How to Automate Customer Sentiment Analysis Reports: A Guide for Developers
If you’re a developer, especially for an automated platform company, this guide is for you. It walks you through a script we created to generate customer sentiment analysis reports automatically. Creating these reports manually takes a lot of time and energy — something few developers have. However, you can automate the report generation process with AI and some scripting. You can find links to download the script file and related materials at the bottom of this guide.
What you’ll need to run the script
- Customer Reviews Data — You should obtain customer reviews data from a reliable source. For this guide, we’re getting the data from the Webz.io eCommerce Reviews API. It provides product information and customer reviews from 900+ eCommerce and marketplace sources.
- You need an API key to use the Webz.io eCommerce Reviews API, and you can get one by contacting Webz.io. This guide includes a free NDJSON file with sample reviews data if you would like to experiment with the script without using the API.
- OpenAI API — You’ll use OpenAI’s API to leverage the GPT-4 and DALL·E models. GPT-4 analyzes and summarizes the text from customer reviews, while DALL·E generates a main image for the report.
- You also need an API key for the OpenAI API. Create an account or sign in at OpenAI to get a key. OpenAI uses pay-per-use pricing for its language and image models. You can see the price points on the OpenAI website.
- Python — We’re using Python to automate the report creation process. You’ll need to ensure you can run Python code on your machine.
Automating customer sentiment analysis reports: script breakdown
The script fetches product reviews from an external NDJSON file generated by the Webz.io eCommerce Reviews API. Next, the script calls the OpenAI API, using its text and image models to analyze the reviews for positive and negative sentiments. It then compiles these findings into a structured report, outputting them into a Word document. The document contains the completed customer sentiment analysis report.
Here is the detailed breakdown of the script:
Import files, packages, and modules
First, the script imports the NDJSON file, Python packages and modules, the OpenAI Python API library, and other necessary files.
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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 |
Set global variable and access API key
Next, the script accesses the Open AI API key through the development environment. It also includes a global variable where you can set the number of reviews included in the report.
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openai.api_key = os.getenv("OPENAI_API_KEY") NUM_OF_REVIEWS = 50 client = OpenAI() |
Orchestrate entire process (main)
Towards the end of the script, you’ll see the “main” function. It orchestrates the entire process — from reading the reviews and generating a report main image to generating the report text and creating the final Word document.
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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() |
Define functions
Now we define the different functions of our script:
Read NDJSON file (read_ndjson_file)
Reads a NDJSON file and returns its content.
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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] |
Send prompt (call_gpt_completion)
Sends a prompt to the GPT-4 model and receives a response.
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def call_gpt_completion(prompt): return client.chat.completions.create( model="gpt-4-1106-preview", max_tokens=4096, messages=[ {"role": "user", "content": prompt}, ] ) |
Extract points (extract_points)
Extracts key points from reviews based on sentiment (positive/negative).
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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 |
Generate title (generate_title)
Creates a title for the sentiment analysis report.
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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 |
Generate introduction (generate_intro)
Generates an introductory paragraph for the report.
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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 api and ChatGPT. The report is generated by calling the Webz.io eCommerce 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 |
Generate negative report (create_negative_report)
Creates a report based on negative feedback.
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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 |
Generate positive report (create_positive_report)
Creates a report based on positive feedback.
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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 |
HTML to formatted text (html_to_word)
Converts HTML content to formatted text in a Word document. It handles bold text and bullet lists.
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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') |
Add hyperlink (add_hyperlink)
Inserts a hyperlink into a Word document paragraph.
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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) |
Add title placeholder (insert_titles_in_text)
Adds a placeholder for inserting the titles.
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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 |
Generate image (generate_article_image)
Generates a main image for the report using OpenAI’s DALL-E model with a specific prompt.
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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="1024x1024" ) image_url = response.data[0].url except Exception as e: print("An error occurred generating the image:", str(e)) return image_url |
Download image (add_image_from_base64)
Downloads an image from a URL and adds it to a Word document.
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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}") |
Create Word doc (create_word_doc)
Assembles the various components into a formatted Word document.
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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) |
AI + Python = a powerful automation tool
This script demonstrates an advanced use case of integrating AI-powered content generation with document automation in Python. It’s a comprehensive example of how combining various Python libraries with AI models can produce a powerful automation tool.
Download the example code and files:
- The full Python script
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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="1024x1024" ) 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() |
- Free sample data (NDJSON format): Product dataset, Review dataset #1, Review dataset #2, Review dataset #3, Review dataset #4
- Example of an auto-generated report in PDF format.
To run the script:
- Ensure that Python and the required Python libraries are installed on your machine.=
- Set your OpenAI API key in your development environment.
- Place the NDJSON file with the product and review information in the specified directory.
- Run the script.
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