimize portfolio management, enhance risk assessment, and improve trading strategies. These solutions enable financial professionals to anticipate market movements, identify trends, and mitigate risks more effectively. The integration of advanced machine learning algorithms and natural language processing (NLP) technologies into sentiment analysis tools has significantly improved their accuracy and predictive capabilities. As regulatory requirements for transparency and risk management become more stringent, financial organizations are increasingly turning to AI-powered sentiment analysis to ensure compliance and maintain a competitive advantage.

The market is also benefiting from the growing recognition of behavioral finance as a key factor in investment decision-making. Investors and asset managers are acknowledging the impact of emotions, biases, and collective sentiment on market dynamics. AI-enhanced sentiment analysis platforms offer a systematic approach to quantifying and incorporating investor sentiment into decision-making processes. This shift towards data-driven behavioral insights is reshaping investment strategies across asset classes, from equities to cryptocurrencies. The ability to monitor and analyze sentiment in real-time allows for more agile and informed responses to market events, further driving the adoption of AI-based solutions in the financial sector.

Regionally, North America continues to dominate the AI-Enhanced Investor Sentiment Analysis market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of leading financial institutions, advanced technological infrastructure, and a high concentration of AI solution providers have positioned North America at the forefront of market innovation. However, Asia Pacific is expected to witness the highest CAGR during the forecast period, fueled by rapid digitalization, increasing investments in AI technologies, and the growing prominence of retail investors in emerging markets. Europe remains a key market, driven by robust regulatory frameworks and a mature financial services sector. Latin America and the Middle East & Africa are also experiencing steady growth, supported by rising fintech adoption and evolving investment landscapes.

Sentiment Analysis Software has become a cornerstone in the financial sector, offering unparalleled insights into market dynamics. These software solutions are designed to process vast amounts of unstructured data, such as news articles, social media posts, and financial reports, to gauge public sentiment. By leveraging advanc

The Sentiment Analytics Software market is poised for robust expansion, projected to reach a substantial market size of approximately $7,500 million by 2025, with a compelling Compound Annual Growth Rate (CAGR) of around 22% anticipated throughout the forecast period of 2025-2033. This impressive growth is propelled by a confluence of factors, chief among them being the escalating demand for nuanced customer understanding and the imperative for data-driven decision-making across diverse industries. Organizations are increasingly leveraging sentiment analytics to glean actionable insights from unstructured data sources such as social media, customer reviews, surveys, and support tickets. This allows them to proactively address customer concerns, enhance product development, refine marketing strategies, and ultimately, foster greater customer loyalty. The burgeoning digital landscape and the proliferation of online interactions have created a rich tapestry of data, making sentiment analysis an indispensable tool for businesses seeking a competitive edge. Key market drivers include the widespread adoption of cloud-based sentiment analysis solutions, which offer scalability, accessibility, and cost-effectiveness, making advanced analytical capabilities available to a broader range of businesses. Furthermore, the continuous innovation in Natural Language Processing (NLP) and Machine Learning (ML) algorithms is significantly enhancing the accuracy and sophistication of sentiment analysis tools, enabling deeper and more precise interpretation of human emotions and opinions. While the market enjoys strong tailwinds, certain restraints, such as concerns over data privacy and the need for skilled personnel to interpret complex sentiment outputs, may present challenges. Nevertheless, the overwhelming benefits of sentiment analytics in driving customer-centricity and optimizing business operations are expected to outweigh these limitations, solidifying its position as a critical component of modern business intelligence. This report delves into the dynamic landscape of Sentiment Analytics Software, providing an in-depth analysis of its market trajectory from 2019 to 2033. The study focuses on the Base Year: 2025, with projections extending through a Forecast Period: 2025-2033, building upon Historical Period: 2019-2024 data. The market is anticipated to experience robust growth, driven by increasing demand for actionable insights from unstructured data.

Three news sources have been used in creating this dataset. 1. Sun, J. (2016, August). Daily News for Stock Market Prediction, Version 1. Retrieved (2024, August) from https://www.kaggle.com/aaron7sun/stocknews. 2. ARYAN SINGH. NYT Articles: 2.1M+ (2000-Present) Daily Updated. https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present. 3. GABRIEL PREDA. BBC News. https://www.kaggle.com/datasets/gpreda/bbc-news.

The first source covers from 2008-06-08 to 2016-07-01, the top 25 news of each day from Reddit World News. The second source is a direct import of the abstract column from New York Times articles from 2016-07-01 to 2017-09-05. The third is also a direct import of the description column from BBC News from 2017-09-05 to 2024-08-03. Thus, the whole coverage is from 2008-06-08 to 2024-08-03.

