I am looking for a dataset for Mood or emotion (Happy, Angry, Sad) classification.That is to classify a text is it a happy, angry or sad related sentential text. I have used naive Bayes classification for this analysis. Now just to train and test the model with the dataset, we require a strong one. We are not getting a good efficiency with the current datasets that we are using, can you please suggest a strong one?
In Computational Linguistics this task is called Sentiment Analysis.
there is a plethora of data sets for sentiment analysis but it will always depend on the kind on text you will want to apply the trained model afterwards. So if you want to apply it to tweets, then your training data shall be annotated tweets, if you want to apply it to Wikipedia pages, then you need annotated Wikipedia pages.
For a start take a look at these sources to get sentiment analysis dataset: huge ngrams dataset from google
storage.googleapis.com/books/ngrams/books/datasetsv2.html http://www.sananalytics.com/lab/twitter-sentiment/ http://inclass.kaggle.com/c/si650winter11/data http://nlp.stanford.edu/sentiment/treebank.html https://github.com/AndbrainTeam/emotional-sensor-data-set-
or you can look into this global ML dataset repository:
play around and see what actually fits you.
you can see this link too i think its useful data set
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:
In the content attribute you can find expressions like sad, happy, angry etc.
Please refer this link: https://snap.stanford.edu/data/other.html