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AI Generated ContentThis document was generated by an AI Agent on Teammately. When developing AI on Teammately, you can also generate, share and publish documents like this.Sentiment Analysis for Product Reviews
About this AI
Summary
This AI, dubbed the Sentiment Insight Extractor, is designed to transform unstructured product reviews into structured sentiment data using existing large language models (LLMs). It accurately interprets customer feedback and automates the extraction of sentiment insights, crucial for businesses seeking informed decision-making and strategic development. The AI application achieves this by engineering effective prompts to extract and analyze sentiment data, ensuring output reliability and validity. Through review summarization and sentiment mapping, it offers a scalable solution for interpreting vast volumes of customer reviews with precision and efficiency.
Major Use Cases
Review Summarization: Provides concise insights from lengthy reviews.
Sentiment Mapping: Converts text reviews to sentiment scores.
Feedback Analysis: Identifies prevalent sentiments in product categories.
Decision Support: Aids businesses in strategy adjustments.
Trend Identification: Detects changes in customer sentiment over time.
Milestone
PRD Drafting: We have completed drafting the Product Requirements Document (PRD), outlining objectives, major use cases, and evaluation metrics.
AI Development: We have developed the AI architecture and logic using LLMs to perform review summarization and sentiment mapping.
Quick Testing: We have conducted successful quick tests to ensure system robustness and accuracy, with all evaluation metrics meeting expectations.
Report Generation: We have generated the comprehensive documentation and report to articulate AI capabilities and integration potential.
AI Architecture & Logic Plans
AI Plans
API INPUT KEYS
product_review_textText
STEPS
Review Summarization
Model
openai / gpt-4oPrompt
## Role: Review Summarization Agent
Your task is to condense product reviews into concise summaries, focusing on capturing the core sentiments and key attributes mentioned by the reviewer, while leaving out unnecessary details. Ensure the summary is brief and highlights essential information like product quality, satisfaction, and specific attributes.
## Summary Instruction
Summarize the review provided with an emphasis on the main sentiments and attributes such as 'quality' and 'satisfaction.'
## Key Points to Capture
- Focus on core sentiments expressed by the reviewer.
- Highlight any specific product attributes mentioned, like quality and satisfaction.
- Retain critical insights that are necessary for understanding the essence of the review.
## Avoided Details
- Do not include redundant information or minor details irrelevant to the overall sentiment.
- Avoid lengthy descriptions or multiple perspectives if they do not add significant value to the summary.
## Example
### Original Review
"The product quality has seriously declined over the past year, which has resulted in a lot of disappointment. Previously, it was excellent, quick to respond, and a favorite among many."
### Condensed Summary
"Declined quality and increased disappointment."
## Output
Generate a concise summary for the following product review:
{{product_review_text}}Value of the API input "product_review_text" is inserted
Response:
Sentiment Mapping
Model
openai / gpt-4oPrompt
## Sentiment Mapping\n
You are an AI tasked with analyzing the summarized text to extract sentiment information that can aid business decision-making.\n
### Instructions\n
- Analyze the summarized text provided.\n
- Identify sentiment indicators and assess whether the overall sentiment is positive, neutral, or negative.\n
- Consider if the sentiment aligns with the key points expressed in the text.\n
- Output your analysis as a JSON object with fields "sentiment" and "category".\n
### Examples\n
Example input: "Quality has generally improved, leading to higher satisfaction."\n
Example output: `{"sentiment": "positive", "category": "quality improvement"}`\n
### Input\n
Summarized Text: {{29nTHtihSN6dqlF3_0U4fg}}Value of the result from the step "Review Summarization" is inserted\n
### Output the sentiment analysis as a JSON object without including unnecessary prefixes in the output. Response:\n
API OUTPUT KEYS
summaryReview SummarizationText
sentimentSentiment MappingText
Here are several examples of the input and output of this model.
Quick Test
Input
AI Synthesized
product_review_text
Run
2 steps1,497 ms
Output
from your model in draft
sentiment
{"sentiment": "negative", "category": "battery and performance issues"}
summary
"Stunning display but poor battery performance and overheating issues."