Three models have been used for sentiment results. NLTK VADER is applied first as it is the most lightweight and fastest to apply on large amounts of data. But, as news is mostly neural, NLTK vader gave a 1.0 neutral score for around 25% of the data. Therefore, two more advanced models, NLTK RoBERTa and HUGGING FACE distilbert-base-uncased-finetuned-sst-2-english, are applied to these neutral articles to identify them accurately.

Part of my school project for Nanyang Polytechnic | AI & Data Engineering

This dataset is a collection of mental health statuses, gathered from real statements shared by individuals. By bringing together and refining data from various sources, it provides a rich and reliable foundation for developing chatbots and exploring sentiment analysis. The goal is to offer meaningful insights and create tools that can better understand and respond to mental health needs.

The dataset brings together information from the following Kaggle datasets: • 3k Conversations Dataset for Chatbot • Depression Reddit Cleaned • Human Stress Prediction • Predicting Anxiety in Mental Health Data • Mental Health Dataset Bipolar • Reddit Mental Health Data • Students Anxiety and Depression Dataset • Suicidal Mental Health Dataset • Suicidal Tweet Detection Dataset

Dataset Overview:

The data is collected from various platforms, including social media, Reddit, Twitter, and others. Each entry is labeled with a specific mental health status. The dataset contains statements categorized under one of the following seven mental health statuses: Normal 1. Depression 2. Suicidal 3. Anxiety 4. Stress 5. Bipolar 6. Personality Disorder

Features:

1. A unique identifier for each entry
2. Statement: The textual data or post
3. Status: The labeled mental health status of each statement

Usage:

The dataset's structure and features make it highly versatile for various applications, particularly in the fields of mental health, artificial intelligence, and data analysis. Here are some key use cases: 1. Mental health chatbot development 2. Sentiment and emotion analysis 3. Early detection and intervention 4. Academic and clinical research 5. Social media monitoring 6. NLP model training

Acknowledgments:

This dataset is created by gathering and refining data from publicly available Kaggle datasets. Special thanks to the original dataset creators for their valuable contributions.

Techsalerator’s Location Sentiment Data for Mauritania

Techsalerator’s Location Sentiment Data for Mauritania offers a detailed and structured dataset that provides valuable insights into public sentiment across different regions. This data is essential for businesses, researchers, and policymakers looking to understand community perceptions, social trends, and regional sentiment variations.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for Mauritania

Techsalerator’s Location Sentiment Data for Mauritania enables in-depth analysis of public opinion and sentiment across urban, rural, and economic zones. The dataset supports industries such as market research, social sciences, government policy-making, and AI-driven sentiment analysis.

Top 5 Key Data Fields

• Geographic Location – Identifies the specific locations where sentiment data was collected, enabling regional sentiment mapping.
• Sentiment Score (Positive, Neutral, Negative) – Classifies public sentiment into measurable categories based on collected data sources.
• Source of Sentiment Data – Includes social media, news articles, customer reviews, and surveys for a holistic analysis.
• Time and Date Stamp – Records when the sentiment was captured, allowing for trend analysis over time.
• Topic/Industry Tags – Categorizes sentiment data by relevant industries such as politics, economy, healthcare, and business.

Top 5 Sentiment Trends in Mauritania

• Economic Sentiment Fluctuations – Public sentiment on economic conditions, including employment and inflation, varies significantly across regions.
• Urban vs. Rural Sentiment Gaps – Differences in sentiment between urban areas like Nouakchott and rural communities highlight distinct societal challenges.
• Public Perception of Government Policies – Analyzing feedback on infrastructure projects, healthcare initiatives, and educational reforms.
• Social Media Impact on Public Opinion – Sentiment shifts driven by trending topics and digital discussions on platforms like Facebook and Twitter.
• Foreign Investment Perception – Monitoring how Mauritanians view foreign business presence and economic partnerships.

