NLP SentimentClassification

blog content

Reference

2006 Comparative Experiments on Sentiment Classification for Online Product Reviews. Hang Cui, Vibhu Mittal, Mayur Datar here

Data Crawler

• 1000 positive and 1000 negative processed reviews
• 25 Movies So Bad They're Unmissable
• The 50 Worst Movies Ever
• Rotten Tomatoes > Movie > On Dvd & Streaming > Browse All

To Do

• add more features

Complete

• Simple Passive-Aggressive Algorithm here
• Simple Winnow algorithm here
• Simple Language Modeling here
• Passive-Aggressive Algorithm & Winnow algorithm adjust training here

Usage

• JavaSE-1.7
• Eclipse

If you compiler error with NOT FOUND MAIN CLASS on eclipse, find menu bar Project > Clean ... and run it to rebuild /bin.

Options

prompt> java -jar NLP-SentimentClassification.jar

Usage: java -jar NLP-SentimentClassification.jar [options]

Input Options

	-path <TRAINING_PATH>	training set folder path. default <TRAINING_PATH>="training_set".

	-tpath <TEST_PATH>		user test folder path. default <TEST_PATH>="user_test".

Processing Options	
	
	-n <NGRAM_MAX>			n-grams size. default <NGRAM_MAX>="3".

	-top <FEATURE_MAX>		pick the number of feature n-grams. default <FEATURE_MAX>="40000".

	-cross <CROSS_TIMES>	default <CROSS_TIMES>="5".

	-crosspart <RATIO>		pick <RATIO> : 5 = TRAINING : OTHER, default <RATIO>="5".

	-ittime <ITCOUNT> 		training model, default <ITCOUNT>="20"

	-oittime <ITCOUNT>		online training model, default <ITCOUNT>="20"

	-ui 					call UI interface.

Sample

Sample 1

java -jar NLP-SentimentClassification.jar -n 3 -top 40000 -path training_set -tpath user_test

Sample 2

java -jar NLP-SentimentClassification.jar -n 3 -top 10000 -path training_set2 -tpath user_test

output with markdown format, and classifier will output under path/output/. For example, ouotput/neg/Adaboost.txt.

<TRAINING_PATH> folder must give the file dictionary like this

.
├── extra		// support data, like dictionary, ban list, ...
├── neg 		// negative processed data 
└── pos 		// positive processed data

<TEST_PATH> folder must give the file dictionary like this

.
├── neg 		// test data which expected negative
└── pos 		// test data which expected positive

If you want to test unknown data, put them in user_test/neg or user_test/pos folder. Program will generate result of classifier to output/neg or output/pos folder.

Good Luck.