Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import nltk | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| import re | |
| from nltk.corpus import stopwords | |
| from nltk.stem import SnowballStemmer | |
| # Download NLTK punkt tokenizer and stopwords | |
| nltk.download('punkt') | |
| nltk.download('stopwords') | |
| # Function to preprocess text | |
| def preprocess_text(text): | |
| # Convert text to lowercase | |
| text = text.lower() | |
| # Remove URLs | |
| text = re.sub(r'http\S+', '', text) | |
| # Remove @ mentions | |
| text = re.sub(r'@\S+', '', text) | |
| # Remove hashtags | |
| text = re.sub(r'#\S+', '', text) | |
| # Remove non-alphabetic characters | |
| text = re.sub(r'[^a-zA-Z]', ' ', text) | |
| # Tokenize text | |
| tokens = nltk.word_tokenize(text) | |
| # Remove stopwords | |
| stop_words = set(stopwords.words('english')) | |
| filtered_tokens = [word for word in tokens if word not in stop_words] | |
| # Stemming | |
| stemmer = SnowballStemmer('english') | |
| stemmed_tokens = [stemmer.stem(word) for word in filtered_tokens] | |
| # Join tokens into a single string | |
| processed_text = ' '.join(stemmed_tokens) | |
| return processed_text | |
| # Function to load and preprocess DataFrame | |
| def load_and_preprocess_dataframe(file_path): | |
| dataframe = pd.read_csv(file_path) | |
| dataframe['processed_text'] = dataframe['text'].apply(preprocess_text) | |
| return dataframe | |
| # Function to create Document-Term Matrix (DTM) | |
| def create_dtm(dataframe): | |
| vectorizer = CountVectorizer() | |
| dtm = vectorizer.fit_transform(dataframe['processed_text']) | |
| return dtm, vectorizer | |
| # Function to plot word frequency | |
| def plot_word_frequency(dtm, vectorizer): | |
| # Sum word frequencies | |
| word_freq = dtm.sum(axis=0) | |
| words = vectorizer.get_feature_names_out() | |
| # Create DataFrame | |
| word_freq_df = pd.DataFrame({'word': words, 'frequency': np.ravel(word_freq)}) | |
| # Sort by frequency | |
| word_freq_df = word_freq_df.sort_values(by='frequency', ascending=False) | |
| # Plot | |
| plt.figure(figsize=(10, 6)) | |
| sns.barplot(x='word', y='frequency', data=word_freq_df.head(20)) | |
| plt.title('Top 20 Words Frequency') | |
| plt.xlabel('Words') | |
| plt.ylabel('Frequency') | |
| plt.xticks(rotation=45, ha='right') | |
| plt.tight_layout() | |
| st.pyplot() | |
| # Streamlit app | |
| def main(): | |
| st.title("Tweet Data Analysis") | |
| st.sidebar.header("Upload CSV file") | |
| uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type="csv") | |
| if uploaded_file is not None: | |
| st.sidebar.success('File uploaded successfully!') | |
| st.sidebar.markdown("### Preprocessing Options") | |
| preprocess_checkbox = st.sidebar.checkbox("Preprocess DataFrame") | |
| # Load DataFrame | |
| df = pd.read_csv(uploaded_file) | |
| if preprocess_checkbox: | |
| # Preprocess DataFrame | |
| df['processed_text'] = df['text'].apply(preprocess_text) | |
| st.subheader("Preprocessed DataFrame") | |
| st.write(df.head()) | |
| # Create DTM | |
| st.subheader("Document-Term Matrix (DTM)") | |
| dtm, vectorizer = create_dtm(df) | |
| st.write(dtm) | |
| # Plot word frequency | |
| st.subheader("Word Frequency Plot") | |
| plot_word_frequency(dtm, vectorizer) | |
| else: | |
| st.subheader("Original DataFrame") | |
| st.write(df.head()) | |
| if __name__ == "__main__": | |
| main() | |