Top 5 Applications of Location Sentiment Data in Mauritania

• Market Research and Consumer Insights – Businesses use sentiment analysis to understand consumer preferences and regional demand.
• Government Policy and Public Services – Authorities leverage sentiment data to assess policy impact and public satisfaction.
• Crisis Management and Risk Assessment – Identifying sentiment trends during economic downturns, political instability, or health crises.
• AI and Machine Learning Development – Enhancing natural language processing (NLP) models with localized sentiment data.
• Media and Journalism Analytics – News organizations analyze sentiment data to track public reaction to major events.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Mauritania, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographic Location
• Sentiment Score (Positive, Neutral, Negative)
• Source of Sentiment Data
• Time and Date Stamp
• Topic/Industry Tags
• Social Media Sentiment
• News and Media Sentiment
• Customer Reviews and Feedback
• Political and Economic Sentiment Trends
• Contact Information

For in-depth insights into public opinion and sentiment patterns across Mauritania, Techsalerator’s dataset is an invaluable resource for businesses, researchers, policymakers, and AI developers.

AI-Driven Brand Sentiment Monitoring Market Outlook

According to our latest research, the AI-Driven Brand Sentiment Monitoring market size reached USD 2.76 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to expand at a CAGR of 17.9% from 2025 to 2033, attaining a value of USD 11.69 billion by 2033. This remarkable growth is propelled by the increasing necessity for real-time brand reputation management and the integration of advanced AI tools that enable organizations to extract actionable insights from vast and varied data sources.

The primary growth driver for the AI-Driven Brand Sentiment Monitoring market is the exponential increase in digital content and consumer interactions across social media channels. As brands strive to maintain a positive public image and respond proactively to customer sentiments, AI-powered sentiment analysis solutions have become indispensable. These solutions leverage natural language processing (NLP) and machine learning algorithms to analyze unstructured data, providing businesses with a nuanced understanding of consumer perceptions and emerging trends. The increasing sophistication of AI models, capable of understanding context, sarcasm, and multilingual content, is further fueling adoption, especially among enterprises dealing with global audiences.

Another significant factor contributing to market expansion is the growing emphasis on customer-centric strategies in highly competitive industries such as retail, BFSI, and healthcare. Organizations are investing in AI-driven sentiment monitoring tools to personalize marketing campaigns, enhance customer service, and swiftly address negative feedback before it escalates. The integration of these tools with customer relationship management (CRM) and business intelligence (BI) platforms is enabling a more holistic approach to brand management. Furthermore, the ability to monitor and analyze sentiment in real-time allows brands to capitalize on positive trends and mitigate reputational risks, resulting in improved customer loyalty and increased revenue streams.

Regulatory compliance and risk management are also shaping the trajectory of the AI-Driven Brand Sentiment Monitoring market. With the proliferation of data privacy laws such as GDPR and CCPA, companies are seeking AI solutions that can ensure compliance while delivering actionable insights. Vendors are increasingly focusing on providing secure, transparent, and customizable platforms that align with industry-specific regulations. Additionally, the rise of misinformation and the need for crisis management have prompted organizations to adopt advanced sentiment monitoring tools capable of detecting and addressing potential threats in real time, safeguarding both brand reputation and stakeholder trust.

Regionally, North America dominates the market, driven by the presence of major technology providers, high digital penetration, and early adoption of AI-driven analytics. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid digitalization, expanding e-commerce, and increasing investments in AI technologies. Europe follows closely, with a focus on regulatory compliance and data privacy. The Middle East & Africa and Latin America are also witnessing steady growth, supported by rising awareness of brand reputation management and the proliferation of social media platforms. This regional diversification is creating new opportunities for vendors to tailor their offerings to local market needs and regulatory environments.

In this evolving landscape, Social Listening AI is becoming an integral tool for organizations aiming to harness the power of consumer insights. By utilizing advanced algorithms, Social Listening AI enables businesses to monitor and analyze conversations across various social media platforms, providing a deeper understanding of public sentiment and emerging trends. This technology not only aids in identifying potential reputational risks but also assists in recognizing opportunities for engagement and brand advocacy. As companies strive to maintain a competitive edge, the ability to swiftly adapt to consumer preferences and market dynamics through Social Listening AI is proving to be invaluable.

Sentiment Analysis Market Outlook

According to our latest research, the global Sentiment Analysis market size reached USD 4.9 billion in 2024, reflecting robust adoption across diverse industries. With a compound annual growth rate (CAGR) of 14.2% from 2025 to 2033, the market is projected to expand significantly, reaching an estimated USD 15.3 billion by 2033. The primary growth driver is the surging demand for real-time insights into customer opinions, which is fueling the integration of advanced sentiment analytics into business intelligence and customer experience management platforms worldwide.

