View monthly updates and historical trends for US Index of Consumer Sentiment. from United States. Source: University of Michigan. Track economic data wit…

Consumer Confidence in the United States decreased to 55 points in October from 55.10 points in September of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Consumer Sentiment Index in the United States stood at 64.7 in January 2025, an increase from the previous month. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

Success.ai’s Consumer Sentiment Data offers businesses unparalleled insights into global audience attitudes, preferences, and emotional triggers. Sourced from continuous analysis of consumer behaviors, conversations, and feedback, this dataset includes psychographic profiles, interest data, and sentiment trends that help marketers, product teams, and strategists better understand their target customers. Whether you’re exploring a new market, refining your brand message, or enhancing product offerings, Success.ai ensures your consumer intelligence efforts are guided by timely, accurate, and context-rich data.

Why Choose Success.ai’s Consumer Sentiment Data?

1. Comprehensive Audience Insights

   • Access psychographic and interest-based profiles that reveal what motivates and influences your audience’s decisions.
   • Continuous updates ensure you stay aligned with shifting consumer sentiments, seasonal preferences, and emerging trends.

2. Global Reach Across Industries and Demographics

   • Includes insights from various markets, age groups, cultural backgrounds, and income levels.
   • Identify consumer attitudes in different regions, helping you tailor campaigns, products, and messaging to diverse audiences.

3. Continuously Updated Datasets

   • Real-time data analysis ensures that your consumer sentiment insights remain fresh, relevant, and actionable.
   • Adapt quickly to consumer feedback, market changes, and competitive pressures.

4. Ethical and Compliant

   • Adheres to global data privacy regulations, ensuring your usage of consumer sentiment data is both legal and respectful of personal boundaries.

Data Highlights:

• Psychographic Profiles: Understand lifestyle preferences, values, and interests that shape consumer choices.
• Sentiment Trends: Track evolving emotional responses to brands, products, and categories.
• Global Audience Insights: Evaluate consumer sentiments across multiple regions, languages, and cultural contexts.
• Continuous Updates: Receive current data that reflects the latest shifts in mood, opinion, and interest.

Key Features of the Dataset:

1. Granular Segmentation

   • Segment audiences by demographic, interest, buying behavior, and sentiment scores for targeted marketing efforts.
   • Focus on the attributes that matter most, from eco-conscious consumers to luxury shoppers or value seekers.

2. Contextual Sentiment Analysis

   • Go beyond basic positive/negative sentiment to understand nuanced emotional responses.
   • Identify triggers that inspire loyalty, dissatisfaction, trust, or skepticism.

3. AI-Driven Enrichment

   • Profiles enriched with actionable data provide deeper insights into consumer lifestyles, brand perceptions, and product affinities.
   • Leverage advanced analytics to develop personalized campaigns and product strategies.

Strategic Use Cases:

1. Marketing and Campaign Optimization

   • Craft campaigns that resonate emotionally by understanding what drives consumer engagement.
   • Adjust messaging, timing, and channels to align with evolving sentiment trends and seasonal shifts in consumer mood.

2. Product Development and Innovation

   • Identify unmet consumer needs and preferences before launching new products.
   • Refine features, packaging, and pricing strategies based on real-time consumer responses.

3. Brand Management and Positioning

   • Monitor brand perceptions to detect early signs of brand fatigue, trust erosion, or negative publicity.
   • Strengthen brand loyalty by addressing concerns, highlighting strengths, and adapting to changing market contexts.

4. Competitive Analysis and Market Entry

   • Benchmark consumer sentiment towards competitors, industry leaders, and emerging disruptors.
   • Assess market readiness and optimize entry strategies for new regions or segments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access high-quality, verified data at competitive prices, ensuring efficient allocation of your marketing and research budgets.

2. Seamless Integration

   • Integrate enriched sentiment data into your analytics, CRM, or marketing platforms via APIs or downloadable formats.
   • Simplify data management and accelerate decision-making processes.

3. Data Accuracy with AI Validation

   • Benefit from AI-driven validation for reliable insights into consumer attitudes, leading to more confident data-driven strategies.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on specific segments, regions, or interests, and scale as your business grows and evolves.

APIs for Enhanced Functionality:

1. Data Enrichment API

   • Enhance your existing consumer records with psychographic and sentiment insights, deepening your understanding of audience motivations.

2. Lead Generation API

   • Identify audience segments receptive to your messaging, streamlini...

