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| import streamlit as st | |
| import pandas as pd | |
| from transformers import pipeline | |
| import base64 | |
| import re | |
| import nltk | |
| from nltk.corpus import stopwords | |
| from nltk.tokenize import word_tokenize | |
| import plotly.express as px | |
| import matplotlib.pyplot as plt | |
| from wordcloud import WordCloud | |
| import numpy as np | |
| from PIL import ImageFont | |
| import os | |
| nltk.download('punkt') | |
| # Load pipelines | |
| sentiment_pipe = pipeline("text-classification", model="ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa") | |
| emotion_pipe = pipeline("text-classification", model="azizp128/prediksi-emosi-indobert") | |
| def load_slank_formal(file): | |
| if file.name.endswith('.txt'): | |
| df = pd.read_csv(file, sep=';', header=None, names=['Slank', 'Formal']) | |
| else: | |
| st.error("Format file tidak didukung. Harap unggah file TXT.") | |
| return None | |
| df.columns = ['Slank', 'Formal'] | |
| return df | |
| def replace_slank_to_formal(sentence, slank_formal_df): | |
| words = re.findall(r'[\w\',./:-]+|[.,]+|[^\x00-\x7F]+', sentence) | |
| for i, word in enumerate(words): | |
| replacement = slank_formal_df.loc[slank_formal_df['Slank'] == word.lower(), 'Formal'].values | |
| if replacement.size > 0: | |
| words[i] = str(replacement[0]) | |
| return ' '.join(words) | |
| def preprocess_text(text, slank_formal_df): | |
| text = text.lower() | |
| text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE) | |
| text = re.sub(r'\@\w+|\#', '', text) | |
| text = re.sub(r'[^\w\s]', '', text) | |
| text = replace_slank_to_formal(text, slank_formal_df) | |
| tokens = word_tokenize(text) | |
| preprocessed_text = ' '.join(tokens) | |
| return preprocessed_text | |
| def generate_wordcloud(text, font_path, title, colormap): | |
| wordcloud = WordCloud( | |
| width=600, | |
| height=600, | |
| background_color='white', | |
| font_path=font_path, | |
| prefer_horizontal=1.0, | |
| colormap=colormap, | |
| max_words=100 | |
| ).generate(text) | |
| plt.figure(figsize=(10, 10)) | |
| plt.title(title, fontsize=20) | |
| plt.imshow(wordcloud, interpolation='bilinear') | |
| plt.axis('off') | |
| st.pyplot(plt) | |
| # Save word cloud to file | |
| wordcloud.to_file(f"{title}.png") | |
| # Add download link for word cloud | |
| st.markdown(get_image_download_link(f"{title}.png"), unsafe_allow_html=True) | |
| def get_image_download_link(image_path): | |
| with open(image_path, "rb") as image_file: | |
| b64 = base64.b64encode(image_file.read()).decode() | |
| href = f'<a href="data:file/png;base64,{b64}" download="{image_path}">Download {image_path}</a>' | |
| return href | |
| def combined_analysis(text, slank_formal_df): | |
| texts = text.split('\n') | |
| results = [] | |
| for text in texts: | |
| if text.strip(): | |
| cleaned_text = preprocess_text(text, slank_formal_df) | |
| sentiment_result = sentiment_pipe(cleaned_text)[0] | |
| emotion_result = emotion_pipe(cleaned_text)[0] | |
| results.append((text, cleaned_text, sentiment_result['label'].lower(), sentiment_result['score'], emotion_result['label'].lower(), emotion_result['score'])) | |
| df = pd.DataFrame(results, columns=['Content', 'Cleaned Content', 'Sentiment', 'Score Sentiment', 'Emotion', 'Score Emotion']) | |
| # Sentiment pie chart | |
| sentiment_counts = df['Sentiment'].value_counts() | |