Quick Evaluation by LLM Judges
Metric
Relevance Output
Score
Reason
The model's response accurately reflects the sentiment expressed in the user request. The summary "Stunning display but poor battery performance and overheating issues" directly captures the positive aspect of the display and the negative aspects of battery life and overheating. The sentiment category "battery and performance issues" aligns with the content of the review. All required fields are present and their values are consistent with the input review. Expand
Metric
Sentiment Accuracy
Score
Reason
The model accurately identifies the sentiment as negative and provides a summary that reflects the sentiment expressed in the review. The JSON output format is also correctly structured, including the "sentiment" and "summary" fields. The sentiment category is also correctly identified as "battery and performance issues" which aligns with the review content. Expand
Metric
Prompt Effectiveness for Sentiment Analysis
Score
Reason
The model response includes a summary and sentiment analysis that accurately reflects the user's product review. The summary "Stunning display but poor battery performance and overheating issues" is a concise and relevant summary of the review text. The sentiment analysis correctly identifies the negative sentiment and categorizes it as "battery and performance issues." All required elements are present and correctly structured, meeting the criteria for a Grade 2. Expand
Evaluation Results
Evaluation Report
Introduction
Evaluation target plan
[Sentiment Insight Extractor](/genflows/wqszs7JlTuSAM8j8Q134Dg/develop/0UiQ2xJJS6OvRkLZfCO2IQ)
Datasets to test this AI model
We've prepared 50 cases from 5 major use cases, generated by LLM Dataset Synthesizer, like
Review Summarization: Provides concise insights from lengthy reviews
Sentiment Mapping: Converts text reviews to sentiment scores
Feedback Analysis: Identifies prevalent sentiments in product categories
Decision Support: Aids businesses in strategy adjustments
Trend Identification: Detects changes in customer sentiment over time
LLM Judge
We've simulated this AI from the prepared test datasets and analyzed the response by LLM Judges. We evaluated with 3 metrics, which are 3-grade labeling of either "Perfect", "OK", or "Bad."
The LLM Judges used are as follows:
Relevance Output: Ensures structured sentiment data aligns fully with the original review content.
Sentiment Accuracy: Agent's ability to accurately identify sentiments in reviews.
Prompt Effectiveness for Sentiment Analysis: Measures the successful elicitation of intended structural responses based on PRD specifications.
Evaluation Results
Performance
Relevance Output: The AI demonstrates exceptional performance with
90.6% rated as Perfect across all use cases, indicating strong capability in providing relevant structured outputs from reviews.Sentiment Accuracy: Achieving
92.5% Perfect ratings, the AI excels in accurately assessing sentiment. All use cases are uniformly scoring toward the upper echelon, ensuring effective sentiment extraction.Prompt Effectiveness for Sentiment Analysis: Notably,
17.0% of outputs fall under OK, suggesting room for improvement. This metric highlights potential challenges in designing prompts across varied use scenarios, impacting overall AI effectiveness.Evaluation Metrics by Type
Use Case Analysis:
Feedback Analysis and Trend Identification consistently achieve high scores (approx.
2.0), showcasing the model's capability in identifying prevalent sentiments and changes over time.Sentiment Mapping and Review Summarization present slight fluctuations (
1.8 to 2.0), yet remain effectively integrated within AI operations.Decision Support demonstrates the broadest variability with scores around
1.7, suggesting the need to refine the model’s ability to assist in strategic decisions with more precision.Average Scores by Use Case
Conclusions:
The AI is robust and performs excellently on most metrics, particularly excelling in Sentiment Accuracy and Relevance Output.
Enhancements are recommended for maximizing Prompt Effectiveness, especially for complex tasks like Decision Support and some variability in Review Summarization.
This insight should guide refinements to improve consistency and exploitability across all identified use cases.
Potential Hallucinations & Common Error Patterns
In several cases, the AI failed to capture nuanced sentiments. For instance, with the input
Good product but slightly overpriced in my opinion., the AI output Good quality but overpriced. did not include the nuance of "slightly." This indicates a tendency to simplify sentiments without capturing subtle differentiators.There are inaccuracies in summarizing reviews with mixed sentiment. For inputs like
Quality is fine, but I have had better for the price. It's okay., the output Average quality, not great value for the price. misses mixed sentiment aspects, oversimplifying nuanced feedback as purely negative. This suggests a pattern of bias towards categorizing mixed reviews as negative.Misalignment in review expression is evident. For example, the input
Fast shipping and great packaging, but the inside product didn't meet my expectations. generated the output Fast shipping and packaging, but product disappointing., which failed to reflect unmet expectations, showing a trend of omitting critical commentary details, reducing overall sentiment accuracy.Overall, the AI reflects a difficulty in completely capturing nuanced expressions and mixed sentiments, frequently favoring simplified or biased interpretations potentially due to prompt limitations.
Conclusions
Is this model production ready?