One of the key growth factors propelling the sentiment analysis market is the exponential rise in digital content generation across social media, e-commerce platforms, and online forums. Organizations are increasingly recognizing the strategic value of harnessing user-generated content and online reviews to gauge public perception, inform product development, and tailor marketing strategies. The proliferation of digital channels has led to a data deluge, and sentiment analysis solutions have become indispensable tools for extracting actionable insights from vast volumes of unstructured textual data. Additionally, advancements in natural language processing (NLP) and machine learning algorithms are enhancing the accuracy and contextual understanding of sentiment analysis tools, thus broadening their applicability across sectors such as retail, BFSI, healthcare, and government.

Another significant driver is the growing emphasis on personalized customer experiences. Enterprises are leveraging sentiment analysis to monitor and respond to customer feedback in real time, enabling proactive issue resolution and fostering brand loyalty. With the competitive landscape intensifying, businesses are prioritizing customer-centric strategies, and sentiment analysis offers a data-driven approach to understanding evolving consumer preferences and emotions. Furthermore, the integration of sentiment analysis with customer relationship management (CRM) systems and marketing automation platforms is streamlining workflows, reducing response times, and improving overall customer satisfaction metrics.

The sentiment analysis market is also benefiting from the increasing adoption of cloud-based analytics solutions. Cloud deployment models offer scalability, flexibility, and cost-effectiveness, making advanced sentiment analysis accessible to small and medium-sized enterprises (SMEs) as well as large organizations. The shift towards cloud-based services is enabling businesses to process and analyze data in real time, irrespective of geographical boundaries. Moreover, the rise of multilingual sentiment analysis tools is supporting global expansion strategies, allowing organizations to monitor sentiment across diverse markets and languages. These technological advancements are expected to sustain the market’s upward trajectory over the forecast period.

From a regional perspective, North America continues to dominate the sentiment analysis market, accounting for the largest revenue share in 2024, driven by the presence of leading technology vendors and early adoption of AI-driven analytics solutions. However, Asia Pacific is emerging as the fastest-growing region, with enterprises in countries such as China, India, and Japan ramping up investments in digital transformation initiatives. The increasing penetration of smartphones, internet connectivity, and social media platforms in Asia Pacific is generating a wealth of data, creating lucrative opportunities for sentiment analysis vendors. Meanwhile, Europe is witnessing steady growth, propelled by regulatory emphasis on data-driven decision-making and customer privacy.

Component Analysis

The sentiment analysis market by component is primarily segmented into Software and Services. The software segment dominates the market, attributed to the widespread deployment of advanced sentiment analysis tools that leverage artificial intelligence, machine learning, and natural language processing. These software solutions are designed to analyze unstructured data from multiple sources, including social media, customer reviews, emails, and chatbots, providing organizations with real-time insights into customer sentiments. The continuous evolution of sentiment analysis algorithms, with improved contextual understanding and multilingual support, is driving the adopt

This dataset contains a collection of 20,581 news headlines related to Artificial Intelligence (AI) published between August 2024 and July 2025. The headlines were aggregated from Google News using automated RSS feed collection and filtered to include only English-language sources. Each entry includes the headline text, publication date, and publisher.The dataset was created to support research on media narratives of AI, particularly trends in volume, sentiment, framing, and thematic focus. It can be used for natural language processing (NLP), sentiment analysis, topic modelling, and media/communication studies exploring how AI is represented in global and regional contexts.

The Public Opinion Monitoring Service market is experiencing robust growth, driven by increasing demand for real-time insights into public sentiment across various sectors. The market's expansion is fueled by the proliferation of social media, online forums, and news websites, which generate vast amounts of unstructured data reflecting public opinion. Businesses, governments, and organizations leverage these services to understand consumer preferences, track brand reputation, anticipate potential crises, and inform strategic decision-making. The market is segmented by service type (e.g., social media monitoring, news analysis, online forum monitoring), deployment mode (cloud-based, on-premise), and industry vertical (e.g., government, healthcare, finance). Competition is relatively fragmented, with companies like Xalted, Knowlesys, Graphen, Surfilter, Qingchuang Cyber Security, and We All Can vying for market share through differentiated offerings and technological advancements. Growth is further propelled by the rising adoption of artificial intelligence (AI) and machine learning (ML) techniques to enhance data analysis and sentiment detection accuracy. While data privacy concerns and the complexity of analyzing diverse data sources pose challenges, the overall market outlook remains positive, anticipating a substantial increase in market value over the forecast period. Despite the positive outlook, the market faces some restraints. The high cost of implementing and maintaining sophisticated monitoring systems can be a barrier to entry for smaller organizations. Moreover, ensuring data accuracy and minimizing biases in sentiment analysis require ongoing investment in technology and expertise. The need for robust data security measures to protect sensitive information adds to the operational complexity. However, the growing recognition of the strategic importance of public opinion monitoring across various sectors is likely to outweigh these challenges, driving market growth in the long term. Continuous technological advancements in natural language processing (NLP) and sentiment analysis are expected to further enhance the efficiency and effectiveness of these services, making them indispensable tools for organizations seeking to stay ahead of the curve.