| fig_sentiment = px.pie(sentiment_counts, values=sentiment_counts.values, names=sentiment_counts.index, title='Sentiment Distribution') | |
| st.plotly_chart(fig_sentiment, use_container_width=True) | |
| # Emotion pie chart | |
| emotion_counts = df['Emotion'].value_counts() | |
| fig_emotion = px.pie(emotion_counts, values=emotion_counts.values, names=emotion_counts.index, title='Emotion Distribution') | |
| st.plotly_chart(fig_emotion, use_container_width=True) | |
| # Generate word clouds | |
| font_path = os.path.join('assets', 'Poppins-Regular.ttf') | |
| # Overall word cloud | |
| overall_text = ' '.join(df['Cleaned Content'].dropna()) | |
| generate_wordcloud(overall_text, font_path, 'Overall Word Cloud', 'viridis') | |
| # Positive sentiment and happy emotion word cloud | |
| positive_happy_text = ' '.join(df[(df['Sentiment'] == 'positive') & (df['Emotion'] == 'senang')]['Cleaned Content'].dropna()) | |
| generate_wordcloud(positive_happy_text, font_path, 'Positive Sentiment & Happy Emotion Word Cloud', 'Greens') | |
| # Negative sentiment and angry or sad emotion word cloud | |
| negative_angry_sad_text = ' '.join(df[(df['Sentiment'] == 'negative') & (df['Emotion'].isin(['marah', 'sedih']))]['Cleaned Content'].dropna()) | |
| generate_wordcloud(negative_angry_sad_text, font_path, 'Negative Sentiment & Angry or Sad Emotion Word Cloud', 'Reds') | |
| # Word frequency | |
| word_freq = pd.Series(' '.join(df['Cleaned Content'].dropna()).split()).value_counts() | |
| st.write("Word Frequency:") | |
| st.write(word_freq) | |
| # Download link for word frequency | |
| word_freq_df = word_freq.reset_index() | |
| word_freq_df.columns = ['Word', 'Frequency'] | |
| st.markdown(get_word_freq_download_link(word_freq_df), unsafe_allow_html=True) | |
| return df | |
| def process_file(file, slank_formal_df): | |
| if file.name.endswith('.xlsx'): | |
| df = pd.read_excel(file) | |
| elif file.name.endswith('.csv'): | |
| df = pd.read_csv(file) | |
| else: | |
| st.error("Format file tidak didukung. Harap unggah file CSV atau XLSX.") | |
| return None | |
| results = [] | |
| for index, row in df.iterrows(): | |
| if pd.notna(row['content']) and isinstance(row['content'], str): | |
| cleaned_text = preprocess_text(row['content'], slank_formal_df) | |
| sentiment, score_sentiment = analyze_sentiment(cleaned_text) | |
| emotion, score_emotion = analyze_emotion(cleaned_text) | |
| results.append((row['content'], cleaned_text, sentiment, score_sentiment, emotion, score_emotion)) | |
| else: | |
| results.append((row['content'], None, None, None, None, None)) | |
| df['Cleaned Content'] = [r[1] for r in results] | |
| df['Sentiment'] = [r[2] for r in results] | |
| df['Score Sentiment'] = [r[3] for r in results] | |
| df['Emotion'] = [r[4] for r in results] | |
| df['Score Emotion'] = [r[5] for r in results] | |
| # Sentiment pie chart | |
| sentiment_counts = df['Sentiment'].value_counts() | |
| fig_sentiment = px.pie(sentiment_counts, values=sentiment_counts.values, names=sentiment_counts.index, title='Sentiment Distribution') | |
| st.plotly_chart(fig_sentiment, use_container_width=True) | |
| # Emotion pie chart | |
| emotion_counts = df['Emotion'].value_counts() | |
| fig_emotion = px.pie(emotion_counts, values=emotion_counts.values, names=emotion_counts.index, title='Emotion Distribution') | |
