Consumer Confidence in the United States decreased to 51 points in November from 53.60 points in October 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.

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)

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

United States - University of Michigan: Consumer Sentiment was 55.10000 Index 1966:Q1=100 in September of 2025, according to the United States Federal Reserve. Historically, United States - University of Michigan: Consumer Sentiment reached a record high of 112.00000 in January of 2000 and a record low of 50.00000 in June of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - University of Michigan: Consumer Sentiment - last updated from the United States Federal Reserve on December of 2025.

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Business Investment And Sentiment (EMVMACROBUS) from Jan 1985 to Oct 2025 about volatility, uncertainty, equity, investment, business, and USA.

“Acc” represents accuracy, “F1” represents Macro-F1 score. The best results are shown in bold and second best underlined. The experimental results of other models are partly from the original paper and partly verified through reproducing the open-source code.

Context:

Sentiment analysis, the task of automatically detecting whether a piece of text is positive or negative, generally relies on a hand-curated list of words with positive sentiment (good, great, awesome) and negative sentiment (bad, gross, awful). This dataset contains both positive and negative sentiment lexicons for 81 languages.

Content:

The sentiment lexicons in this dataset were generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them. The general intuition is that words which are closely linked on a knowledge graph probably have similar sentiment polarities. For this project, sentiments were generated based on English sentiment lexicons.

This dataset contains sentiment lexicons for the following languages:

• Afrikaans
• Albanian
• Arabic
• Aragonese
• Armenian
• Azerbaijani
• Basque
• Belarusian
• Bengali
• Bosnian
• Breton
• Bulgarian
• Catalan
• Chinese
• Croatian
• Czech
• Danish
• Dutch
• Esperanto
• Estonian
• Faroese
• Finnish
• French
• Galician
• Georgian
• German
• Greek
• Gujarati
• Haitian Creole
• Hebrew
• Hindi
• Hungarian
• Icelandic
• Ido
• Indonesian
• Interlingua
• Irish
• Italian
• Kannada
• Khmer
• Kirghiz
• Korean
• Kurdish
• Latin
• Latvian
• Lithuanian
• Luxembourgish
• Macedonian
• Malay
• Maltese
• Marathi
• Norwegian
• Norwegian
• Persian
• Polish
• Portuguese
• Romanian
• Romansh
• Russian
• Scottish
• Serbian
• Slovak
• Slovene
• Spanish
• Swahili
• Swedish
• Tagalog
• Tamil
• Telugu
• Thai
• Turkish
• Turkmen
• Ukrainian
• Urdu
• Uzbek
• Vietnamese
• Volapük
• Walloon
• Welsh
• Western Frisian

• Yiddish

  For more information and additional sentiment lexicons, please visit the project’s website.

Acknowledgements:

This dataset was collected by Yanqing Chen and Steven Skiena. If you use it in your work, please cite the following paper:

Chen, Y., & Skiena, S. (2014). Building Sentiment Lexicons for All Major Languages. In ACL (2) (pp. 383-389).

It is distributed here under the GNU General Public License. Note that this is the full GPL, which allows many free uses, but does not allow its incorporation into any type of distributed proprietary software, even in part or in translation. For commercial applications please contact the dataset creators.

Inspiration:

• These word lists contain many words with similar meanings. Can you automatically detect which words are cognates?
• Can you use these sentiment lexicons to reverse-engineer the knowledge graphs that generated them?

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.

Germany - Economic sentiment indicator was 91.30% in November of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Germany - Economic sentiment indicator - last updated from the EUROSTAT on December of 2025. Historically, Germany - Economic sentiment indicator reached a record high of 117.20% in September of 2021 and a record low of 86.80% in December of 2024.

This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis Sources: Above 140k articles (01.2016-03.2019): Gigaom 0.5% Euractiv 0.9% The Conversation 1.3% Politico Europe 1.3% IEEE Spectrum 1.8% Techforge 4.3% Fastcompany 4.5% The Guardian (Tech) 9.2% Arstechnica 10.0% Reuters 11% Gizmodo 17.5% ZDNet 18.3% The Register 19.5% Methodology The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers. As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise. The process included the following main steps: The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology) The articles containing the given social issue and co-occurring term are identified The identified articles are divided into paragraphs Social issue and co-occurring words are removed from the paragraph The VADER sentiment analysis is carried out for every identified and modified paragraph The average for the given word pair is calculated for the final result Therefore, the procedure has been repeated for 100 words for all identified social issues. The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down. The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way: The articles containing the given social issue are identified The paragraphs containing the social issue are selected for sentiment analysis *Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. Files sentiments_mod11.csv sentiment score based on chosen unigrams sentiments_mod22.csv sentiment score based on chosen bigrams sentiments_cooc_mod11.csv, sentiments_cooc_mod12.csv, sentiments_cooc_mod21.csv, sentiments_cooc_mod22.csv combinations of co-occurrences: unigrams-unigrams, unigrams-bigrams, bigrams-unigrams, bigrams-bigrams

This bar chart displays polarity sentiment score by news using the aggregation average. The data is filtered where the keywords includes University of Akron.

We implement the calculation of cosine similarity using the sklearn package [45].

This horizontal bar chart displays polarity sentiment score by classification using the aggregation average. The data is filtered where the keywords includes Slovenia.

This bar chart displays polarity sentiment score by news title using the aggregation average. The data is filtered where the keywords includes Sexual orientation-Political aspects-United States and the section is science.

Graph and download economic data for University of Michigan: Consumer Sentiment (DISCONTINUED) (UMCSENT1) from 1952-11-01 to 1977-11-01 about consumer sentiment, consumer, and USA.

View weekly updates and historical trends for US Investor Sentiment, % Bullish. from United States. Source: The American Association of Individual Investo…

Graph and download economic data for CSBS Community Bank Sentiment Index (CBSICO) from Q2 2019 to Q3 2025 about community, business sentiment, banks, depository institutions, indexes, and USA.

This horizontal bar chart displays polarity sentiment score by keywords using the aggregation average. The data is filtered where the entities includes universities and the keywords includes Ireland.

This bar chart displays polarity sentiment score by keywords using the aggregation average. The data is filtered where the keywords includes Colombia.