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I'm curious as to whether the training data for a sentiment analysis tool needs to be specifically geared toward the domain it's being using in. For example, the IMDB movie review data makes sense if I want to gauge sentiment on movie reviews, but would it still be okay to use if I want to gauge sentiment on what the general public thinks about a company's stock?

If I want to gauge sentiment on a company's stock, should I create my own training set that uses data from financial news/social media reaction to companies?

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One of the most fundamental assumptions in machine learning is that the training data is 'similar' to the test data. Training makes no sense otherwise.

So the question is: How similar are reviews of, say, movies and stocks? Maybe somewhat, but not too much. Your movie-trained algorithm would certainly be able to deal with statements like 'This company is awesome' or 'Warning, do not buy this stock'.

But what about 'The stock price will explode/implode' or 'Concerning this stock, I am bearish/bullish'? The words explode, implode, bearish and bullish have probably never been expressions of sentiment in movie reviews.

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If I understand your concern correctly, you want to know if you can perform sentiment analysis on sources other than imdb data (domains other than movie reviews that is). Short answer: YES!

Researchers in the past (even today) have tried to perform sentiment analysis on various aspects of life such as:

The challenge here would lie in the fact that you have to get some data for yourself if it already does not exists.

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  • $\begingroup$ Thank you for the reply and information @trollster ! I think there may be a misunderstanding though. What I was trying to ask is, can I use a model trained on the IMDB movie dataset to predict sentiment for test data that contains financial news (or any data that is not a movie review)? $\endgroup$ – aalberti333 Jan 10 '18 at 17:57

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