Sentiment-Driven Routing AI Market Outlook

According to our latest research, the global Sentiment-Driven Routing AI market size reached USD 1.43 billion in 2024, reflecting rapid adoption across diverse industries. The market is projected to expand at a robust CAGR of 19.7% from 2025 to 2033, culminating in a forecasted value of USD 6.93 billion by 2033. This accelerated growth is primarily fueled by the increasing demand for enhanced customer engagement, real-time sentiment analysis, and the need for intelligent automation in customer-facing operations, as organizations strive to deliver personalized and context-aware experiences.

Several factors are driving the expansion of the Sentiment-Driven Routing AI market. One of the most significant growth drivers is the surge in digital transformation initiatives across enterprises of all sizes. Businesses are increasingly leveraging AI-powered sentiment analysis to optimize customer interactions, route inquiries to the most suitable agents, and deliver superior service outcomes. The proliferation of digital channels, such as social media, web chats, and messaging platforms, has made it imperative for organizations to deploy advanced AI solutions capable of interpreting customer emotions and intent in real time. As a result, sentiment-driven routing AI is becoming an essential component in the modern customer experience ecosystem, enabling companies to increase satisfaction, loyalty, and operational efficiency.

Another key factor contributing to market growth is the rapid advancement in natural language processing (NLP) and machine learning technologies. These innovations have significantly improved the accuracy and scalability of sentiment analysis engines, allowing them to process vast volumes of unstructured data from multiple sources. Furthermore, the integration of sentiment-driven routing AI with existing CRM, ERP, and contact center platforms has become increasingly seamless, reducing deployment barriers and accelerating time to value. Organizations across sectors such as BFSI, healthcare, retail, and telecommunications are investing heavily in these solutions to gain actionable insights, proactively address customer concerns, and differentiate themselves in highly competitive markets.

Regulatory compliance and the growing emphasis on ethical AI are also shaping the trajectory of the Sentiment-Driven Routing AI market. Industries such as banking, healthcare, and finance must adhere to stringent data privacy and security regulations, necessitating the adoption of AI solutions that are transparent, explainable, and compliant. Vendors are responding by developing sentiment-driven routing platforms that incorporate robust security measures, audit trails, and bias mitigation capabilities, thereby fostering greater trust among enterprise buyers. Additionally, the integration of multilingual and multicultural sentiment analysis is expanding the addressable market, enabling organizations to serve global customer bases more effectively.

From a regional perspective, North America continues to dominate the Sentiment-Driven Routing AI market, accounting for the largest revenue share in 2024. This leadership is attributed to the high concentration of technology innovators, early adopters, and significant investments in AI research and development. However, the Asia Pacific region is poised for the fastest growth over the forecast period, driven by rising digitalization, a burgeoning e-commerce sector, and increasing awareness of the benefits of AI-powered customer engagement. Europe also remains a significant market, propelled by stringent regulatory frameworks and growing demand for personalized customer experiences across industries.

Component Analysis

The Sentiment-Driven Routing AI market is segmented by component into Software, Hardware, and Services. The software segment currently holds the largest share, owing to the critical role of AI-powered sentiment analysis engines, routing algorithms, and integration platforms.

Sentiment Analysis for Financial Services Market Outlook

According to our latest research, the global sentiment analysis for financial services market size reached USD 4.2 billion in 2024 and is projected to grow at a robust CAGR of 15.8% from 2025 to 2033, ultimately reaching USD 14.7 billion by 2033. This impressive growth is primarily driven by the increasing adoption of artificial intelligence and machine learning technologies in financial institutions seeking to enhance decision-making, manage risks, and deliver superior customer experiences. The rising volume of unstructured data from social media, news feeds, and customer interactions has made sentiment analysis a critical tool for financial services firms aiming to gain actionable insights and maintain a competitive edge in a dynamic market landscape.

