A survey conducted between 2022 and 2024 among consumers in the United States found that most of Yahoo! users visit the platform every day. In 2024, over 20 percent of respondents reported accessing Yahoo! services such as Yahoo Mail and Yahoo Finance daily. This represents a marginal increase compared to the usage recorded in the previous years. While approximately 40 percent of respondents reporting to have never used Yahoo! websites, daily and weekly usage remained more common than monthly access.

https://logos-world.net/wp-content/uploads/2020/04/Facebook-Logo.png" alt="Facebook">

Facebook is an American online social media and social networking service owned by Facebook, Inc.

Founded in 2004 by Mark Zuckerberg with fellow Harvard College students and roommates Eduardo Saverin, Andrew McCollum, Dustin Moskovitz, and Chris Hughes, its name comes from the face book directories often given to American university students. Membership was initially limited to Harvard students, gradually expanding to other North American universities and, since 2006, anyone over 13 years old. As of 2020, Facebook claimed 2.8 billion monthly active users, and ranked seventh in global internet usage. It was the most downloaded mobile app of the 2010s.

The dataset of INR to Dollar exchange rates from 2003 to 2024 downloaded from Yahoo Finance likely contains historical exchange rate data for the Indian Rupee (INR) against the US Dollar (USD) over the specified time period. Here's a general description of what you might find in such a dataset:

1. Date: Each entry in the dataset likely includes a date or timestamp indicating when the exchange rate was recorded.

2. Exchange Rate: The dataset should include the exchange rate value, representing the number of Indian Rupees equivalent to one US Dollar on the corresponding date.

3. Time Period: The dataset should cover exchange rate data for each trading day or a specified frequency (e.g., weekly, monthly) from 2003 to 2024.

4. Additional Information: Depending on the source and format of the dataset, it may include additional information such as opening, high, low, and closing exchange rates for each day, as well as volume and adjusted closing prices.

5. Currency Pair: The dataset focuses specifically on the exchange rate between the Indian Rupee (INR) and the US Dollar (USD), allowing users to analyze trends and fluctuations in the value of the Indian Rupee relative to the US Dollar over time.

6. The dataset of INR to Dollar exchange rates from 2003 to 2024 downloaded from Yahoo Finance likely contains historical exchange rate data for the Indian Rupee (INR) against the US Dollar (USD) over the specified time period. Here's a general description of what you might find in such a dataset:

7. Date: Each entry in the dataset likely includes a date or timestamp indicating when the exchange rate was recorded.

8. Exchange Rate: The dataset should include the exchange rate value, representing the number of Indian Rupees equivalent to one US Dollar on the corresponding date.

9. Time Period: The dataset should cover exchange rate data for each trading day or a specified frequency (e.g., weekly, monthly) from 2003 to 2024.

10. Additional Information: Depending on the source and format of the dataset, it may include additional information such as opening, high, low, and closing exchange rates for each day, as well as volume and adjusted closing prices.

11. Currency Pair: The dataset focuses specifically on the exchange rate between the Indian Rupee (INR) and the US Dollar (USD), allowing users to analyze trends and fluctuations in the value of the Indian Rupee relative to the US Dollar over time.

12. Data Quality: It's important to consider the reliability and accuracy of the data. Ensure that the dataset is sourced from a reputable financial data provider like Yahoo Finance and that any missing or erroneous data points are appropriately handled.

Overall, this dataset can be used for various analytical purposes, including trend analysis, forecasting, and risk management in the context of currency exchange markets and international finance.: It's important to consider the reliability and accuracy of the data. Ensure that the dataset is sourced from a reputable financial data provider like Yahoo Finance and that any missing or erroneous data points are appropriately handled.

Overall, this dataset can be used for various analytical purposes, including trend analysis, forecasting, and risk management in the context of currency exchange markets and international finance.

Yahoo.com was the most-visited finance-related website worldwide in 2024, with an average of ************ visits. Paypal.com was ranked second with ************* monthly visits, while tradingview.com was ranked third, with ************* average accesses.

Context

The following information can also be found at https://www.kaggle.com/davidwallach/financial-tweets. Out of curosity, I just cleaned the .csv files to perform a sentiment analysis. So both the .csv files in this dataset are created by me.

Anything you read in the description is written by David Wallach and using all this information, I happen to perform my first ever sentiment analysis.

"I have been interested in using public sentiment and journalism to gather sentiment profiles on publicly traded companies. I first developed a Python package (https://github.com/dwallach1/Stocker) that scrapes the web for articles written about companies, and then noticed the abundance of overlap with Twitter. I then developed a NodeJS project that I have been running on my RaspberryPi to monitor Twitter for all tweets coming from those mentioned in the content section. If one of them tweeted about a company in the stocks_cleaned.csv file, then it would write the tweet to the database. Currently, the file is only from earlier today, but after about a month or two, I plan to update the tweets.csv file (hopefully closer to 50,000 entries.

I am not quite sure how this dataset will be relevant, but I hope to use these tweets and try to generate some sense of public sentiment score."

Content

This dataset has all the publicly traded companies (tickers and company names) that were used as input to fill the tweets.csv. The influencers whose tweets were monitored were: ['MarketWatch', 'business', 'YahooFinance', 'TechCrunch', 'WSJ', 'Forbes', 'FT', 'TheEconomist', 'nytimes', 'Reuters', 'GerberKawasaki', 'jimcramer', 'TheStreet', 'TheStalwart', 'TruthGundlach', 'Carl_C_Icahn', 'ReformedBroker', 'benbernanke', 'bespokeinvest', 'BespokeCrypto', 'stlouisfed', 'federalreserve', 'GoldmanSachs', 'ianbremmer', 'MorganStanley', 'AswathDamodaran', 'mcuban', 'muddywatersre', 'StockTwits', 'SeanaNSmith'

Acknowledgements

The data used here is gathered from a project I developed : https://github.com/dwallach1/StockerBot

Inspiration

I hope to develop a financial sentiment text classifier that would be able to track Twitter's (and the entire public's) feelings about any publicly traded company (and cryptocurrency)