February 2018
Beginner to intermediate
258 pages
5h 47m
English
Content preview from R Deep Learning Projects
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Preparing the data
The data is a subset of the Stanford Large Movie Review dataset, originally published in:
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).
This data is available to download at http://ai.stanford.edu/~amaas/data/sentiment/, provided proper credit is given to the original paper. This is the raw data, but you can find a preprocessed version in Kaggle, https://www.kaggle.com/c/word2vec-nlp-tutorial/data.
Let's begin with loading the data:
df <- read.csv("./data/labeledTrainData.tsv", encoding = "utf-8", quote = "", sep="\t", stringsAsFactors ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.