One of the most significant growth factors for the sentiment analysis for financial services market is the exponential increase in data generated across digital channels. Financial institutions are inundated with vast amounts of textual and voice data from sources such as social media platforms, online reviews, call center transcripts, and news articles. Sentiment analysis solutions enable these organizations to efficiently process and analyze this unstructured data, extracting valuable insights into market trends, customer sentiment, and emerging risks. By leveraging advanced natural language processing (NLP) and machine learning algorithms, financial firms can proactively respond to market fluctuations, identify reputational risks, and tailor their products and services to align with evolving customer preferences. This data-driven approach is fueling the rapid adoption of sentiment analysis tools, particularly among banks, asset management firms, and fintech companies.

Another driving force behind the expansion of the sentiment analysis for financial services market is the growing need for enhanced risk management and fraud detection capabilities. The financial sector is highly regulated and faces constant threats from cybercriminals and fraudulent activities. Sentiment analysis enables institutions to monitor customer communications, transaction patterns, and public sentiment in real-time, helping to detect anomalies, suspicious behaviors, and potential compliance breaches. Early detection of negative sentiment or unusual activity can trigger timely investigations, minimizing financial losses and reputational damage. As regulatory requirements become more stringent and the complexity of financial crimes increases, the demand for sophisticated sentiment analysis solutions is expected to surge, further propelling market growth.

Additionally, the relentless pursuit of improved customer experience is a major catalyst for the adoption of sentiment analysis in the financial services industry. TodayÂ’s customers expect personalized, responsive, and transparent interactions with their financial service providers. Sentiment analysis tools empower organizations to gauge customer emotions, satisfaction levels, and pain points across various touchpoints, enabling them to deliver targeted interventions, resolve issues swiftly, and foster long-term loyalty. By integrating sentiment analysis into customer relationship management (CRM) systems, financial institutions can prioritize high-value clients, anticipate churn, and develop innovative products that resonate with their audience. This focus on customer-centricity is a key differentiator in an increasingly competitive market, driving sustained investment in sentiment analysis technologies.

Sentiment-Driven Routing AI is emerging as a transformative technology in the financial services sector. This AI-driven approach leverages sentiment analysis to dynamically route customer queries and interactions based on the emotional tone detected in communications. By understanding the sentiment behind customer messages, financial institutions can prioritize and direct inquiries to the most appropriate resources, enhancing response times and customer satisfaction. Sentiment-Driven Routing AI not only improves operational efficiency but also empowers financial firms to deliver more personalized and empathetic customer service. As the volume of customer interactions continues to grow, the integration of sentiment-driven routing capabilities is becoming increasingly vital for maintaining a competitive edge and fostering cu

AI in Text Analytics Market Outlook

According to our latest research, the global AI in Text Analytics market size reached USD 7.4 billion in 2024, driven by the surging demand for actionable insights from unstructured text data across various industries. The market is expected to grow at a robust CAGR of 19.2% from 2025 to 2033, projecting a value of USD 31.7 billion by 2033. Key growth factors fueling this expansion include rapid technological advancements in natural language processing, increased adoption of big data analytics, and a growing emphasis on enhancing customer experience and operational efficiency.

A primary driver for the AI in Text Analytics market is the exponential growth in unstructured data generation across enterprises. Organizations worldwide are experiencing a data deluge from diverse sources such as emails, social media, customer reviews, and internal documents. This vast volume of text data, if left unprocessed, represents a missed opportunity for extracting business intelligence. AI-powered text analytics solutions are increasingly being leveraged to mine these datasets for sentiment, emerging trends, and actionable insights, enabling organizations to make informed decisions, personalize customer interactions, and optimize products and services. The integration of advanced machine learning algorithms and natural language processing models has significantly improved the accuracy and scalability of text analytics, making it an indispensable tool for data-driven enterprises.

Another significant growth factor is the rising demand for real-time analytics and automation in business processes. Companies are under pressure to respond swiftly to market changes, customer feedback, and potential risks. AI in Text Analytics empowers organizations to automate the extraction and analysis of critical information from vast text corpora, reducing manual effort and turnaround time. This automation not only enhances operational efficiency but also supports proactive risk management, fraud detection, and compliance monitoring. The proliferation of cloud-based text analytics platforms has further lowered the entry barriers for organizations of all sizes, enabling even small and medium enterprises to harness the power of AI without significant upfront investments in infrastructure.

The growing focus on customer-centric strategies is also propelling the adoption of AI in Text Analytics. In highly competitive industries such as retail, BFSI, and telecommunications, understanding customer sentiment, preferences, and pain points is crucial for sustaining growth. AI-driven text analytics tools allow businesses to analyze customer feedback from multiple touchpoints, uncover hidden patterns, and predict future behavior. This capability is particularly valuable in enhancing customer experience management, personalizing marketing campaigns, and improving product development. Moreover, regulatory requirements for transparency and data-driven decision-making in sectors like healthcare and finance are accelerating the deployment of robust text analytics solutions.

