import tkinter as tk from tkinter import filedialog, ttk, messagebox import nltk from nltk.sentiment.vader import SentimentIntensityAnalyzer import os import pandas as pd from openpyxl import load_workbook from openpyxl.styles import PatternFill from openpyxl.chart import PieChart, Reference from openpyxl.chart.label import DataLabelList class SentimentAnalyzerApp: def __init__(self, root): self.root = root self.root.title("Sentiment Analyzer") self.root.geometry("600x400") # Verificare VADER try: nltk.data.find('vader_lexicon') except LookupError: nltk.download('vader_lexicon') ttk.Label(root, text="Select text files to analyze:").pack(pady=10) ttk.Button(root, text="Select Files", command=self.select_files).pack(pady=5) self.progress_text = tk.Text(root, height=15, width=70) self.progress_text.pack(pady=10) self.analyze_btn = ttk.Button(root, text="Start Analysis", command=self.start_analysis) self.analyze_btn.pack(pady=5) self.analyze_btn.config(state='disabled') self.selected_files = [] def select_files(self): self.selected_files = filedialog.askopenfilenames( title="Select Text Files", filetypes=[("Text files", "*.txt")], multiple=True ) if self.selected_files: self.progress_text.delete(1.0, tk.END) self.progress_text.insert(tk.END, f"Selected {len(self.selected_files)} files:\n") for file in self.selected_files: self.progress_text.insert(tk.END, f"{os.path.basename(file)}\n") self.analyze_btn.config(state='normal') def process_file(self, file_path): sid = SentimentIntensityAnalyzer() try: with open(file_path, "r", encoding='utf-8') as f: text = f.read() scores = sid.polarity_scores(text) dominant = max(scores['neg'], scores['neu'], scores['pos']) if dominant == scores['neg']: dominant_sentiment = 'Negative' elif dominant == scores['pos']: dominant_sentiment = 'Positive' else: dominant_sentiment = 'Neutral' file_name = os.path.basename(file_path) return [file_name, scores['neg'], scores['neu'], scores['pos'], scores['compound'], dominant_sentiment] except Exception as e: self.progress_text.insert(tk.END, f"Error processing {file_path}: {str(e)}\n") file_name = os.path.basename(file_path) return [file_name, 0.0, 0.0, 0.0, 0.0, 'Error'] def start_analysis(self): if not self.selected_files: return self.progress_text.insert(tk.END, "Starting analysis...\n") self.root.update() results = [] for file in self.selected_files: result = self.process_file(file) results.append(result) self.progress_text.insert(tk.END, f"Processed: {os.path.basename(file)}\n") self.root.update() # Creare DataFrame df = pd.DataFrame(results, columns=['File', 'Negative', 'Neutral', 'Positive', 'Compound', 'Dominant']) # Salvare în Excel excel_file_path = f"results_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.xlsx" df.to_excel(excel_file_path, index=False) # Formatare Excel wb = load_workbook(excel_file_path) ws = wb.active # Definire culori red_fill = PatternFill(start_color='FF9999', end_color='FF9999', fill_type='solid') # Negative yellow_fill = PatternFill(start_color='FFFF99', end_color='FFFF99', fill_type='solid') # Neutral green_fill = PatternFill(start_color='99FF99', end_color='99FF99', fill_type='solid') # Positive # Aplicare culori pe coloanele de sentiment for row in range(2, len(results) + 2): neg_cell = f'B{row}' # Negative neu_cell = f'C{row}' # Neutral pos_cell = f'D{row}' # Positive dom_cell = f'F{row}' # Dominant neg_val = float(ws[neg_cell].value) neu_val = float(ws[neu_cell].value) pos_val = float(ws[pos_cell].value) if neg_val > neu_val and neg_val > pos_val: ws[neg_cell].fill = red_fill elif neu_val > neg_val and neu_val > pos_val: ws[neu_cell].fill = yellow_fill elif pos_val > neg_val and pos_val > neu_val: ws[pos_cell].fill = green_fill dominant_value = ws[dom_cell].value if dominant_value == 'Negative': ws[dom_cell].fill = red_fill elif dominant_value == 'Positive': ws[dom_cell].fill = green_fill elif dominant_value == 'Neutral': ws[dom_cell].fill = yellow_fill # Ajustare lățime coloane for col in ws.columns: max_length = 0 column = col[0].column_letter for cell in col: try: if len(str(cell.value)) > max_length: max_length = len(str(cell.value)) except: pass adjusted_width = (max_length + 2) ws.column_dimensions[column].width = adjusted_width # Adăugare grafic pie în aceeași foaie sentiment_counts = df['Dominant'].value_counts() if not sentiment_counts.empty: # Adăugăm datele pentru grafic sub tabelul principal start_row = len(results) + 3 # Lăsăm o linie liberă după tabel ws[f'A{start_row}'] = 'Sentiment' ws[f'B{start_row}'] = 'Count' for i, (sentiment, count) in enumerate(sentiment_counts.items(), start=start_row + 1): ws[f'A{i}'] = sentiment ws[f'B{i}'] = count pie = PieChart() pie.title = "Sentiment Distribution" labels = Reference(ws, min_col=1, min_row=start_row + 1, max_row=start_row + len(sentiment_counts)) data = Reference(ws, min_col=2, min_row=start_row, max_row=start_row + len(sentiment_counts)) pie.add_data(data, titles_from_data=True) pie.set_categories(labels) pie.dataLabels = DataLabelList() # Creăm explicit obiectul DataLabelList pie.dataLabels.showPercent = True # Afișăm procentele # Poziționăm graficul sub date ws.add_chart(pie, f"H{start_row}") wb.save(excel_file_path) else: self.progress_text.insert(tk.END, "No valid sentiment data for chart!\n") wb.save(excel_file_path) # Afișare statistici self.progress_text.insert(tk.END, "\nStatistics:\n" + df.describe().to_string() + "\n") self.progress_text.insert(tk.END, f"Results saved to: {excel_file_path}\n") messagebox.showinfo("Success", "Analysis completed with chart!") def main(): root = tk.Tk() app = SentimentAnalyzerApp(root) root.mainloop() if __name__ == "__main__": main()