def test_classic_lm():
train_pos = FileCorpusSource(data.LM_TRAIN_POSITIVE).entries()
train_neg = FileCorpusSource(data.LM_TRAIN_NEGATIVE).entries()
test_pos = FileCorpusSource(data.LM_TEST_POSITIVE).entries()
test_neg = FileCorpusSource(data.LM_TEST_NEGATIVE).entries()
run_classic(
train_pos,
train_neg,
test_pos,
test_neg,
verbose=False
)
def test_classic_lm_filtered():
train_pos = filter_lines(FileCorpusSource(data.LM_TRAIN_POSITIVE).entries(), positive_emoji)
train_neg = filter_lines(FileCorpusSource(data.LM_TRAIN_NEGATIVE).entries(), negative_emoji)
test_pos = filter_lines(FileCorpusSource(data.LM_TEST_POSITIVE).entries(), positive_emoji)
test_neg = filter_lines(FileCorpusSource(data.LM_TEST_NEGATIVE).entries(), negative_emoji)
run_classic(
train_pos,
train_neg,
test_pos,
test_neg,
verbose=False
)
def test_classic_rt():
positive_training_corpus = itertools.chain(*[
FileCorpusSource(data.RT_CORPUS_FILE_POSITIVE).entries(),
FileCorpusSource(data.BIASED_FILE_POSITIVE).entries(),
])
negative_training_corpus = itertools.chain(*[
FileCorpusSource(data.RT_CORPUS_FILE_NEGATIVE).entries(),
FileCorpusSource(data.BIASED_FILE_NEGATIVE).entries(),
])
positive_test_corpus = FileCorpusSource(data.TEST_FILE_POSITIVE).entries()
negative_test_corpus = FileCorpusSource(data.TEST_FILE_NEGATIVE).entries()
run_classic(
positive_training_corpus,
negative_training_corpus,
positive_test_corpus,
negative_test_corpus,
verbose=False
)