Customer Feedback Analysis Through Entity Tagging and
Sentiment Analysis for a Global Technology Company
A global technology giant and leader in enterprise software and cloud computing solutions aimed to improve its understanding of customer sentiment and preferences. With millions of users worldwide, the company needed to generate valuable insights from its customer reviews to enhance product and service offerings.
Challenge
The company faced the complex task of analyzing vast amounts of unstructured textual data, including emails, online reviews, customer interactions, and social media posts. The objectives included identifying entities, sentiments, and intents within customer feedback, and the company partnered with iMerit to help expedite the process.
CHALLENGES IN SENTIMENT ANALYSIS AND ASPECT IDENTIFICATION
The challenges lay in consolidating sentiments associated with diverse aspects of the data and accurately defining and locating various aspects within reviews. In sentiment analysis, the challenge lies in navigating sentiments intricately tied to various attributes within diverse and unstructured customer feedback data. The goal was to conduct a comprehensive sentiment analysis, delving into the nuanced expressions to grasp the overarching sentiment directed toward specific attributes. It involved dissecting sentiments associated with different elements, providing a holistic understanding of customer opinions across the company’s offerings.
Our focus was on the intricate task of defining and pinpointing various aspects within customer reviews. It encompassed a complex process of locating and identifying concerned objects that spanned from detailed features of the products to broader attributes. It helped the tech giant with a nuanced understanding of the specific elements customers referenced in their feedback, enriching the depth of insights derived from the analysis.
Solution
SOLUTION FOR ENTITY TAGGING AND ACCURACY ENHANCEMENT
iMerit collaborated with the company to overcome challenges with entity tagging and sentiment analysis of customer reviews. The approach involved implementing a robust sentiment analysis model, leveraging deep learning and big data. Accurate data labeling was deemed crucial for the model’s performance and precision.
- The model underwent rigorous training on a diverse set of labeled data.
- Human-in-the-loop validation was crucial in augmenting aspect identification and sentiment analysis accuracy.
- Our teams with skilled annotators meticulously validated model predictions, contributing to an iterative refinement process that significantly improved overall performance.
Result
IMPROVED DATA LABELING EFFICIENCY
iMerit’s strategic approach involved implementing a robust sentiment analysis model, accurate data labeling, and a strong emphasis on precision with human annotators. This strategy translated into outstanding results for the company:
- Realized an impressive 15% boost in efficiency by significantly reducing the median Time Per Task (TPT) with the highest data quality standards.
- Completed a substantial volume of 60,000 tasks for quick and effective task execution.
- Attained a 95% accuracy in data annotation, ensuring precise analyses and insights for informed decision-making.
The collaborative effort between iMerit and the global technology company in entity tagging, aspect identification, and sentiment analysis led to an improved understanding of customer preferences and behaviors. The data annotation solution by iMerit empowered the company to leverage insights for strategic decision-making, ultimately enhancing customer satisfaction and informing future business strategies.
