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| import matplotlib as mpl | |
| import matplotlib.pyplot as plt | |
| import nltk | |
| import re | |
| import streamlit as st | |
| from nltk.corpus import stopwords | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from yellowbrick.text import FreqDistVisualizer | |
| nltk.download('stopwords') | |
| mpl.rcParams["figure.dpi"] = 300 | |
| plt.rcParams.update({'font.size': 25}) | |
| mpl.rcParams['xtick.labelsize']=18 | |
| mpl.rcParams['ytick.labelsize']=18 | |
| # must be called as first command | |
| try: | |
| st.set_page_config(layout="wide") | |
| except: | |
| st.beta_set_page_config(layout="wide") | |
| #stop-words list | |
| stop_words = stopwords.words("english") | |
| news_stopwords = [ | |
| "would", | |
| "also", | |
| "may", | |
| "even", | |
| "like", | |
| "way", | |
| "year", | |
| "years", | |
| "one", | |
| "many", | |
| "us", | |
| "view", | |
| "singapore", | |
| "mr", | |
| "mrs", | |
| "ms", | |
| "madam", | |
| "madame", | |
| "prosperity", | |
| "every", | |
| "day", | |
| "https", | |
| "www", | |
| "channelnewsasia", | |
| "read", | |
| "today", | |
| "todayonline", | |
| "com", | |
| "news", | |
| "say", | |
| "said", | |
| "add", | |
| "added" | |
| ] | |
| stop_words.extend(news_stopwords) | |
| #text-cleaning function | |
| def clean_text(text): | |
| text = text.encode("ascii", errors="ignore").decode("ascii") #remove non-ascii, Chinese characters | |
| text = text.lower() | |
| text = re.sub(r"http\S+", "", text) | |
| text = re.sub(r"\n", " ", text) | |
| text = re.sub(r"\n\n", " ", text) | |
| text = re.sub(r"\t", " ", text) | |
| text = re.sub(r"\W", " ", text) | |
| text = re.sub(r"[^\w\s]", "", text) | |
| text = re.sub(r"^\d+\s|\s\d+\s|\s\d+$", " ", text) | |
| text = text.strip(" ") | |
| text = re.sub(' +',' ', text).strip() # get rid of multiple spaces and replace with a single | |
| return text | |
| st.title("Word Frequency Counter") | |
| user_input = st.text_area( | |
| "Paste selected text here", | |
| height=250, | |
| ) | |
| ngram_select = st.slider("N-gram Range, or length of consecutive words", min_value=1, max_value=3, value=(1, 3)) | |
| st.markdown( | |
| """ | |
| ---------------------------- | |
| """ | |
| ) | |
| if user_input != "": | |
| with st.spinner("..."): | |
| input_text = clean_text(user_input) | |
| vect = CountVectorizer( | |
| stop_words=stop_words, | |
| min_df=1, | |
| ngram_range=(ngram_select[0], ngram_select[1]), | |
| ) | |
| docs = vect.fit_transform([input_text]) | |
| features = vect.get_feature_names() | |
| convo_title = " " | |
| fig, ax = plt.subplots(figsize=(20, 12)) | |
| fig.suptitle( | |
| "10 Most Frequently Used Word(s)", | |
| fontsize=25, | |
| ) | |
| ax.legend().set_visible(False) | |
| visualiser = FreqDistVisualizer( | |
| features=features, n=10, color="steelblue", title=convo_title | |
| ) | |
| visualiser.fit(docs) | |
| for item in ( | |
| [ax.title, ax.xaxis.label, ax.yaxis.label] | |
| + ax.get_xticklabels() | |
| + ax.get_yticklabels() | |
| ): | |
| item.set_fontsize(18) | |
| st.pyplot(fig) | |
| st.markdown( | |
| """ | |
| ---------------------------- | |
| """ | |
| ) | |
| st.subheader("How To Use:") | |
| st.write("1. This app works only for English text.") | |
| st.write( | |
| "2a. By adjusting the N-gram range, you can discover the most frequently appearing single word, 2 or 3-word pairings (bigrams/trigrams)." | |
| ) | |
| st.write( | |
| "2b. Examples: To find the most frequently appearing single word, move the yellow dot on '3' to '1'. For the most frequently appearing 2-word pairing, move the yellow dots on both ends to the centre, or the '2' position." | |
| ) | |
| st.write( | |
| "3. Routine words and honorifics will be filtered out automatically in the results." | |
| ) |