Regionally, North America continues to dominate the AI in Text Analytics market, owing to early technology adoption, a strong presence of leading vendors, and substantial investments in AI research and development. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digital transformation, expanding e-commerce, and increasing awareness of AI’s potential in business intelligence. Europe also holds a significant market share, driven by stringent data regulations and a growing emphasis on customer data analytics. Overall, the global landscape is characterized by increasing cross-industry adoption, with organizations recognizing the strategic value of unlocking insights from unstructured text data.

Component Analysis

The AI in Text Analytics market is segmented by component into Software and Services, each playing a pivotal role in the overall ecosystem. The software segment encompasses a wide array of platforms and tools designed to automate the extraction, classification, and interpretation of text data. These solutions leverage advanced natural language processing, machine learning, and deep learning algorithms to deliver high accuracy in sentiment analysis, entity recognition, and information extraction. The software market is highly dynamic, with frequent updates and innovations aimed at enhancing usability, scalability, and integration capabilities

Techsalerator’s Location Sentiment Data for Saudi Arabia

Techsalerator’s Location Sentiment Data for Saudi Arabia offers a structured analysis of public sentiment across various locations, providing valuable insights for businesses, researchers, and policymakers. This dataset is essential for understanding regional sentiment trends, consumer behavior, and social dynamics in Saudi Arabia.

Top 5 Key Data Fields

• Geographical Location – Identifies the specific region, city, or district where sentiment data is collected, offering localized insights.
• Sentiment Score – Measures public sentiment on a scale from negative to positive, allowing for trend analysis over time.
• Sentiment Source Type – Classifies data sources such as social media, news articles, reviews, and surveys for comprehensive sentiment tracking.
• Topic Categorization – Groups sentiment data based on subjects like politics, economy, tourism, and consumer goods to identify key trends.
• Time of Sentiment Capture – Records the date and time of sentiment expression, enabling the study of sentiment fluctuations and event-driven changes.

Top 5 Location Sentiment Trends in Saudi Arabia

• Urban vs. Rural Sentiment – Major cities like Riyadh and Jeddah exhibit higher engagement levels with diverse sentiment trends, while rural areas show a stronger community-driven sentiment.
• Tourism and Hospitality – Sentiment analysis of tourist destinations like Mecca and Medina reveals insights into visitor satisfaction and service expectations.
• Economic and Business Sentiment – Data on consumer confidence and economic outlook is helping businesses refine strategies in retail, banking, and real estate sectors.
• Social and Cultural Discussions – Analysis of public sentiment on social topics, including entertainment, sports, and cultural events, helps media and policymakers understand public interests.
• Technology and Innovation – Saudi Arabia’s Vision 2030 initiatives impact sentiment around smart city developments, AI, and digital transformation.

Top 5 Applications of Location Sentiment Data in Saudi Arabia

• Market Research & Consumer Insights – Companies leverage sentiment data to fine-tune marketing strategies and customer engagement.
• Government Policy & Public Opinion Analysis – Policymakers use sentiment insights to gauge public reaction to new regulations and initiatives.
• Tourism & Hospitality Enhancement – Hotels, airlines, and tourism boards analyze location-based sentiment to improve visitor experiences.
• Investment & Economic Forecasting – Financial institutions and investors track sentiment trends to assess market opportunities.
• Crisis Management & Public Relations – Businesses and government entities monitor real-time sentiment shifts to address concerns effectively.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Saudi Arabia, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographical Location
• Sentiment Score
• Sentiment Source Type
• Topic Categorization
• Time of Sentiment Capture
• Social Media Mentions
• Consumer Reviews & Feedback
• Economic Sentiment Trends
• Political & Policy Sentiment
• Contact Information

For in-depth insights into location-based sentiment trends in Saudi Arabia, Techsalerator’s dataset is an invaluable resource for businesses, analysts, and government organizations.

Tourism Sentiment Analysis Market Outlook

According to our latest research, the global tourism sentiment analysis market size reached USD 1.54 billion in 2024, demonstrating a robust expansion trajectory. The market is experiencing a healthy compound annual growth rate (CAGR) of 16.8% from 2025 to 2033. By 2033, the market is forecasted to attain a value of USD 5.08 billion. This significant growth is primarily driven by the increasing adoption of advanced analytics and artificial intelligence solutions by tourism stakeholders to better understand traveler sentiments, enhance customer experiences, and drive data-driven decision-making processes.

