code stringlengths 3 6.57k |
|---|
stem_text(temp3) |
corpus_full.append(final) |
pos.iterrows() |
strip_non_alphanum(str(temp) |
strip_punctuation(temp1) |
strip_multiple_whitespaces(temp2) |
stem_text(temp3) |
corpus_pos.append(final) |
neg.iterrows() |
strip_non_alphanum(str(temp) |
strip_punctuation(temp1) |
strip_multiple_whitespaces(temp2) |
stem_text(temp3) |
corpus_neg.append(final) |
stopwords.words('english') |
split() |
print (len(stoplist) |
stoplist.update(stop_words) |
print(len(stop_words) |
print(len(stoplist) |
document.lower() |
split() |
print(text_full) |
document.lower() |
split() |
document.lower() |
split() |
defaultdict(int) |
defaultdict(int) |
defaultdict(int) |
corpora.Dictionary(corpus_removeOne_full) |
corpora.Dictionary(corpus_removeOne_pos) |
corpora.Dictionary(corpus_removeOne_neg) |
dictionary_full.save('redditTest_full.dict') |
dictionary_pos.save('redditTest_pos.dict') |
dictionary_neg.save('redditTest_neg.dict') |
gensim.corpora.Dictionary.load('redditTest.dict') |
len(text) |
corpus_removeOne_full.remove(text) |
len(text) |
corpus_removeOne_pos.remove(text) |
len(text) |
corpus_removeOne_neg.remove(text) |
dictionary_full.doc2bow(text) |
corpora.MmCorpus.serialize('redditTest_full.mm', bow_corpus_full) |
gensim.corpora.MmCorpus('redditTest_full.mm') |
models.TfidfModel(bow_corpus_full) |
dictionary_pos.doc2bow(text) |
corpora.MmCorpus.serialize('redditTest_pos.mm', bow_corpus_pos) |
gensim.corpora.MmCorpus('redditTest_pos.mm') |
models.TfidfModel(bow_corpus_pos) |
dictionary_neg.doc2bow(text) |
corpora.MmCorpus.serialize('redditTest_neg.mm', bow_corpus_neg) |
gensim.corpora.MmCorpus('redditTest_neg.mm') |
models.TfidfModel(bow_corpus_neg) |
gensim.models.wrappers.LdaMallet(mallet_path, corpus=bow_corpus_full, num_topics=9, id2word=dictionary_full, workers=1, alpha=110, random_seed=109, iterations=50) |
lda_full.print_topics(9) |
gensim.models.wrappers.LdaMallet(mallet_path, corpus=bow_corpus_pos, num_topics=9, id2word=dictionary_pos, workers=1, alpha=110, random_seed=109, iterations=50) |
lda_pos.print_topics(9) |
gensim.models.wrappers.LdaMallet(mallet_path, corpus=bow_corpus_neg, num_topics=9, id2word=dictionary_neg, workers=1, alpha=110, random_seed=109, iterations=50) |
lda_neg.print_topics(9) |
np.array([color for name, color in mcolors.TABLEAU_COLORS.items() |
np.zeros(n_topics) |
topic_weights_full.append(tmp) |
pd.DataFrame(topic_weights_full) |
fillna(9) |
np.argmax(arr_full, axis=1) |
tsne_model_full.fit_transform(arr_full) |
str.maketrans("0123456789", "₀₁₂₃₄₅₆₇₈₉") |
plt.xlabel('t-SNE1'.translate(sub) |
plt.ylabel('t-SNE2'.translate(sub) |
plt.title('t-SNE Plot of Topics within Positive Sentiment Corpus') |
plt.scatter(x=tsne_lda_full[:,0], y=tsne_lda_full[:,1]) |
plt.show(tsne_full) |
np.zeros(n_topics) |
topic_weights_pos.append(tmp) |
pd.DataFrame(topic_weights_pos) |
fillna(0) |
np.argmax(arr_pos, axis=1) |
tsne_model_pos.fit_transform(arr_pos) |
str.maketrans("0123456789", "₀₁₂₃₄₅₆₇₈₉") |
plt.xlabel('t-SNE1'.translate(sub) |
plt.ylabel('t-SNE2'.translate(sub) |
plt.title('t-SNE Plot of Topics within Positive Sentiment Corpus') |
plt.scatter(x=tsne_lda_pos[:,0], y=tsne_lda_pos[:,1]) |
plt.show(tsne_pos) |
np.zeros(n_topics) |
topic_weights_neg.append(tmp) |
pd.DataFrame(topic_weights_neg) |
fillna(0) |
np.argmax(arr_neg, axis=1) |
tsne_model_neg.fit_transform(arr_neg) |
str.maketrans("0123456789", "₀₁₂₃₄₅₆₇₈₉") |
plt.xlabel('t-SNE1'.translate(sub) |
plt.ylabel('t-SNE2'.translate(sub) |
plt.title('t-SNE Plot of Topics within Negative Sentiment Corpus') |
plt.scatter(tsne_lda_neg[:,0], tsne_lda_neg[:,1]) |
plt.show(tsne_neg) |
lda_full.show_topics(formatted=False) |
Counter(flatten_full) |
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