Consumer Confidence in the United States increased to 61.80 points in July from 60.70 points in June 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.

In April 2020, the global consumer confidence index of ** countries worldwide dropped to **** following the outbreak of the COVID-19 pandemic. It then slowly increased until July 2021, when it reached an index score of ****. Global consumer confidence dropped in the latter half of 2022 following rising inflation rates, but has been increasing since November that year.

The Performance Confidence Index assesses the extent to which employees believe their organization has an outstanding competitive future, based on innovative, high-quality products and services that are highly regarded by the marketplace. The Performance Confidence Index is an average of the responses for the five items: Employees in my work unit meet the needs of our customers; Employees in my work unit contribute positively to my agency’s performance; Employees in my work unit produce high-quality work; Employees in my work unit adapt to changing priorities; Employees in my work unit achieve our goals.

United States CSI: Home Values: 1 Yr Ago: Index Score data was reported at 162.000 1966=100 in May 2018. This records an increase from the previous number of 154.000 1966=100 for Apr 2018. United States CSI: Home Values: 1 Yr Ago: Index Score data is updated monthly, averaging 138.000 1966=100 from Jan 1990 (Median) to May 2018, with 315 observations. The data reached an all-time high of 173.000 1966=100 in Aug 2005 and a record low of 46.000 1966=100 in Feb 2009. United States CSI: Home Values: 1 Yr Ago: Index Score data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Do you think the current value of your home--I mean, what it would bring if you sold it today--has increased compared with a year ago, has decreased compared with a year ago, or has it remained about the same?

The economic sentiment indicator (ESI) is a composite indicator produced by the Directorate General for Economic and Financial Affairs (DG ECFIN) of the European Commission. Its objective is to track GDP growth at Member states, EU and euro area levels. The ESI is a weighted average of the balances of replies to selected questions addressed to firms in five sectors covered by the EU Business and Consumer Surveys and to consumers. The sectors covered are industry (weight 40 %), services (30 %), consumers (20 %), retail (5 %) and construction (5 %). Balances are constructed as the difference between the percentages of respondents giving positive and negative replies. EU and euro-area aggregates are calculated on the basis of the national results and seasonally adjusted. The ESI is scaled to a long-term mean of 100 and a standard deviation of 10. Thus, values above 100 indicate above-average economic sentiment and vice versa. Data are seasonally adjusted (SA). Further details on the construction of the ESI can be found in the user guide of the Joint Harmonised EU Programme of Business and Consumer Surveys.

Gallup's Economic Confidence Index combines the responses of Gallup's Economic Conditions and Economic Outlook measures. Daily results are based on telephone interviews with approximately 1,500 national adults; Margin of error is ±3 percentage points.

Gallup's Economic Confidence Index is based on the combined responses to two questions, the first asking Americans to rate economic conditions in this country today, and second, whether they think economic conditions in the country as a whole are getting better or getting worse. Results are based on telephone interviews with approximately 3,500 national adults; margin of error is ±2 percentage points.

Business confidence is a sentiment indicator for the Dutch private sector, which indicates the direction in which the Dutch economy (gross domestic product, GDP) is expected to develop. When assessing the results, it can be assumed that the more optimistic or pessimistic the entrepreneurs are, the more the value of business confidence will deviate positively or negatively from the zero line and the greater the expectation is that the development of GDP will increase or decrease in the coming months. Business confidence in the total Dutch private sector is a weighted average of the confidence indicators of the underlying sectors/industries, which together form a representative reflection of the Dutch business community from a economic vieuwpoint. In addition to the quarterly series of business confidence, confidence indicators are also available on a monthly basis for the manufacturing industry and some underlying industries. These are published in a separate table under the name Producer confidence; sentiment indicator of manufacturing industry, branches. The results for the months of January, April, July and October correspond with the indicators manufacturing industry of Business confidence in quarters 1, 2, 3 and 4 respectively.

Data available from: 4th quarter 2008 - 2nd quarter 2023.

Changes as of July 27, 2023: This table has been discontinued. The reason for this is the renewed method for calculating the business confidence.

When will new figures be published? Does not apply. This table is followed by Business confidence; to sector/branches (active on August 15, 2023). See paragraph 3.

Statistics Netherlands is currently working on adjustments to the definition of the Business Confidence indicator. The aim is to improve the comparability between industries and between regional and national figures. The adjustments will come into effect when the results for the third quarter of 2023 are published. More information will follow at www.cbs.nl.

As of April 2025, the Indonesian consumer confidence index stood at 121.7 points, a slight increase compared to the previous month. The consumer confidence index score in Indonesia has fluctuated over the observed period, with the highest point recorded in May 2022.

Consumer Confidence in the United Kingdom increased to -18 points in June from -20 points in May of 2025. This dataset provides the latest reported value for - United Kingdom Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The overall consumer confidence in the economy, based on direct survey results

United States CSI: Personal: Current Financial Situation: 5Yrs Ago: Index Score data was reported at 144.000 1966=100 in Oct 2018. This records a decrease from the previous number of 152.000 1966=100 for Sep 2018. United States CSI: Personal: Current Financial Situation: 5Yrs Ago: Index Score data is updated monthly, averaging 118.000 1966=100 from Feb 1979 (Median) to Oct 2018, with 124 observations. The data reached an all-time high of 152.000 1966=100 in Sep 2018 and a record low of 83.000 1966=100 in Dec 2011. United States CSI: Personal: Current Financial Situation: 5Yrs Ago: Index Score data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s United States – Table US.H027: Consumer Sentiment Index: Personal Finance. The question was: Now thinking back 5 years, would you say you (and your family living there)are better off or worse off financially now than you were 5 years ago?