The global Consumer Sentiment Analysis Solution market size was valued at USD 1254.1 million in 2025 and is projected to grow at a CAGR of 12.5% from 2025 to 2033. The market growth is attributed to the rising need for understanding customer sentiment and preferences, increasing adoption of advanced analytics and machine learning technologies, and growing awareness of the benefits of sentiment analysis solutions. The market is segmented into three types: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). By application, the market is segmented into government, small and medium enterprises (SMEs), and large enterprises. North America is the largest market, followed by Europe and Asia Pacific. Major players in the market include Authenticx, InData Labs, Lexalytics, Lionbridge, MonkeyLearn, Rankraze, Rapidminer, Repustate, Starkdata, The Data Company, USM, Webs Utility, MAZAJ, IBM Watson, Salesforce, Adobe Experience Cloud, Sprinklr, Clarabridge, Brandwatch, Talkwalker, Lexalytics, NetBase Quid, Socialbakers, and others.

Consumer Confidence In the Euro Area increased to -14.90 points in September from -15.50 points in August of 2025. This dataset provides the latest reported value for - Euro Area Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

WiserBrand's Comprehensive Customer Call Transcription Dataset: Tailored Insights

WiserBrand offers a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:

User ID and Firm Name: Identify and categorize calls by unique user IDs and company names. Call Duration: Analyze engagement levels through call lengths. Geographical Information: Detailed data on city, state, and country for regional analysis. Call Timing: Track peak interaction times with precise timestamps. Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues. Device and OS Types: Information on the devices and operating systems used for technical support analysis. Transcriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.

Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data, Consumer Behavior Data, Consumer Sentiment Data, Consumer Review Data, AI Training Data, Textual Data, and Transcription Data applications.

WiserBrand's dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.

Cases:

1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models WiserBrand's Comprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:

Enriching STT Models: The dataset includes a wide variety of real-world customer service calls with diverse accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.

Contextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.

Improving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.

Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.

1. Training AI Agents for Replacing Customer Service Representatives WiserBrand’s dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:

Customer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.

Sentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.

Customizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. This means AI agents can be trained to recognize the types of queries that come up frequently, allowing them to automate routine tasks such as ...

Consumer Confidence in China decreased to 87.90 points in June from 88 points in May of 2025. This dataset provides - China Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

"This dataset includes consumer-submitted reviews from over 6000 companies, covering both product- and service-based businesses. It’s built to support CX, AI, and analytics teams seeking structured insight into what real customers say, feel, and expect — across the E-commerce industry

Each review includes:

• Authentic customer reviews (text, rating, pros and cons)
• Labeled sentiment and tone (positive, neutral, negative)
• Service context across industries: purchase, delivery, support, return, usage
• Industry and company filters (fully customizable per buyer request)
• Optional metadata: platform, review length, timestamp, geo-location

The list may vary based on the industry and can be customized as per your request.

Use this dataset to:

• Track public perception trends across specific brands or verticals
• Segment sentiment insights by industry, region, or company
• Power NLP pipelines that require diverse tone, emotion, and domain specificity
• Build dashboards or LLM prompts grounded in real user language
• Train review summarization, classification, or escalation engines

This dataset offers flexibility for custom delivery-by industry, domain, or company, making it ideal for teams needing scalable consumer voice data tailored to specific strategic goals."

Global Consumer Confidence Index by Country, 2023 Discover more data with ReportLinker!

Wroclaw Corpus of Consumer Reviews is a corpus of Polish reviews annotated with sentiment at the level of the whole text (text) and at the level of sentences (sentence) for the following domains: hotels, medicine, products and university (reviews*). Sentences are annotated with sentiment only for hotels and medicine. Each sentence file contains a single sentence with a sentiment labelz_X and each text file contains a single review with a sentiment labelmeta_X. Regardless a resource type, X can be: minus_m -- strong negative; minus_s -- weak negative, zero -- neutral, amb -- ambiguous, plus_s -- weak positive, plus_m -- strong positive. all sets are groups of all domains within each text/sentence type. Train/dev/test divisions were used for the evaluation. Results are available in the following paper:

@InProceedings{Kocon2019,

Title = {{Multi-level analysis and recognition of the text sentiment on the example of consumer opinions}},

Author = {Koco{\'n}, Jan and Zaśko-Zielińska, Monika and Miłkowski, Piotr},

Booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2019},

Year = {2019},

}

Please cite this paper if you use this resource.

Technological advancements in the Consumer Sentiment Analysis Solution industry are shaping the future market landscape. The report evaluates innovation-driven growth and how emerging technologies are transforming industry practices, offering a comprehensive outlook on future opportunities and market potential.

