Here is what I was looking for: http://untroubled.org/spam/ This archive has around a gigabyte of compressed accumulated spam messages dating 1998 – 2011. Now I just need to get non-spam email. So I’ll just query my own Gmail for that using the getmail program and the tutorial at mattcutts.com
https://archive.ics.uci.edu/ml is the University of California Irvine repository of machine learning datasets. It’s a really great resource, and I believe that they are all in CSV files.
http://www.cs.cornell.edu/home/llee/data/ http://mpqa.cs.pitt.edu/corpora/mpqa_corpus You can use twitter, with its smileys, like this: http://web.archive.org/web/20111119181304/http://deepthoughtinc.com/wp-content/uploads/2011/01/Twitter-as-a-Corpus-for-Sentiment-Analysis-and-Opinion-Mining.pdf Hope that gets you started. There’s more in the literature, if you’re interested in specific subtasks like negation, sentiment scope, etc. To get a focus on companies, you might pair a method with topic detection, or cheaply just a lot of mentions of … Read more
You first need to split the data into training and test set (validation set could be useful too). Don’t forget that testing data points represent real-world data. Feature normalization (or data standardization) of the explanatory (or predictor) variables is a technique used to center and normalise the data by subtracting the mean and dividing by … Read more
Funny nobody told you that maybe you lack of management skills. Once, I ended up working with people not being able to code a loop after a year and a half of training. I trained them, until they were able to use a full feature web framework, and it took only one month. Maybe you … Read more
If you want to build a solid model you have to follow that specific protocol of splitting your data into three sets: One for training, one for validation and one for final evaluation, which is the test set. The idea is that you train on your training data and tune your model with the results … Read more
This stratify parameter makes a split so that the proportion of values in the sample produced will be the same as the proportion of values provided to parameter stratify. For example, if variable y is a binary categorical variable with values 0 and 1 and there are 25% of zeros and 75% of ones, stratify=y … Read more