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import matplotlib.pyplot as plt
import re
import numpy as np
from collections import defaultdict
def createGraph(resultPath):
# Initialize lists to store emotions sequentially
emotions_list = []
emotion_counts = defaultdict(int)
emotion_scores = defaultdict(float)
# Define a regular expression pattern to match the emotion and score
pattern = re.compile(r"'emotion': '(\w+)', 'score': ([\d\.]+)")
# Read and parse the file
with open(resultPath, 'r') as file:
for line in file:
match = pattern.search(line)
if match:
emotion = match.group(1)
score = float(match.group(2))
emotions_list.append(emotion)
emotion_counts[emotion] += 1
emotion_scores[emotion] += score
else:
print(f"Skipping malformed line: {line.strip()}")
# Group emotions into positive, neutral, and negative categories
emotion_group_map = {
'happy': 'Positive',
'surprise': 'Positive', # Positive emotions
'neutral': 'Neutral', # Neutral emotions
'sad': 'Negative',
'angry': 'Negative',
'fear': 'Negative',
'disgust': 'Negative' # Negative emotions
}
# Aggregate counts for positive, neutral, and negative categories
grouped_counts = defaultdict(int)
for emotion in emotions_list:
grouped_counts[emotion_group_map[emotion]] += 1
# Define the categories and their counts
categories = ['Positive', 'Neutral', 'Negative']
counts = [grouped_counts[category] for category in categories]
# **Figure 1: Bar Chart for Positive, Neutral, and Negative Emotions**
plt.figure(figsize=(8, 6))
plt.bar(categories, counts, color=['green', 'grey', 'red'], alpha=0.7)
plt.xlabel('Emotion Group')
plt.ylabel('Count')
plt.title('Distribution of Positive, Neutral, and Negative Emotions')
plt.grid(True)
# Save the bar plot
plt.savefig('data/output/emotion_bar_plot.png')
# **Figure 2: Stem Plot (unchanged from previous version)**
emotion_values = [1 if emotion_group_map[e] == 'Positive' else
0 if emotion_group_map[e] == 'Neutral' else
-1 for e in emotions_list]
# Prepare x-axis values (line numbers)
line_numbers = np.arange(1, len(emotion_values) + 1)
plt.figure(figsize=(12, 6))
plt.plot(line_numbers, emotion_values, color='blue', linewidth=2, linestyle='-', marker='o', alpha=0.7)
for i, emotion in enumerate(emotions_list):
if emotion_group_map[emotion] == 'Positive':
plt.axvspan(i + 0.5, i + 1.5, color='green', alpha=0.3)
elif emotion_group_map[emotion] == 'Neutral':
plt.axvspan(i + 0.5, i + 1.5, color='grey', alpha=0.3)
elif emotion_group_map[emotion] == 'Negative':
plt.axvspan(i + 0.5, i + 1.5, color='red', alpha=0.3)
plt.xlabel('Line Number')
plt.ylabel('Emotion Group')
plt.title('Emotion Type Across the File (Stem Plot)')
plt.ylim([-2, 2])
plt.grid(True)
plt.yticks([-1, 0, 1], ['Negative', 'Neutral', 'Positive'])
# Save the stem plot
plt.savefig('data/output/emotion_stem_plot.png')
# **Figure 3: Combination Bar and Line Chart for Counts and Scores (unchanged)**
emotions = list(emotion_counts.keys())
counts = [emotion_counts[e] for e in emotions]
average_scores = [emotion_scores[e] / emotion_counts[e] for e in emotions]
fig, ax1 = plt.subplots()
ax1.bar(emotions, counts, color='b', alpha=0.7)
ax1.set_xlabel('Emotion')
ax1.set_ylabel('Count', color='b')
ax1.tick_params(axis='y', labelcolor='b')
ax2 = ax1.twinx()
ax2.plot(emotions, average_scores, color='r', marker='o')
ax2.set_ylabel('Average Score', color='r')
ax2.tick_params(axis='y', labelcolor='r')
plt.title('Emotion Distribution and Average Scores')
plt.savefig('data/output/emotionAVG.png')
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