sentiment

This package contains tools for (pretty naïve) sentiment analysis.

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AnyRef, Any

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class SentimentAnalyzer extends AnyRef
An instance of this class is a "sentiment analyzer" taught on a corpus of texts whose sentiments have been appraised and annotated by a human.
An instance of this class is a "sentiment analyzer" taught on a corpus of texts whose sentiments have been appraised and annotated by a human. Given a roughly similar piece of text as those that appear in its training data, the analyzer can assign the text a numerical rating that reflects how positive or negative it is; see the rate method.
One does not simply create SentimentAnalyzer objects with new. The SentimentAnalyzer companion object provides methods for initializing analyzer objects from training data.

def cleanText(text: String): String
Cleans the given line of text for use by a SentimentAnalyzer: normalizes apostrophes and whitespace and discards unwanted punctuation.
object SentimentAnalyzer
This companion object of class SentimentAnalyzer provides methods for initializing a analyzer instances from training data.
This companion object of class SentimentAnalyzer provides methods for initializing a analyzer instances from training data.
The methods expect the data to be in the following line-based format:
```
-1 Aggressive self-glorification and a manipulative whitewash.
2 A comedy-drama of nearly epic proportions rooted in a sincere performance by the title character undergoing midlife crisis.
-1 Narratively, Trouble Every Day is a plodding mess.
1 The Importance of Being Earnest, so thick with wit it plays like a reading from Bartlett's Familiar Quotations
0 It's everything you'd expect -- but nothing more.
```
That is, each line of input should start with a sentiment rating of -2, -1, 0, 1, 2. A negative numbers means that the rest of the line expresses a negative sentiment, as determined by a human rater; positive numbers correspondingly reflect positive sentiments.

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