Active Learning for Domain Adaptation in Sentiment Analysis

The code repository for the Active Learning for Domain Adaptation in Sentiment Analysis paper by Shay Palachy and Inabr Naor.

The paper (in PDF form) can be found at this link.

Type	Name	Latest commit message	Commit time
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	uHellingerGMMs
	.gitattributes
	.gitignore
	ActiveLearner.py
	AlphaRes.py
	BlitzerDatasetDomain.py
	NewSampleSelector.py
	QueryByCommitteeSampleSelector.py
	QueryByPartialDataCommiteeSampleSelector.py
	README.md
	STQBC_accuracy.pdf
	SampleSelector.py
	SentimentDistinctnessSampleSelector.py
	SentimentIntensitySampleSelector.py
	SentimentPolaritySampleSelector.py
	SentimentWordFrequencyModel.py
	TargetAndSourceQBCSampleSelector.py
	ToyDataSimulation.py
	UncertaintySampleSelector.py
	WordFrequencyModel.py
	accuracy_bar_graph.pdf
	alpha_04.pdf
	alpha_05.pdf
	alpha_06.pdf
	alpha_07.pdf
	alpha_08.pdf
	alpha_09.pdf
	alpha_1.pdf
	alpha_sim.pdf
	caseResults.xlsx
	comparative_accuracy.pdf
	countDomainLines.py
	generateSmallerBalancedFile.py
	generateTestAndTrainSet.py
	matplotlibrc
	parseDataFiles.m
	parseDataFiles.py
	parseProcessedDataFile.py
	parseProcessedDataFileForNltk.py
	parseProcessedDataFileForScikit.py
	partialQBC_accuracy.pdf
	plotInbarResults.py
	plotStuff.py
	readMatrixFromMatlab.py
	resultsFile.txt
	sentiment_distinctness_accuracy.pdf
	sentiment_intensity_accuracy.pdf
	sentiment_polarity_accuracy.pdf
	simulateGaussianDomains.py
	testActiveLearner.py
	testBlitzerDomains.py
	testParser.py
	testSentimentWordFrequencyModel.py
	testWordFrequencyModel.py
	uncertainty_accuracy.pdf

shaypal5/active_learning_for_domain_adaptation_in_sentiment_analysis