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
Here are my two questions :
- 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?