Code for the Active Learning for Domain Adaptation in Sentiment Analysis paper by Shay Palachy and Inbar Naor
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uHellingerGMMs
.gitattributes
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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

README.md

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.