Data and expert analysis on RBI's Consumer Confidence Survey - Current Situation Index (CSI) and Future Expectation Index (FEI).

China Consumer Confidence Indicator: sa: Normalised data was reported at 97.312 Normal=100 in Dec 2023. This records an increase from the previous number of 97.240 Normal=100 for Nov 2023. China Consumer Confidence Indicator: sa: Normalised data is updated monthly, averaging 100.016 Normal=100 from Jan 1990 (Median) to Dec 2023, with 408 observations. The data reached an all-time high of 102.062 Normal=100 in Feb 2021 and a record low of 97.059 Normal=100 in Nov 2022. China Consumer Confidence Indicator: sa: Normalised data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.MEI: Consumer Opinion Surveys: Seasonally Adjusted: Non OECD Member. The Consumer Confidence Survey is conducted by China Economic Monitoring and Analysis Center (CEMAC) of the National Bureau of Statistics. Data for Consumer Confidence Indicator are available from June 1996 onwards. Starting from Q4 2009, CEMAC extended the sample size and coverage (including all tiers of urban cities in the East, Central, West and Northwest as well as rural areas).

Consumer Confidence in Japan increased to 35.30 points in September from 34.90 points in August of 2025. This dataset provides - Japan Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Madison Square Garden Entertainment (MSGE) : A Rollercoaster Ride Ahead?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Stock price and consumer sentiment consistently serve as pivotal economic indicators for the performance and growth of e-commerce enterprises. It is essential to comprehend and forecast the co-movement between the two to inform financing and investment decision-making effectively. Prior research has focused on predicting individual indicators, but not much of them attempt to forecast their co-movement. We propose a novel Rule Combination based on Bivariate Co-movement Network (RC-BCN) approach for bivariate co-movement forecasting. Bivariate co-movement features extracted utilizing the BCN’s topological nature instruct the entropy optimization in order to enhance the RC-BCN’s predictions. We conduct four sets of experiments on 1,135 data sets from JD.com between 2018 and 2022, where consumer sentiment is measured using text sentiment analysis of online reviews. The results indicate that RC-BCN’s prediction accuracy reaches at most 91% under distortion preference and is improved by 18% compared without entropy optimization. This study highlights the value of complex network and entropy theory in forecasting bivariate co-movement for e-commerce enterprises.

Consumer Confidence in Switzerland increased to -37 points in September from -40 points in August of 2025. This dataset provides - Switzerland Consumer Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

"This dataset includes consumer-submitted reviews from over 1523 companies, covering both product- and service-based businesses. It’s built to support CX, AI, and analytics teams seeking structured insight into what real customers say, feel, and expect — across Transportation and Logistics.

Each review includes:

• Authentic customer reviews (text, rating, pros and cons)
• Labeled sentiment and tone (positive, neutral, negative)
• Service context across industries: purchase, delivery, support, return, usage
• Industry and company filters (fully customizable per buyer request)
• Optional metadata: platform, review length, timestamp, geo-location

The list may vary based on the industry and can be customized as per your request.

Use this dataset to:

• Track public perception trends across specific brands or verticals
• Segment sentiment insights by industry, region, or company
• Power NLP pipelines that require diverse tone, emotion, and domain specificity
• Build dashboards or LLM prompts grounded in real user language
• Train review summarization, classification, or escalation engines

This dataset offers flexibility for custom delivery-by industry, domain, or company, making it ideal for teams needing scalable consumer voice data tailored to specific strategic goals."

The Bag Brand Sentiment Dataset is a collection of tweet data from Twitter that focuses on several popular designer bag brands. The dataset includes tweets related to seven specific keywords: "gucci bag", "chanel bag", "dior bag", "louis vuitton bag", "prada bag", "hermes bag", and "supreme bag".

The data was obtained using the Twitter API, which is a tool used to extract data from Twitter. The dataset consists of a total of 2881 tweets that were obtained through Twitter crawling. Before the dataset was compiled, a pre-processing process was conducted to remove duplicate data, ensuring that the dataset contains only unique tweets.

The Bag Brand Sentiment Dataset is useful for analyzing consumer sentiment towards popular designer bag brands. It can be used by marketers to gain insights into consumer preferences and attitudes towards specific brands. Additionally, researchers can use the dataset to study trends in consumer sentiment towards luxury goods or to explore how social media platforms are used to discuss designer bag brands.