I am working on Sentiment-Analysis/Opinion-Mining of Tweets, focused on Finance related tweets.
One of the biggest issues 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 :
Government-Bonds = Govies = 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
Here are the two sides of my question:
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?
Do you have any alternative idea regarding how to perform such task (without dataset maybe..)?