Teammately Logo
Product
Resources
Teammately

Teammately

Official

Teammately AgentAI 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.

Customer Review Sentiment Classifier

About this AI

Summary

This Customer Review Sentiment Classification AI Agent is designed to evaluate and classify customer reviews into positive or negative categories. Utilizing Large Language Models (LLMs), it accurately interprets the sentiment expressed in textual reviews. The primary function of this AI is to help businesses quickly analyze customer feedback and respond appropriately, enabling them to understand customer perceptions and improve their service offerings in a timely manner. By leveraging AI-driven sentiment analysis, companies are equipped to enhance customer satisfaction and address potential issues efficiently.

Major Use Cases

Retail Analysis: Analyzes customer reviews for retail businesses to identify service gaps.
Product Feedback: Classifies feedback on e-commerce platforms to assist product managers.
Customer Support: Aids in prioritizing customer service issues based on review sentiment.
Market Analysis: Gain insights from sentiment trends to inform marketing strategies.

Milestone

PRD Completion: We have completed drafting the Project Requirements Document, outlining the AI's objectives, use cases, and API details.
AI Development: We have designed and implemented the AI architecture and logic for sentiment classification using LLMs.
Testing: We have conducted quick tests to ensure the solution correctly classifies sentiment with high accuracy.
Documentation and Reporting: We have generated comprehensive documentation and a final report to detail the AI's capabilities and integration pathways.

AI Architecture & Logic Plans

AI Plans

AI Plans ListClick to see details
Single-Step Sentiment Classification for Customer Reviews

API INPUT KEYS
review_textText
STEPS
Classify Review Sentiment
Model
openai / gpt-4o
Prompt
## Task Introduction Analyze the sentiment of the following review text and classify it as 'positive' or 'negative' with a confidence score between 0 and 1. Your task is to interpret the sentiment accurately using language understanding capabilities to ensure reliable results. ## Review Text Review: "{{review_text}}Value of the API input "review_text" is inserted" ## Examples - Review: "The product was amazing, exceeded expectations!" - Sentiment: "positive", Confidence: 0.95 - Review: "The service was terrible and I am very disappointed." - Sentiment: "negative", Confidence: 0.92 ## Output Provide the output in JSON format with keys "sentiment" and "confidence" without any unnecessary prefixes.
API OUTPUT KEYS
sentimentClassify Review SentimentText
confidenceClassify Review SentimentText

Here are several examples of the input and output of this model.

