We at Macgence understand the importance of accurate and reliable text annotations. In the following case study, we’ll highlight how we assisted a customer feedback processing company to improve its sentiment analysis algorithm. Macgence’s custom AI annotation deployed with quality-controlled measures resulted in providing accurate sentiment analysis along with deep insights to the customer. They even observed an increase in their operational efficiency.
Table of Contents
Problem
Due to inaccurate sentiment analysis, our client was struggling with obtaining reliable insights.
The primary reason for the same was the low accuracy of their existing sentiment analysis model. These models produce less accurate results often due to a lack of domain-specific annotations and due to limited training data.
Further, they had a gigantic volume of customer feedback data, and manual annotation of such data would have been a tedious and time-consuming task. The quality of results after manual annotation would still be questionable.
Resolution
Our approach to this challenge involved a combined manual and automated data annotation process as the volume of customer feedback data was quite large.
Our experts trained their sentiment analysis model with the help of machine learning algorithms and natural language processing(NLP) techniques.
To improve the accuracy and reliability of the customer feedback processing model, the annotated data was used for fine-tuning the existing sentiment analysis model.
Results and Excellent Customer Feedback
- Increase in Operational Efficiency:
Our streamlined annotation process combined with our quality control measures reduced the time and effort required for manual annotation to a great extent. This led to a hike in the model’s operational efficiency.
- Enhanced Sentiment Analysis:
With the help of our custom annotations, the client’s sentiment analysis algorithm saw a much higher accuracy and reliability which in turn led to better customer insights.
- Better Customer Feedback Processing:
With the improved sentiment analysis, our client was able to get deeper insights from customer feedback. This helped them in making better decisions for business expansion.
A leader in AI & ML solutions, Macgence provides its services globally. With our expertise in in-text annotation, we guarantee to improve the sentiment analysis of customer feedback. We ensure accurate and reliable annotations through our customized annotation process, automated tools, and quality control measures.
Applications of Text Annotation
Speech Recognition
Annotating text transcriptions with time stamps and speaker information for training speech-to-text models, is crucial for developing voice-activated assistants and transcription services.
Emotion Detection
Annotating text with emotional states (joy, anger, sadness, etc.). This is applied in customer feedback analysis, social media monitoring, and human-computer interaction.
Document Summarization
Annotating key sentences or phrases that encapsulate the main ideas of a document. This helps in developing models for automatic text summarization.
Text Classification
Labeling texts according to predefined categories (e.g., spam vs. non-spam, topic categorization). This is used in email filtering, news categorization, and content moderation.
The Macgence Way
TAT
Compliant high-quality data available at your disposal that comes with benefits of customization as well that can be quickly delivered
COMPLIANCE
Adherence to both the mandatory compliances of HIPAA & GDPR
ACCURACY
Provides ~98% accuracy across different annotation types and model datasets
NO. OF USE CASES SOLVED
Experience across a diverse range of use cases