A sentiment is a personal feeling:
on a target (e.g., a named entity) in the text expressed with a single word or a phrase.
In the training data, to be able to find the sentiment, you can use polarity signals such as:
If the tweet could ever appear:
then it belongs in the neutral class. Many tweets do not have sentiment.
This approach is to use a list of positive and negative keywords. For each tweet, the number of negative keywords and positive keywords are counted. This classifier returns the polarity with the higher count.
Automatic extraction of sentiment expressions associated with given targets because the polarity of a sentiment expression is sensitive to its target.
Example with Predictable:
From Be a Text Analytics Heretic
Everybody wants sentiment analysis. At a certain level, that’s smart. Knowing how many people are mentioning your product (or any other topic) doesn’t mean much if you don’t also know something about what they are saying.
But assessing sentiment in text is a tricky business. Humans don’t agree with one another consistently when assessing sentiment of text. In fact, even a single person asked to assess the sentiment expressed in a particular bit of text on several occasions will often give different answers. It’s hard even to make a presentation on the topic, because the audience invariably gets caught up in picking over the individual cases and debating whether the assessments are acceptable. Where's the actionable insight in that?
Instead of sentiment categories, look for something better defined and more actionable in your data. Take the example of Paypal’s Han-Sheong Lai, who uses text analytics to identify customers with intent close their accounts. Does he look for broad categories of positive and negative sentiment? No. He looks for people saying things like, “I’m going to close my account.” You can bet that makes it a lot easier to accurately assess risk, and quantity results.