One of the primary growth factors fueling the tourism sentiment analysis market is the exponential increase in digital engagement across the travel and tourism ecosystem. With travelers sharing their experiences on social media, review platforms, and travel forums, there is a wealth of unstructured data that can be harnessed to extract actionable insights. This surge in user-generated content, coupled with advancements in natural language processing (NLP) and machine learning, has enabled organizations to gauge real-time sentiment and adjust their offerings accordingly. As a result, destination managers, travel agencies, and hospitality providers are increasingly integrating sentiment analysis tools to monitor brand perception, identify emerging trends, and proactively address negative feedback, thereby improving overall customer satisfaction and loyalty.

Another significant driver is the growing emphasis on personalized and seamless travel experiences. The tourism industry is highly competitive, and organizations are seeking innovative ways to differentiate themselves. Sentiment analysis solutions empower businesses to anticipate traveler needs, tailor marketing campaigns, and optimize service delivery based on insights derived from sentiment data. This capability is particularly critical in the post-pandemic era, where traveler expectations regarding safety, hygiene, and customer service have evolved rapidly. By leveraging sentiment analysis, tourism stakeholders can maintain a pulse on shifting consumer preferences and swiftly adapt their strategies to foster trust and engagement, leading to increased bookings and repeat visits.

Moreover, government and tourism boards are recognizing the strategic value of sentiment analysis in destination management and policy formulation. By analyzing public sentiment related to attractions, events, and tourism policies, authorities can make informed decisions to enhance destination appeal, manage crises, and allocate resources more effectively. The integration of sentiment analysis with other data sources, such as economic indicators and mobility data, allows for a holistic understanding of tourism dynamics, supporting sustainable growth and long-term competitiveness. As regulatory environments and data privacy frameworks mature, the adoption of sentiment analysis is expected to become more widespread, further propelling market expansion.

From a regional perspective, North America currently commands the largest share of the tourism sentiment analysis market, driven by the presence of leading technology providers, high digital literacy, and a mature travel industry. However, the Asia Pacific region is poised for the fastest growth over the forecast period, fueled by rapid digitization, burgeoning middle-class populations, and increasing investments in tourism infrastructure. Europe, with its rich cultural heritage and advanced tourism management practices, also represents a significant market, while Latin America and the Middle East & Africa are emerging as attractive opportunities due to rising inbound tourism and digital transformation initiatives. The interplay of technological innovation, evolving traveler behaviors, and supportive policy environments across these regions will continue to shape the global market landscape.

Sentiment Analysis has become an indispensable tool for the tourism industry, enabling stakeholders to decode the emotions and opinions expressed by travelers across various platforms. By employing sophisticated algorithms and natural language processing techniques, sentiment analysis tools can sift through vast amounts of unstructured data to identify patterns and trends. This capability

Text Analytics Market size was valued at USD 9.49 Billion in 2024 and is projected to reach USD 55.24 Billion by 2032, growing at a CAGR of 38.90% during the forecasted period 2026 to 2032.Growing Volume of Unstructured Data: A Data Deluge Driving Demand The exponential expansion of unstructured data is the primary catalyst for the text analytics market. With a significant volume of textual data being produced daily from sources like social media, customer emails, online reviews, and call center transcripts, businesses face the challenge of extracting meaningful information from this deluge. Text analytics tools and services are essential because they provide the technological capability to process, categorize, and analyze this messy, unorganized text at scale. This ability to glean insightful information from vast datasets is crucial for data-driven decision-making, directly fueling the growing demand for robust text analytics solutions globally.Growing Adoption of Natural Language Processing and Artificial Intelligence: The AI Edge The rapid adoption of Natural Language Processing (NLP) and Artificial Intelligence (AI) is revolutionizing the text analytics market. New developments in these technologies have allowed for the creation of more sophisticated and accurate tools, including advanced sentiment analysis, topic modeling, entity recognition, and machine translation. Businesses are increasingly leveraging these AI-powered text analytics tools to move beyond simple keyword counting and achieve a deeper understanding of language nuances. This capability is vital for gaining superior competitive intelligence, understanding industry trends, and deciphering complex consumer behavior, making AI and NLP core drivers of market innovation and growth.

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Sentiment Analysis allows a range of -1 (negative) to +1 (positive) to determine impact.