import json
import glob
import docx
import requests
from openai import OpenAI
import os
import openai
from docx.shared import Pt
from bs4 import BeautifulSoup
import io
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.oxml.shared import OxmlElement, qn
from docx.opc.constants import RELATIONSHIP_TYPE
openai.api_key = os.getenv(“OPENAI_API_KEY”)
NUM_OF_REVIEWS = 50
client = OpenAI()
def add_image_from_base64(doc, image_url):
response = requests.get(image_url)
# Check if the request was successful
if response.status_code == 200:
image_stream = io.BytesIO(response.content)
doc.add_picture(image_stream, width=docx.shared.Inches(6))
else:
print(f“Failed to download image. Status code: {response.status_code}”)
def html_to_word(doc, html_content):
soup = BeautifulSoup(html_content, ‘html.parser’)
for element in soup.find_all([‘strong’, ‘ul’]):
if element.name == ‘strong’:
# Add bold text as a heading
doc.add_paragraph(element.get_text().strip(), style=‘Heading 2’)
elif element.name == ‘ul’:
for item in element.find_all(‘li’):
# Add list items
doc.add_paragraph(item.get_text().strip(), style=‘List Bullet’)
def add_hyperlink(paragraph, url, text):
“””
A function that places a hyperlink within a paragraph object.
:param paragraph: The paragraph we are adding the hyperlink to.
:param url: The URL the link points to.
:param text: The text displayed for the link.
“””
part = paragraph.part
r_id = part.relate_to(url, RELATIONSHIP_TYPE.HYPERLINK, is_external=True)
hyperlink = OxmlElement(‘w:hyperlink’)
hyperlink.set(qn(‘r:id’), r_id)
new_run = OxmlElement(‘w:r’)
rPr = OxmlElement(‘w:rPr’)
# Set the style for the hyperlink (color, underline)
c = OxmlElement(‘w:color’)
c.set(qn(‘w:val’), ‘0000FF’) # Blue color
rPr.append(c)
u = OxmlElement(‘w:u’)
u.set(qn(‘w:val’), ‘single’)
rPr.append(u)
new_run.append(rPr)
new_run.text = text
hyperlink.append(new_run)
paragraph._p.append(hyperlink)
def insert_titles_in_text(text, reports):
# Placeholder for inserting the titles
placeholder = “[]”
# Extracting the titles from the reports and formatting them with new lines
titles = “\n”.join([report[‘title’] for report in reports])
# Replacing the placeholder with the titles
updated_text = text.replace(placeholder, titles)
return updated_text
def generate_article_image(name):
print(“Generating post image”)
image_url = “”
try:
response = client.images.generate(
model=“dall-e-3”,
prompt=f“Create a professional and informative cover for a Customer Sentiment Analysis report. The image should feature a diverse range of emoticons, including happy, sad, and neutral faces, symbolizing different customer emotions. Include a large, digital-style bar graph in the center, illustrating varying levels of customer satisfaction, with each bar colored according to the sentiment it represents (green for positive, red for negative, yellow for neutral). The overall color scheme should be clean and professional, with a balance of bright and muted colors to convey a sense of data-driven analysis and insight. The image must not show any text. “,
n=1,
size=“1024×1024”
)
image_url = response.data[0].url
except Exception as e:
print(“An error occurred generating the image:”, str(e))
return image_url
def call_gpt_completion(prompt):
return client.chat.completions.create(
model=“gpt-4-1106-preview”,
max_tokens=4096,
messages=[
{“role”: “user”, “content”: prompt},
]
)
def extract_points(reviews, sentiment):
print(“Extract Points: “ + sentiment)
points = []
for review in reviews:
review_text = review[‘title’] + “\n” + review[‘text’]
prompt = f“The following is a {sentiment} review of a product, summarize in one bullet point the main {sentiment} feedback:\n{review_text}”
summary = “”
try:
response = call_gpt_completion(prompt)
for choice in response.choices:
summary += choice.message.content
except Exception as e:
print(“An error occurred:”, str(e))
points.append(summary)
if len(points) == NUM_OF_REVIEWS:
break
return points
def generate_intro(product_name, product_description):
print(“Generate post intro”)
prompt = f“””
Write a paragraph introducing a customer sentiment report about:
Product name: {product_name}
Product description: {product_description}
The report is created automatically by using Webz.io eCommerce Reviews api and ChatGPT. The report is generated by calling the Webz.io eCommerce Reviews API for the reviews about {product_name}. It then splits the product reviews into positive and negative reviews. Following this step, it summarizes up to {NUM_OF_REVIEWS} reviews from both negative and positive reviews using ChatGPT to create a comprehensive list of posts. It then gives those lists to ChatGPT to create a comprehensive report highlighting both positive and negative feedback and provide a report based on the feedback.