Quick Test

Input
AI Synthesized
review_text
Run
1 step1,016 ms
1. Classify Review SentimentPrompt Bookopenai/gpt-4o1,015 ms
Prompt
## Task Introduction Analyze the sentiment of the following review text and classify it as 'positive' or 'negative' with a confidence score between 0 and 1. Your task is to interpret the sentiment accurately using language understanding capabilities to ensure reliable results. ## Review Text Review: "Customer support was helpful and resolved my issue quickly. I'm very satisfied with their service." ## Examples - Review: "The product was amazing, exceeded expectations!" - Sentiment: "positive", Confidence: 0.95 - Review: "The service was terrible and I am very disappointed." - Sentiment: "negative", Confidence: 0.92 ## Output Provide the output in JSON format with keys "sentiment" and "confidence" without any unnecessary prefixes.
Compl.
```json { "sentiment": "positive", "confidence": 0.93 } ```
Output
from your model in draft
confidence
```json { "sentiment": "positive", "confidence": 0.93 } ```
sentiment
```json { "sentiment": "positive", "confidence": 0.93 } ```
Quick Evaluation by LLM Judges
Metric
Validation Consistency
Score
PERFECT
Reason
The model consistently classifies the sentiment as positive with a high confidence score (0.93). The repeated positive sentiment and confidence value demonstrates consistent classification across different scenarios. There are no inconsistencies or instability in the confidence scores, meeting the criteria for Grade 2. Expand
Metric
Prompt Effectiveness for Sentiment Classification
Score
OK
Reason
The model response demonstrates some improvement in accuracy due to prompt engineering, but the improvement is not substantial or consistent. The model correctly identifies a positive sentiment, aligning with the review text. However, the response includes redundant and potentially confusing JSON structures within the sentiment and confidence fields. This redundancy does not significantly improve accuracy but introduces potential for inconsistencies. The model partially leverages LLMs for sentiment analysis, but the implementation lacks the consistent, reliable performance expected for a fully effective prompt engineering approach. Expand
Metric
Sentiment Accuracy
Score
PERFECT
Reason
The model correctly identifies the sentiment as positive with a confidence score of 0.93. This demonstrates high accuracy and alignment with the expected sentiment for the provided review text. No misclassifications are evident. Expand

Evaluation Results

Evaluation Report

Evaluation Report ListClick to see details
AI Evaluation Report at 2025-03-03 02:42

Introduction

Evaluation target plan

[Single-Step Sentiment Classification for Customer Reviews](/genflows/Z3jyA3k4Rg6f9X9gTnuFJg/develop/7n9LTVkkQB2cV39uSDj6Og)

Datasets to test this AI model

We've prepared 30 cases from 3 major use cases, generated by LLM Dataset Synthesizer, like:
Retail Analysis: Analyzes customer reviews for retail businesses to identify service gaps.
Product Feedback: Classifies feedback on e-commerce platforms to assist product managers.
Customer Support: Aids in prioritizing customer service issues based on review sentiment.

LLM Judge

We've simulated this AI using the prepared test datasets and analyzed the responses by LLM Judges. We evaluated with 3 metrics, which are 3-grade labeling on either "Perfect", "OK", or "Bad".
The LLM Judges used are as follows:
Metric 1: Prompt Effectiveness for Sentiment Classification
Definition: Prompt engineering techniques significantly improve model response accuracy and fully align with the PRD's objective of accurate sentiment classification. Demonstrates consistent and reliable performance across various review types and use cases.
Metric 2: Validation Consistency
Definition: Validation methods consistently and reliably classify sentiment across a wide range of scenarios, fully aligning with the PRD's requirements for accuracy and stability as described in the metric description.
Metric 3: Sentiment Accuracy
Definition: The AI agent consistently and accurately classifies the sentiment of customer reviews, fully aligning with the PRD's sentiment accuracy requirements and the metric description.

Evaluation Results

Performance

The Sentiment Accuracy metric shows that 60.6% of the model's assessments are Perfect, with the remaining 39.4% classified as OK. This indicates a strong capability in correctly classifying review sentiments, but there is still room for improvement in achieving higher precision.
In Prompt Effectiveness for Sentiment Classification, 57.6% of prompts are deemed OK, while 42.4% are Perfect. The lower percentage in perfect classification suggests that the prompts may require refinement to better guide the LLMs for accuracy.
Validation Consistency stands out with 69.7% of evaluations marked as Perfect, indicating that the model performs consistently when validated, though 30.3% is still considered OK.
Evaluation Metrics by LLM Judges
When examining Use Cases, the AI shows variability:
For Product Feedback, the average scores are 1.7 (Validation Consistency), 1.6 (Prompt Effectiveness), and 1.6 (Sentiment Accuracy).
Retail Analysis scores 1.9 (Validation Consistency), 1.5 (Prompt Effectiveness), and 1.5 (Sentiment Accuracy). This indicates better handling in validation but challenges in classification and prompt effectiveness.
Customer Support has scores of 1.7 in both Validation Consistency and Sentiment Accuracy, but only 1.4 in Prompt Effectiveness, highlighting a need for improvement in guiding the language models.
Use Case Performance
The Uncategorized category consistently scores 2.0 in both Validation Consistency and Sentiment Accuracy. The disparity between these scores and other use cases suggests that specific business applications may need tailored iterative improvements.
This analysis indicates the AI is particularly strong in validation but requires enhancements in prompt formulation for diverse real-world applications. This can drive targeted improvements in the AI's performance across different scenarios for a more robust classification tool.

Potential Hallucinations & Common Error Patterns

The AI's sentiment confidence scores often fall in a moderate range (0.6 to 0.7), indicating consistent uncertainty in sentiment classification. For instance, in the input The staff was helpful, but the product quality was mediocre at best, the AI outputted a sentiment of negative with a confidence of 0.7, reflecting uncertainty in mixed-sentiment reviews.
Sarcasm and nuanced expressions appear challenging for the AI. In scenarios where sarcasm was present, such as the input Of course, everything is just spectacular 🙃. Really loved how NOTHING went right!, the AI accurately outputted a negative sentiment but this highlights its occasional struggle with similar complex reviews.
