Difference between revisions of "NLTK: Sentiment Strength Detection in Bahasa Indonesia"
		
		
		
		
		
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Onnowpurbo (talk | contribs)  (Created page with "   ==Referensi==  * https://github.com/masdevid/SentiStrengthID")  | 
				Onnowpurbo (talk | contribs)   | 
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| + | ==SentiStrengthID==  | ||
| + | Sentiment Strength Detection in Bahasa Indonesia. This is unsupervised version of SentiStrength (http://sentistrength.wlv.ac.uk/) in Bahasa Indonesia.  | ||
| + | Core Feature:  | ||
| + | |||
| + | * Sentiment Lookup  | ||
| + | * Negation Word Lookup  | ||
| + | * Booster Word Lookup  | ||
| + | * Emoticon Lookup  | ||
| + | * Idiom Lookup  | ||
| + | * Question Word Lookup  | ||
| + | * Slang Word Lookup  | ||
| + | * Spelling Correction (optional) using Pater Norvig (http://norvig.com/spell-correct.html)  | ||
| + | * Negative emotion ignored in question  | ||
| + | * Exclamation marks count as +2  | ||
| + | * Repeated Punctuation boosts sentiment  | ||
| + | |||
| + | ==Ignored Rule:==  | ||
| + | |||
| + |     repeated letters more than 2 boosts sentiment score. This rule do not applied due to my own pre-processing rule which removing word's extra character  | ||
| + |     score +2, -2 in word "miss". Do not apply in Bahasa Indonesia.  | ||
| + | |||
| + | Warning!  | ||
| + | |||
| + | This is work in progress. Experimental for my Master Thesis  | ||
Revision as of 10:22, 25 February 2017
SentiStrengthID
Sentiment Strength Detection in Bahasa Indonesia. This is unsupervised version of SentiStrength (http://sentistrength.wlv.ac.uk/) in Bahasa Indonesia. Core Feature:
- Sentiment Lookup
 - Negation Word Lookup
 - Booster Word Lookup
 - Emoticon Lookup
 - Idiom Lookup
 - Question Word Lookup
 - Slang Word Lookup
 - Spelling Correction (optional) using Pater Norvig (http://norvig.com/spell-correct.html)
 - Negative emotion ignored in question
 - Exclamation marks count as +2
 - Repeated Punctuation boosts sentiment
 
Ignored Rule:
repeated letters more than 2 boosts sentiment score. This rule do not applied due to my own pre-processing rule which removing word's extra character score +2, -2 in word "miss". Do not apply in Bahasa Indonesia.
Warning!
This is work in progress. Experimental for my Master Thesis