Use Google’s Word2Vec for movie reviews Kaggle Tutorial.
From the webpage:
In this tutorial competition, we dig a little “deeper” into sentiment analysis. Google’s Word2Vec is a deep-learning inspired method that focuses on the meaning of words. Word2Vec attempts to understand meaning and semantic relationships among words. It works in a way that is similar to deep approaches, such as recurrent neural nets or deep neural nets, but is computationally more efficient. This tutorial focuses on Word2Vec for sentiment analysis.
Sentiment analysis is a challenging subject in machine learning. People express their emotions in language that is often obscured by sarcasm, ambiguity, and plays on words, all of which could be very misleading for both humans and computers. There’s another Kaggle competition for movie review sentiment analysis. In this tutorial we explore how Word2Vec can be applied to a similar problem.
Mark Needham mentions this Kaggle tutorial in Thoughts on Software Development Python NLTK/Neo4j:….
The description also mentions:
Since deep learning is a rapidly evolving field, large amounts of the work has not yet been published, or exists only as academic papers. Part 3 of the tutorial is more exploratory than prescriptive — we experiment with several ways of using Word2Vec rather than giving you a recipe for using the output.
To achieve these goals, we rely on an IMDB sentiment analysis data set, which has 100,000 multi-paragraph movie reviews, both positive and negative.
Movie, book, TV, etc., reviews are fairly common.
Where would you look for a sentiment analysis data set on contemporary U.S. criminal proceedings?