| st.plotly_chart(fig_emotion, use_container_width=True) | |
| # Generate word clouds | |
| font_path = os.path.join('assets', 'Poppins-Regular.ttf') | |
| # Overall word cloud | |
| overall_text = ' '.join(df['Cleaned Content'].dropna()) | |
| generate_wordcloud(overall_text, font_path, 'Overall Word Cloud', 'viridis') | |
| # Positive sentiment and happy emotion word cloud | |
| positive_happy_text = ' '.join(df[(df['Sentiment'] == 'positive') & (df['Emotion'] == 'senang')]['Cleaned Content'].dropna()) | |
| generate_wordcloud(positive_happy_text, font_path, 'Positive Sentiment & Happy Emotion Word Cloud', 'Greens') | |
| # Negative sentiment and angry or sad emotion word cloud | |
| negative_angry_sad_text = ' '.join(df[(df['Sentiment'] == 'negative') & (df['Emotion'].isin(['marah', 'sedih']))]['Cleaned Content'].dropna()) | |
| generate_wordcloud(negative_angry_sad_text, font_path, 'Negative Sentiment & Angry or stSad Emotion Word Cloud', 'Reds') | |
| # Word frequency | |
| word_freq = pd.Series(' '.join(df['Cleaned Content'].dropna()).split()).value_counts() | |
| st.write("Word Frequency:") | |
| st.write(word_freq) | |
| # Download link for word frequency | |
| word_freq_df = word_freq.reset_index() | |
| word_freq_df.columns = ['Word', 'Frequency'] | |
| st.markdown(get_word_freq_download_link(word_freq_df), unsafe_allow_html=True) | |
| return df | |
| def analyze_sentiment(text): | |
| result = sentiment_pipe(text)[0] | |
| return result['label'].lower(), result['score'] | |
| def analyze_emotion(text): | |
| result = emotion_pipe(text)[0] | |
| return result['label'].lower(), result['score'] | |
| def get_download_link(df, filename): | |
| csv = df.to_csv(index=False) | |
| b64 = base64.b64encode(csv.encode()).decode() | |
| href = f'<a href="data:file/csv;base64,{b64}" download="{filename}.csv">Download CSV</a>' | |
| return href | |
| def get_word_freq_download_link(word_freq_df): | |
| csv = word_freq_df.to_csv(index=True) | |
| b64 = base64.b64encode(csv.encode()).decode() | |
| href = f'<a href="data:file/csv;base64,{b64}" download="word_frequency.csv">Download Word Frequency CSV</a>' | |
| return href | |
| def main(): | |
| st.title("Aplikasi Analisis Sentimen dan Prediksi Emosi") | |
| slank_file = st.file_uploader("Upload file slank (CSV atau TXT)", type=["csv", "txt"]) | |
| if slank_file is not None: | |
| df_slank_formal = load_slank_formal(slank_file) | |
| if df_slank_formal is None: | |
| st.stop() | |
| else: | |
| st.warning("Harap upload file slank terlebih dahulu.") | |
| st.stop() | |
| menu = st.sidebar.selectbox("Pilih Metode", ["Analisis Langsung", "Import dari File"]) | |
| if menu == "Analisis Langsung": | |
| user_input = st.text_area("Masukkan teks yang ingin dianalisis (pisahkan dengan enter):") | |
| if st.button("Analisis"): | |
| df = combined_analysis(user_input, df_slank_formal) | |
| st.write("Hasil Analisis:") | |
| st.write(df) | |
| st.markdown(get_download_link(df, "analisis_sentimen_emosi"), unsafe_allow_html=True) | |
| elif menu == "Import dari File": | |
| uploaded_file = st.file_uploader("Upload file CSV atau XLSX", type=["csv", "xlsx"]) | |
| if uploaded_file is not None: | |
| df = process_file(uploaded_file, df_slank_formal) | |
| st.write("Hasil Analisis:") | |
| st.write(df) | |
| st.markdown(get_download_link(df, "analisis_sentimen_emosi"), unsafe_allow_html=True) | |
| if __name__ == '__main__': | |
| main() | |