“””
intro = “”
try:
response = call_gpt_completion(prompt)
for choice in response.choices:
intro += choice.message.content
except Exception as e:
print(“An error occurred:”, str(e))
return intro
def generate_title(product_name):
print(“Creating a title”)
prompt = “Create a title for a customer sentiment report about the following product:\n” + product_name
title_text = “”
try:
response = call_gpt_completion(prompt)
for choice in response.choices:
title_text += choice.message.content
except Exception as e:
print(“An error occurred:”, str(e))
title_text = title_text.strip(” “).strip(‘\”‘)
if title_text.startswith(“Title:”): # Sometimes ChatGPT prefix the title with Title:
return title_text[len(“Title:”):]
return title_text
def create_negative_report(feedback, product_name):
print(“Generating Negative Report”)
prompt = f“””Create a customer sentiment analysis report that includes the following sections. Use <UL> and <LI> tags for listing items and <strong> for the titles of each section.
<HTML>
<strong>Analysis of Feedback</strong>
<UL><LI>Summarize recurring and common negative issues mentioned in the reviews.</LI></UL>
<strong>Recommendations</strong>
<UL><LI>Based on the analysis, suggest actionable measures the company can take to address the issues raised in the feedback.</LI></UL>
<strong>Conclusion</strong>
<UL><LI>Summarize the key findings of the report.</LI></UL>
</HTML>
The following is the list of the negative feedback about the product you will base your report on:
{feedback}
“””
try:
response = call_gpt_completion(prompt)
report = “”
for choice in response.choices:
report += choice.message.content
except Exception as e:
print(“An error occurred:”, str(e))
return report
def create_positive_report(feedback, product_name):
print(“Generating Positive Report”)
prompt = f“””Create a customer sentiment analysis report that includes the following sections. Use <UL> and <LI> tags for listing items and <strong> for the titles of each section.
<HTML>
<strong>Detailed Analysis</strong>
<UL><LI>Summarize common positive feedback mentioned in the reviews.</LI></UL>
<strong>Recommendations </strong>
<UL><LI>Propose marketing strategies that leverage the positive aspects highlighted in the reviews. </LI></UL>
<strong>Conclusion </strong>
<UL><LI>Summarize the key findings and the overall sentiment of the customers towards the product.</LI></UL>
</HTML>
The following is the list of the positive feedback about the product you will base your report on:
{feedback}
“””
try:
response = call_gpt_completion(prompt)
report = “”
for choice in response.choices:
report += choice.message.content
except Exception as e:
print(“An error occurred:”, str(e))
return report
def create_word_doc(file_name, title_text, image_url, product_details, intro, negative_report, positive_report, one_star_count, two_star_count, three_star_count, four_star_count, five_star_count):
print(“Saving to word document”)
total_count = one_star_count + two_star_count + three_star_count + four_star_count + five_star_count
doc = docx.Document()
# Add a title
title = doc.add_paragraph()
title.style = ‘Title’
title_run = title.add_run(title_text)
title_run.font.size = Pt(24) # Set the font size
title_run.font.name = ‘Arial (Body)’ # Set the font
title.alignment = WD_ALIGN_PARAGRAPH.CENTER # Center align the title
if len(image_url) > 0:
add_image_from_base64(doc, image_url)
title = doc.add_paragraph(style=‘Heading 1’)
title_run = title.add_run(“Product Details”)
title_run.font.name = ‘Arial (Body)’ # Set the font
p = doc.add_paragraph()
add_hyperlink(p, product_details[‘url’], product_details[‘name’])
