How to Build a Winning Tool for Sentiment Analysis of Twitter Data

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Today, we’ll summarize my continued efforts to build a sentiment analysis tool. This tool is using the Twitter API to consume 97 different Twitter handles, that provide Crypto trading signals. This method can easily be applied to traditional markets — effectively capitalizing on new and highly lucrative means for trading stocks online. We’ll be reviewing the opportunities to add a neuro evolutionary AI to our bot. You can review the first iteration of this series on Twitter sentiment bots here on my blog.

When it comes to classical issues facing sentiment analysis applications, it’s important to consider how algorithms work. If we’re using a Natural Language Processing tool to rank the individual words in a phrase based on positive, neutral and negative sentiment, wouldn’t a product review like ‘All these wonderful new features! Although none of them work!’ confuse the machine? Words like ‘none’ are the true sentiment — being negative — but the overall feeling the programming gets from the message involve words like ‘wonderful’ ‘new’ ‘work.’ This would yield a false positive in the ranking and scoring of phrases.

What we need is a way for the computer to overcome this sarcasm in the tweets. Certain AIs are capable of establishing context by ranking sentiment, emotion and personality. We’ll then apply this when we analyze Twitter data. As Chief Liquidity Officer of Coindex Labs, I have access to a…