Almost ready, the Sentiment Insight Extractor shows promising results, with a high degree of accuracy across most metrics. Specifically, the AI achieves
92.5% Perfect ratings in Sentiment Accuracy and 90.6% in Relevance Output. The fraction of "Bad" outputs is minimal, indicating that the model is nearly production-ready. However, 17.0% of outputs are rated as OK for Prompt Effectiveness for Sentiment Analysis, suggesting the need for slight refinements.In general, the low percentage of "Bad" records supports the model's readiness for larger deployment. Nevertheless, human oversight remains crucial for continuous monitoring and addressing unexpected input behaviors as they arise. It's essential to review logs and gain further insights for robust real-world performance.
Future Improvements
Enhance Prompt Engineering: One of the key areas for future improvement is enhancing prompt engineering to address limitations in Prompt Effectiveness, particularly in complex scenarios like Decision Support. By crafting more precise and contextually aware prompts, it should be possible to reduce the
17.0% OK ratings further, leading to improved AI satisfaction across diverse use cases.Refinement in Nuanced Sentiment Capture: As observed from the evaluation, the AI has a tendency to simplify nuanced sentiments, such as failing to capture subtleties like "slightly overpriced." Developing more refined sentiment capture mechanisms can reduce the bias towards oversimplifying sentiments and improve the accuracy of mixed sentiment analysis, addressing the inclination to categorize mixed feedback as predominantly negative.
Integration
How this model is served
The Sentiment Analysis AI Agent is already deployed and accessible via Teammately API at the endpoint:
https://tmmt.ly/:id.Integration Example
For the use case of extracting sentiment data for analytics dashboards, you can integrate the AI with a data processing application using Python. This allows you to automate the intake and processing of reviews into structured sentiment insights:
1.Sentiment Analysis Integration in Python for Data Processing
python
import requests
def analyze_sentiment(review_text):
url = "https://tmmt.ly/:id"
payload = {"input": {"text": review_text}}
headers = {
"Authorization": f"Bearer YOUR_TEAMMATELY_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
return response.json()
# Example usage
product_review = "The product quality has declined over the past year, resulting in reduced satisfaction."
sentiment_data = analyze_sentiment(product_review)
print(sentiment_data)For businesses that want to integrate the AI into a microservices architecture, you can use Node.js for seamless API calls within a cloud-based system:
1.Sentiment Analysis Integration in Node.js for Microservices
javascript
const axios = require('axios');
async function analyzeSentiment(reviewText) {
const url = "https://tmmt.ly/:id";
const payload = { input: { text: reviewText }};
const headers = {
"Authorization": `Bearer YOUR_TEAMMATELY_API_KEY`,
"Content-Type": "application/json"
};
try {
const response = await axios.post(url, payload, { headers });
return response.data;
} catch (error) {
console.error("Error analyzing sentiment:", error);
throw error;
}
}
// Example usage
const productReview = "The product quality has declined over the past year, resulting in reduced satisfaction.";
analyzeSentiment(productReview)
.then(sentimentData => console.log(sentimentData))
.catch(error => console.error(error));Frontend Example
Frontend Example
Next: How to improve more?
Evaluation and Testing
Comprehensive Evaluation Needed: While the AI model has shown promising results in preliminary tests, a comprehensive evaluation is necessary to ensure reliability and robustness before deployment in a production environment. Consider running the model with hundreds of diverse test cases to establish its readiness. Teammately Agents can assist in synthesizing test cases and generating tailored LLM Judges to help evaluate the model at scale.
Integration Opportunities
Enhance with Knowledge Bases: Integration with existing domain-specific knowledge bases could significantly enhance the AI's performance and contextual understanding. Examples of such knowledge bases could include domain-specific ontologies or databases that are regularly updated. This integration would require careful mapping of the AI's capabilities to the information structures present in these systems.
Model Optimization
Experiment with Smaller Models: To optimize computational costs and reduce response latency, consider experimenting with smaller models. This approach could maintain effectiveness while making the AI more efficient in terms of resource usage. With the help of Teammately Agents, you can iterate on smaller models while leveraging LLM Judges for continuous evaluations to maintain the quality output.
Continuous Improvement
Iterative Enhancement: The AI system can benefit from an iterative enhancement process in which regular updates and improvements are implemented based on user feedback and performance data. This method includes analyzing user interactions to identify any shortcomings or areas where the AI could better anticipate or respond to user needs.
User Engagement and Feedback Loop: Establishing a robust feedback loop with end-users will provide valuable insights into real-world performance and usage patterns. This feedback can guide future enhancements, ensuring the AI evolves according to user needs and expectations.
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