Producer confidence is a sentiment indicator for the manufacturing industry that indicates the direction in which manufacturing production is expected to develop. The indicator is an unweighted arithmetic average of three component indicators from the Business sentiment survey of the manufacturing industry. Before the Producer confidence is calculated, seasonal effects are removed from the three component indicators. The questions concern the expected activity in the next three months, opinion on order books and opinion on stocks of finished products. Results of the latter question are inverted when calculating the producer confidence, since a surplus of finished products is seen as something negative.

The more optimistic or pessimistic manufacturing companies are, the more positive or negative the value of the producer confidence indicator is compared with the zero line, and the greater the expectation that manufacturing production in the coming months will increase or decrease respectively. The producer confidence indicator has been available since 1985. For the sectors of manufacturing industry given in this table, the results are available from the start of 1994. This publication is created using co-financing by the European Commission.

Data available from: January 1985

Status of the figures: The figures are definitive.

Changes as of June 27th 2025: Figures of June 2025 have been added.

When will new figures be published? Figures of July 2025 are expected to be published the 30th of July 2025.

Brain Sentiment Indicator [version Currencies, Cryptocurrencies and Commodities] monitors public financial news for 8 currencies, more than 10 cryptocurrencies and more than 60 commodities from about 2000 financial media sources in 33 languages.

The sentiment scoring technology is based on a combination of various natural language processing techniques.

The sentiment score assigned to each stock is a value ranging from -1 (most negative) to +1 (most positive) that is updated with a daily frequency. The sentiment score corresponds to the average of sentiment for each piece of news and it is available on two time scales; 7 days and 30 days.

1. Financial news are collected every few minutes from various financial media

2. Brain engine assigns a specific category to each piece of news (e.g. “patent win” or “contract lose”) using semantic rules. Each category has a predefined value of sentiment.

3. If the categorization fails a bag of words approach is used based on dictionaries customized for Financial news. The approach includes a strategy for negation handling.

4. Repetition of similar news is kept into account in the sentiment aggregation.

The sentiment data for each piece of news is averaged on two time scales, considering the piece of news of last 7 days and of last 30 days. The data are exported daily and are available by 6.00 AM UTC on a dedicated S3 bucket..

United States Consumer Confidence Score: Investing in Future: Negative Response data was reported at 62.440 Score in Jan 2023. This records an increase from the previous number of 61.356 Score for Dec 2022. United States Consumer Confidence Score: Investing in Future: Negative Response data is updated monthly, averaging 51.347 Score from Jan 2002 (Median) to Jan 2023, with 253 observations. The data reached an all-time high of 72.400 Score in Oct 2011 and a record low of 34.000 Score in Jan 2004. United States Consumer Confidence Score: Investing in Future: Negative Response data remains active status in CEIC and is reported by Ipsos Group S.A.. The data is categorized under Global Database’s United States – Table US.IPSOS: Consumer Confidence Survey.

Singapore, Indonesia, and India had the highest consumer confidence of the ** countries as of September 2024. On the other hand, consumer confidence was lowest in Turkey and Hungary. On average, consumer confidence in the ** countries included in the survey reached an index score of ****.

Ratings are based on percent positive scores of the survey items that make up each index. The ratings provide a reliable assessment of where agencies rank on the Employee Engagement Index, Global Satisfaction Index, and Performance Confidence Index. A review of results on the various items that comprise each index provides agencies with a richer understanding of aspects of the workplace (e.g., management practices) that employees perceive as effective versus those which should be developed and improved. Subindex scores are calculated by averaging the unrounded percent positive of each of the items in the subindex. To view index ratings, select from the options below.

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In Korea, the CPI significantly impacts decisions on interest rates, fiscal policy frameworks, and the Bank of Korea’s strategies for economic stability. Given its importance, accurately forecasting the Total CPI is crucial for informed decision-making. Achieving accurate estimation, however, presents several challenges. First, the Korean Total CPI is calculated as a weighted sum of 462 items grouped into 12 categories of goods and services. This heterogeneity makes it difficult to account for all variations in consumer behavior and price dynamics. Second, the monthly frequency of CPI data results in a relatively sparse time series, limiting the performance of the analysis. Furthermore, external factors such as policy changes and pandemics add further volatility to the CPI. To address these challenges, we propose a novel framework consisting of four key components: (1) a hybrid Convolutional Neural Network-Long Short-Term Memory mechanism designed to capture complex patterns in CPI data, enhancing estimation accuracy; (2) multivariate inputs that incorporate CPI component indices alongside auxiliary variables for richer contextual information; (3) data augmentation through linear interpolation to convert monthly data into daily data, optimizing it for highly parametrized deep learning models; and (4) sentiment index derived from Korean CPI-related news articles, providing insights into external factors influencing CPI fluctuations. Experimental results demonstrate that the proposed model outperforms existing approaches in CPI prediction, as evidenced by lower RMSE values. This improved accuracy has the potential to support the development of more timely and effective economic policies.

Results of the first step PCA.