Could you explain more about what you need the data for? I'm not aware of any pre-built data sets, but you could attempt to construct your own. You'll need to break the problem into two parts though.
The easiest route to identifying the entities is the OpenCalais API, which despite its name is a closed-source service, but has generous usage limits. You can ...
The first source of raw interactions that fit your needs that comes to mind is Twitter.
NCSU's Tweet Sentiment Visualization
http://www.csc.ncsu.edu/faculty/healey/tweet_viz/tweet_app/ seems pretty good in that it allows you to enter keywords and get a graph of recent tweets graphed on axes (pleasant/unpleasant and active/inactive). It also has some pretty ...
I found NRC Word-Emotion Association Lexicon has something that I was looking for.
The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive). The annotations were manually done by crowdsourcing.
Some nice data sets for practicing sentiment classification are:
Dataset by Sanders
This one on Github
One from a Kaggle contest
These are some open datasets which contain emotions like happy, sad, etc:
Affective Sciences (Data in .sav data files)
467 million Twitter posts from 20 million users covering a 7 month period from June 1 2009 to December 31 2009. We estimate this is about 20-30% of all public tweets published on Twitter during the particular time frame.
For each public tweet the following information is available:
Please refer this link: https://snap.stanford.edu/data/...
There are 3 things that are decreasing your search results count:
The Twitter Search API only gives results about 1 week back.
Only a fraction of tweets are geo-tagged.
Search terms may be too specific.
There isn't too much to do about 1 and 2, but for 3, I can recommend getting familiar with Advanced Search. You can construct a query with the website, ...
(Example) Spanish Language Corpora:
Any of the wiki projects starting with "es" - https://dumps.wikimedia.org/backup-index.html
For example - https://dumps.wikimedia.org/eswiki/20150805/
Twitter API public stream with lang:es as stream filter - details
Affective Word list for Spanish
The Spanish ...
I have actually been working on something very similar; a search engine for restaurants in London. The search engine crawls popular social media platforms (TripAdvisor, Open Table etc.) on a daily basis and allows users to get an overall view for a given restaurant based on written comments from EVERY review.
You can also search for a particular dish / ...
Yes, please share this information with the world.
Sentiment analysis is always desired, I see plenty of questions regarding sa, as well as seeking twitter datasets on here regularly.
Don't worry about not wanting to build a site/maintain something/etc., sharing the data is entirely enough.
As far as storage/sharing, you have a plethora of options, here's ...
As with many new areas of research, this doesn't seem to be a domain with much truly open data, but you may be able to contact academic researchers about using datasets they have compiled for their work.
This W3C wiki page on sentiment analysis lists several such datasets.
For example, the Center for the Study of Emotion and Attention at University of ...
In retail domain, you can find reviews of products from e-commerce sites. Sentiment analysis on the reviews of a product can be leveraged by the manufacturer for that product lifecycle management and/or in ideation of production of similar kind of a new product in the market.
I would be tempted to use Google Finance or Yahoo Finance to get a list of stock symbols. i.e. VOD.L
Then you could use the twitter API to extract search results for each one, again i.e. $VOD.L https://twitter.com/search?q=%24VOD.L
There is a list of news APIs on Programmable Web that could perhaps be used to extract news items for each symbol: http://www....
The best one I know is this:
this is also available in MySQL format!
It's the email corpus of Enron during the moments when it collapsed. The emails are clearly characterized by underlying feelings.
This dataset is quite unique and heavily described in the podcast Linear ...
The FIRST corpus sounds like it might fit the bill. You might also look at the corpora used in similar research like Good News or Bad News? Let the Market Decide. A Google search along the lines of "sentiment analysis labeled news corpus" (without the quotes) might turn up some other relevant leads to follow.
Putting a high quality corpus together is ...
I think the simplest answer would be to look at how articles are labelled by the news aggregators.
If you go to http://newsnow.co.uk or https://news.google.com/ or http://www.moreover.com/ they aggregate news articles and then helpfully label them up to help you with sentiment.