Financial Slang and NLP for Sentiment Analysis

Question

I am working on a Sentiment-Analysis/Opinion-Mining of Tweets, focused on Finance related tweets.

One of the biggest issue I am facing is the unability of my algorithm to detect equivalent entities (Definition in B.Liu 2012: Page 18-19) when Financial slang is used. For example, for those familiar with it I would like the following entities to be detected as equivalent after lemmatization :

• Govies = Government-Bonds = Sovereign-Debt
• Cash = Monetary
• Stocks = Equities
• FX = Forex = Currency-exchange = Foreign-Exchange
• Bund = German-Bonds = Bundesbank 10y
• T-Notes = US10 = Treasury-Notes = US-Govies = American-Sovereign-Debt
• Etc...

Here are my two questions :

1. I was thinking about using some supervised learning (Naive-Bayesian-Classification) for such task, but can't find any classified set of data for training. Do you know if such dataset exists?
2. Do you have any alternative idea regarding how to perform such task?

Thanks.

Answer 1

The best answer was provided on a similar question on 'Cross-Validated',recommending to use the word2vec algorithm.

I obtained great results by fitting the model on a very large dataset of financial tweets and specialized financial-newspapers articles.

• Link to the answer: https://stats.stackexchange.com/questions/228291/financial-slang-and-nlp-for-sentiment-analysis

• And for the curious ones, here's a great video explaining word2vect, from the not less great CS224D stanford class on machine-learning applied to NLP: https://www.youtube.com/watch?v=aRqn8t1hLxs