p.add_run(“\n* Price: {}\n* Total Reviews: {}\n* Aggregated Rating: {}\n* 1 Start Count: {}\n* 2 Start Count: {}\n* 3 Start Count: {}\n* 4 Start Count:{}\n* 5 Start Count: {}\n”.format(
product_details[‘price’], product_details[‘review_count’], product_details[‘aggregated_rating’], one_star_count, two_star_count, three_star_count, four_star_count, five_star_count))
title = doc.add_paragraph(style=‘Heading 1’)
title_run = title.add_run(“Introduction”)
title_run.font.name = ‘Arial (Body)’ # Set the font
doc.add_paragraph(intro)
# Positive feedback
title = doc.add_paragraph(style=‘Heading 1’)
title_run = title.add_run(“Positive Feedback Analysis”)
positive_percentage = round(100 * ((four_star_count + five_star_count) / total_count))
doc.add_paragraph(f“Around {positive_percentage}% gave the product a very positive feedback (four and five star rating). The following analysis summarizes the recurring themes and findings in the reviews for the product. “)
title_run.font.name = ‘Arial (Body)’ # Set the font
html_to_word(doc, positive_report)
# Negative feedback
title = doc.add_paragraph(style=‘Heading 1’)
title_run = title.add_run(“Negative Feedback Analysis”)
negative_percentage = round(100 * ((one_star_count + two_star_count) / total_count))
doc.add_paragraph(f“Around {negative_percentage}% gave the product a very negative feedback (one and two star rating). The following analysis summarizes the recurring themes and findings in the reviews for the product. “)
title_run.font.name = ‘Arial (Body)’ # Set the font
html_to_word(doc, negative_report)
for paragraph in doc.paragraphs:
for run in paragraph.runs:
run.font.name = ‘Arial (Body)’
# Save the document
doc.save(file_name)
def read_ndjson_file(file_path):
“””Reads an ndjson file and returns the content as a list of dictionaries.”””
with open(file_path, ‘r’, encoding=‘utf-8’) as file:
return [json.loads(line) for line in file]
def main():
# Path to the reviews folder
reviews_folder = ‘reviews’
# Read product information
product_info = read_ndjson_file(os.path.join(reviews_folder, ‘Products.ndjson’))[0]
# Initialize an empty list to store all reviews
positive_reviews = []
negative_reviews = []
# Counters for each star rating
one_star_count = 0
two_star_count = 0
three_star_count = 0
four_star_count = 0
five_star_count = 0
# Read all reviews files in the reviews folder and add those with substantial text into separate lists
for review_file in glob.glob(os.path.join(reviews_folder, ‘Reviews_*.ndjson’)):
reviews = read_ndjson_file(review_file)
for review in reviews:
if review[‘rating’] == 1:
one_star_count += 1
elif review[‘rating’] == 2:
two_star_count += 1
elif review[‘rating’] == 3:
three_star_count += 1
elif review[‘rating’] == 4:
four_star_count += 1
elif review[‘rating’] == 5:
five_star_count += 1
if len(review[‘text’]) > 100:
if review[‘rating’] <3: # 1-2 stars rating is negative
negative_reviews.append(review)
if review[‘rating’] >3: # 4-5 starts reating is positive
positive_reviews.append(review)
image_url = generate_article_image(product_info[‘name’])
title = generate_title(product_info[‘name’])
intro = generate_intro(product_info[‘name’], product_info[‘description’])
positive_bullet_points = “\n”.join(extract_points(positive_reviews, ‘positive’))
negative_bullet_points = “\n”.join(extract_points(negative_reviews, ‘negative’))
positive_report = create_positive_report(positive_bullet_points, product_info[‘name’])
negative_report = create_negative_report(negative_bullet_points, product_info[‘name’])
create_word_doc(“customer sentiment analysis report.docx”, title, image_url, product_info , intro, negative_report, positive_report,
one_star_count, two_star_count, three_star_count, four_star_count, five_star_count)
print(“done”)
if __name__ == “__main__”:
main()