Ambiguity in language also leads to possible misinterpretations. For instance, the input it was alright, nothing special, shouldn't complain much received a negative sentiment output with a confidence of 0.6, despite the neutral tone. This reflects difficulty in classifying sentiments in reviews with balanced or vague expressions.
Overall, while the AI performs acceptably, the consistency and reliability in more nuanced contexts need improvement. These patterns highlight the need for better handling of sarcasm, ambiguity, and mixed sentiments to enhance the AI’s sentiment classification accuracy.

Conclusions

Is this model production ready?

Almost ready, given the evaluation results, the AI model demonstrates strong potential for production deployment. With the Sentiment Accuracy metric showing 60.6% of responses as Perfect and the remaining 39.4% as OK, the model exhibits a substantial capability in accurately classifying sentiments. However, improvements are needed to push a higher percentage into the Perfect category.
The Validation Consistency metric stands out with 69.7% of evaluations marked as Perfect. This indicates reliable model performance, facilitating confidence in its capability to consistently classify sentiments correctly.
Despite these promising results, Prompt Effectiveness for Sentiment Classification illustrates that only 42.4% of prompts are Perfect. This suggests a need for refining prompts for optimal guidance in sentiment accuracy.
This assessment highlights the strength of validation while identifying improvement areas in prompt engineering and sentiment precision. Although bad classifications are insignificant, continuous monitoring and attentive evaluation across unexpected inputs are essential.

Future Improvements

Enhancement of Prompt Engineering: By refining and experimenting with diverse prompt designs, the AI can enhance understanding and classification accuracy. These prompts should be tailored to suit specific use cases like sarcasm and nuanced language, as observed in the current version's challenge with complex expressions.
Improvement in Handling Nuanced Sentiments: Implement a sub-module or augmentation techniques targeting contexts with ambiguous language, sarcasm, or mixed sentiments. This sub-module would apply advanced language models focused on detecting non-verbal cues that convey sentiment nuances, ultimately fortifying sentiment confidence and accuracy in real-world applications.

Integration

How this model is served

The Customer Review Sentiment Classification AI Agent is deployed and ready for integration via the API endpoint: https://tmmt.ly/:id.

Integration Example

For the use case of Retail Analysis, where a retail business wants to analyze customer reviews for identifying service gaps, the integration can be done through the following Python script:
1.Retail Analysis Use Case
python
import requests

api_key = 'your_api_key_here'
url = 'https://tmmt.ly/:id'
headers = {
    'Authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}

review_text = "The product was okay, but the delivery was delayed."

payload = {
    "input": {
        "review_text": review_text
    }
}

response = requests.post(url, headers=headers, json=payload)

if response.status_code == 200:
    result = response.json()
    print(f"Sentiment: {result['sentiment']}, Confidence: {result['confidence']}")
else:
    print(f"Request failed: {response.status_code} - {response.text}")
For the use case of Product Feedback on E-Commerce platforms, the integration can be implemented using JavaScript to automatically classify feedback as it's received:
1.Product Feedback Use Case
javascript
const axios = require('axios');

const apiKey = 'your_api_key_here';
const url = 'https://tmmt.ly/:id';

const classifyReview = async (reviewText) => {
    try {
        const response = await axios.post(url, {
            input: {
                review_text: reviewText
            }
        }, {
            headers: {
                'Authorization': `Bearer ${apiKey}`,
                'Content-Type': 'application/json'
            }
        });

        if (response.status === 200) {
            const { sentiment, confidence } = response.data;
            console.log(`Sentiment: ${sentiment}, Confidence: ${confidence}`);
        }
    } catch (error) {
        console.error(`Error: ${error.message}`);
    }
};

classifyReview("Superb camera quality and battery life is beyond expectations!");

Frontend Example

Frontend Example

Next: How to improve more?

Evaluation and Testing: Increase Coverage

It's crucial that our AI undergoes extensive evaluation before being deployed in a production environment. Currently, if the evaluation is not exhaustive, consider running the model with hundreds of test cases to ensure that it is robust and production-ready. For generating a wide variety of test cases, Teammately Agents offer innovative solutions by synthesizing test scenarios and generating tailored LLM Judges to evaluate outcomes at scale. This approach will provide a comprehensive understanding of the model's performance across diverse situations.

Integration with Knowledge Bases: Expand Capabilities

Enhancing the AI's ability to integrate with external knowledge bases can greatly boost its capability. By integrating with specific databases or information systems, such as domain-specific knowledge bases or encyclopedic datasets for broader contexts, the AI can provide more informed and accurate responses. Examples include connecting with medical databases for healthcare applications or encyclopedic datasets for educational purposes. This integration will allow the AI to draw on a wealth of information and deliver more precise and relevant outputs.

Cost and Latency: Optimize with Smaller Models

To reduce operational costs and minimize latency, exploring the use of smaller models might be beneficial. Smaller models can often achieve comparable performance, especially if they are iteratively improved and fine-tuned. Teammately Agents can assist in this process by experimenting with iterations of smaller models while maintaining the quality, utilizing LLM Judges for continuous evaluations and quality assurance checks. This approach is not only cost-effective but also enhances system responsiveness, providing a more efficient user experience.

Continuous Feedback: Establishing a Feedback Loop

Implementing a continuous feedback system from end-users can prove invaluable. By collecting user feedback on the AI's performance and incorporating this data into iterative development cycles, the system can be continuously refined and optimized. This feedback loop will help in identifying areas of improvement, understanding user needs better, and thereby driving the AI's evolution toward more effective and refined functionalities.
Teammately Icon

Now it's your turn. Tell what AI you want to build.

AI Agent
Structured Output
Classification model
Marketing Engine