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topic_weight_full.append([word, i , weight, counter_full[word]]) |
pd.DataFrame(topic_weight_full, columns=['word', 'topic_id', 'importance', 'word_count']) |
plt.subplots(3, 3, figsize=(10,6) |
enumerate(axes.flatten() |
ax.bar(x='word', height="word_count", data=data_frame_full.loc[data_frame_full.topic_id==i, :], color=colors[i], width=0.5, alpha=0.3, label='Word Count') |
ax.twinx() |
ax_twin.bar(x='word', height="importance", data=data_frame_full.loc[data_frame_full.topic_id==i, :], color=colors[i], width=0.2, label='Weights') |
ax.set_ylabel('Word Count', color=colors[i]) |
ax_twin.set_ylim(0, 0.5) |
ax.set_ylim(0, 100) |
ax.set_title('Topic: ' + str(i+1) |
ax.tick_params(axis='y', left=False) |
ax.set_xticklabels(data_frame_full.loc[data_frame_full.topic_id==i, 'word'], rotation=90, horizontalalignment= 'center') |
ax.legend(loc='upper left') |
ax_twin.legend(loc='upper right') |
fig.tight_layout(w_pad=2) |
plt.show() |
lda_pos.show_topics(formatted=False) |
Counter(flatten_pos) |
topic_weight_pos.append([word, i , weight, counter_pos[word]]) |
pd.DataFrame(topic_weight_pos, columns=['word', 'topic_id', 'importance', 'word_count']) |
plt.subplots(3, 3, figsize=(10,6) |
enumerate(axes.flatten() |
ax.bar(x='word', height="word_count", data=data_frame_pos.loc[data_frame_pos.topic_id==i, :], color=colors[i], width=0.5, alpha=0.3, label='Word Count') |
ax.twinx() |
ax_twin.bar(x='word', height="importance", data=data_frame_pos.loc[data_frame_pos.topic_id==i, :], color=colors[i], width=0.2, label='Weights') |
ax.set_ylabel('Word Count', color=colors[i]) |
ax_twin.set_ylim(0, 0.5) |
ax.set_ylim(0, 100) |
ax.set_title('Topic: ' + str(i+1) |
ax.tick_params(axis='y', left=False) |
ax.set_xticklabels(data_frame_pos.loc[data_frame_pos.topic_id==i, 'word'], rotation=90, horizontalalignment= 'center') |
ax.legend(loc='upper left') |
ax_twin.legend(loc='upper right') |
fig.tight_layout(w_pad=2) |
plt.show() |
lda_neg.show_topics(formatted=False) |
Counter(flatten_neg) |
topic_weight_neg.append([word, i , weight, counter_neg[word]]) |
pd.DataFrame(topic_weight_neg, columns=['word', 'topic_id', 'importance', 'word_count']) |
plt.subplots(3, 3, figsize=(10,6) |
enumerate(axes.flatten() |
ax.bar(x='word', height="word_count", data=data_frame_neg.loc[data_frame_neg.topic_id==i, :], color=colors[i], width=0.5, alpha=0.3, label='Word Count') |
ax.twinx() |
ax_twin.bar(x='word', height="importance", data=data_frame_neg.loc[data_frame_neg.topic_id==i, :], color=colors[i], width=0.2, label='Weights') |
ax.set_ylabel('Word Count', color=colors[i]) |
ax_twin.set_ylim(0, 0.5) |
ax.set_ylim(0, 100) |
ax.set_title('Topic: ' + str(i+1) |
ax.tick_params(axis='y', left=False) |
ax.set_xticklabels(data_frame_neg.loc[data_frame_neg.topic_id==i, 'word'], rotation=90, horizontalalignment= 'center') |
ax.legend(loc='upper left') |
ax_twin.legend(loc='upper right') |
fig.tight_layout(w_pad=2) |
plt.show() |
WordCloud(stopwords=stoplist, background_color='white', width=2500, height=1800, max_words=7, colormap='tab10', color_func=lambda *args, **kwargs: colors[i], prefer_horizontal=1.0) |
lda_full.show_topics(formatted=False) |
plt.subplots(3, 3, figsize=(10, 6) |
enumerate(axes.flatten() |
fig.add_subplot(ax) |
dict(topics_full[i][1]) |
cloud.generate_from_frequencies(topic_words_full, max_font_size=300) |
plt.gca() |
imshow(cloud) |
plt.gca() |
set_title('Topic ' + str(i+1) |
dict(size=10) |
plt.gca() |
axis('off') |
plt.axis('off') |
plt.tight_layout() |
plt.show() |
WordCloud(stopwords=stoplist, background_color='white', width=2500, height=1800, max_words=7, colormap='tab10', color_func=lambda *args, **kwargs: colors[i], prefer_horizontal=1.0) |
lda_pos.show_topics(formatted=False) |
plt.subplots(3, 3, figsize=(10, 6) |
enumerate(axes.flatten() |
fig.add_subplot(ax) |
dict(topics_pos[i][1]) |
cloud.generate_from_frequencies(topic_words_pos, max_font_size=300) |
plt.gca() |
imshow(cloud) |
plt.gca() |
set_title('Topic ' + str(i+1) |
dict(size=10) |
plt.gca() |
axis('off') |
plt.axis('off') |
plt.tight_layout() |
plt.show() |
WordCloud(stopwords=stoplist, background_color='white', width=2500, height=1800, max_words=7, colormap='tab10', color_func=lambda *args, **kwargs: colors[i], prefer_horizontal=1.0) |
lda_neg.show_topics(formatted=False) |
plt.subplots(3, 3, figsize=(10, 6) |
enumerate(axes.flatten() |
fig.add_subplot(ax) |
dict(topics_neg[i][1]) |
cloud.generate_from_frequencies(topic_words_neg, max_font_size=300) |
plt.gca() |
imshow(cloud) |
plt.gca() |
set_title('Topic ' + str(i+1) |
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