-
Notifications
You must be signed in to change notification settings - Fork 0
/
comment_analyzer.py
174 lines (152 loc) · 7.02 KB
/
comment_analyzer.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import sys
import os
import textwrap
from datetime import datetime
import logging
from docx import Document as DocxDocument
from PyPDF2 import PdfReader
from odf.opendocument import load
from odf import text as odf_text
from ai_discussion_analyzer import analyze_discussion, start_stanford_server, stop_stanford_server
import config
from sentiment_benchmark import benchmark_sentiment_analysis
# Set up logging
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
def read_file(file_path):
_, file_extension = os.path.splitext(file_path)
content = ""
try:
logging.debug(f"Attempting to read file: {file_path}")
if file_extension.lower() in ['.txt', '.md']:
with open(file_path, 'r', encoding='utf-8') as file:
content = file.read()
elif file_extension.lower() == '.docx':
doc = DocxDocument(file_path)
content = '\n'.join([paragraph.text for paragraph in doc.paragraphs])
elif file_extension.lower() == '.pdf':
reader = PdfReader(file_path)
content = '\n'.join([page.extract_text() for page in reader.pages if page.extract_text() is not None])
elif file_extension.lower() == '.odt':
doc = load(file_path)
all_paragraphs = doc.getElementsByType(odf_text.P)
content = '\n'.join([para.firstChild.data for para in all_paragraphs if para.firstChild is not None])
else:
raise ValueError(f"Unsupported file type: {file_extension}")
logging.debug(f"File read successfully. Content length: {len(content)}")
except Exception as e:
logging.error(f"Error reading file {file_path}: {str(e)}")
raise
return content
def print_wrapped(text, width=100):
for line in text.split('\n'):
print('\n'.join(textwrap.wrap(line, width=width)))
def interpret_sentiment(score):
if score <= 0.5:
return "Very Negative"
elif score <= 1.5:
return "Negative"
elif score <= 2.5:
return "Neutral"
elif score <= 3.5:
return "Positive"
else:
return "Very Positive"
def interpret_engagement(score):
if score >= 80:
return "Very High Engagement"
elif score >= 60:
return "High Engagement"
elif score >= 40:
return "Moderate Engagement"
elif score >= 20:
return "Low Engagement"
else:
return "Very Low Engagement"
def save_analysis_result(analysis_result, file_path):
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_filename = f"analysis_result_{timestamp}.md"
output_path = os.path.join(config.OUTPUT_PATH, output_filename)
with open(output_path, 'w', encoding='utf-8') as f:
f.write(f"# Analysis Result for {os.path.basename(file_path)}\n\n")
f.write(f"Analysis performed on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
# Sentiment Analysis
sa = analysis_result.get('sentiment_analysis', {})
f.write("## Sentiment Analysis\n\n")
f.write(f"- Overall Sentiment Score: {sa.get('score', 0):.2f}\n")
f.write(f"- Interpretation: {interpret_sentiment(sa.get('score', 0))}\n")
f.write(f"- Stanford Sentiment: {sa.get('stanford_sentiment', 'N/A'):.2f}\n")
f.write(f"- Sentence Sentiments: {sa.get('sentence_sentiments', 'N/A')}\n\n")
# Engagement Analysis
ea = analysis_result.get('engagement_analysis', {})
f.write("## Engagement Analysis\n\n")
f.write(f"- Overall Engagement: {ea.get('score', 'N/A'):.1f}/100\n")
f.write(f"- Interpretation: {interpret_engagement(ea.get('score', 0))}\n")
f.write(f"- Raw Engagement Score: {ea.get('raw_score', 'N/A'):.2f}\n")
f.write(f"- Text Length: {ea.get('text_length', 'N/A')} words\n")
f.write(f"- Likes: {ea.get('likes', 'N/A')}\n")
f.write(f"- Dislikes: {ea.get('dislikes', 'N/A')}\n")
f.write(f"- Replies: {ea.get('replies', 'N/A')}\n")
f.write(f"- Questions: {ea.get('questions', 'N/A')}\n")
f.write(f"- Exclamations: {ea.get('exclamations', 'N/A')}\n\n")
# AI Analysis
f.write("## AI Analysis\n\n")
f.write(analysis_result.get('ai_analysis', 'N/A'))
print(f"Analysis result saved to: {output_path}")
def main(file_path):
try:
logging.debug("Starting analysis")
start_stanford_server()
logging.debug("Stanford server started")
content = read_file(file_path)
logging.debug(f"File read, content length: {len(content)}")
if len(content) > config.MAX_CONTENT_LENGTH:
content = content[:config.MAX_CONTENT_LENGTH]
logging.debug(f"Content truncated to {config.MAX_CONTENT_LENGTH} characters")
logging.debug(f"Analyzing file: {file_path}")
logging.debug(f"Content (first 100 characters): {content[:100]}...")
logging.debug("Performing analysis...")
analysis_result = analyze_discussion(content)
if 'error' in analysis_result:
logging.error(f"Error in analysis: {analysis_result['error']}")
else:
logging.debug("Analysis completed successfully")
# Print results to console
print("\nSentiment Analysis:")
sa = analysis_result['sentiment_analysis']
print(f"Overall Sentiment Score: {sa['score']:.2f}")
print(f"Interpretation: {interpret_sentiment(sa['score'])}")
print(f"Stanford Sentiment: {sa['stanford_sentiment']:.2f}")
print("Sentence Sentiments:")
for i, sentiment in enumerate(sa['sentence_sentiments']):
print(f" Sentence {i+1}: {sentiment:.2f}")
print("\nEngagement Analysis:")
ea = analysis_result['engagement_analysis']
print(f"Overall Engagement: {ea['score']:.1f}/100")
print(f"Interpretation: {interpret_engagement(ea['score'])}")
print(f"Raw Engagement Score: {ea['raw_score']:.2f}")
print(f"Text Length: {ea['text_length']} words")
print(f"Likes: {ea['likes']}")
print(f"Dislikes: {ea['dislikes']}")
print(f"Replies: {ea['replies']}")
print(f"Questions: {ea['questions']}")
print(f"Exclamations: {ea['exclamations']}")
print("\nAI Analysis:")
print_wrapped(analysis_result['ai_analysis'], width=100)
# Save analysis result to file
save_analysis_result(analysis_result, file_path)
except Exception as e:
logging.exception(f"Error processing file: {str(e)}")
finally:
stop_stanford_server()
if __name__ == "__main__":
if len(sys.argv) == 2 and sys.argv[1] == "--benchmark":
data_dir = "Data/stanfordSentimentTreebank"
start_stanford_server()
try:
benchmark_sentiment_analysis(data_dir)
finally:
stop_stanford_server()
elif len(sys.argv) == 2:
main(sys.argv[1])
else:
print(f"Usage: python {sys.argv[0]} <path_to_file> or --benchmark")