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Assessed and Enhanced Sentiment/Behavior Models by Improving Query Methodology and Model Thresholds

Case Study

Background

Client did not have validation and proper correlation established to confirm whether sentiment scoring model was accurate and truly reflective of a call center conversation. An enhanced sentiment scoring model aims to improve upon existing sentiment analysis techniques by leveraging more sophisticated algorithms, richer contextual information, and domain-specific knowledge.

Challenges

Data Quality and Quantity

Sentiment and agent behavior models heavily rely on data for training. One challenge is ensuring the data used is of high quality and quantity. Noise, bias, and imbalance in the data can lead to inaccurate models.

Labeling and Annotation

Supervised learning approaches require labeled data for training. However, labeling sentiment or behavior data can be subjective and labor-intensive. Ensuring consistency and reliability in labeling is a significant challenge.

Real-time Processing

In applications such as customer service and social media monitoring, sentiment and behavior models often need to process data in real-time. Developing efficient algorithms and infrastructure for real-time processing can be challenging.

Saarthee Solution

Data Collection and Preprocessing

  • Gather a diverse and representative dataset of text data relevant to the domain of interest (e.g., social media posts, customer reviews, news articles).
  • Preprocess the text data by removing noise, such as HTML tags, punctuation, and stop words. Perform tokenization, stemming, and lemmatization to standardize the text.

Feature Engineering

  • Extract features from the preprocessed text data that capture relevant information for sentiment analysis. This may include:
  • Bag-of-Words (BoW) or TF-IDF representations to capture word frequencies and importance.
  • Word embeddings (e.g., Word2Vec, GloVe) to represent words in a continuous vector space.
  • Character-level n-grams to capture morphological and syntactic patterns.
  • Part-of-Speech (POS) tagging or Named Entity Recognition (NER) to identify important entities and grammatical structures.

Model Selection

  • Choose a suitable machine learning or deep learning model architecture for sentiment analysis. Some options include:
  • Traditional machine learning algorithms such as Support Vector Machines (SVM), Naive Bayes, or Logistic Regression.
  • Deep learning architectures like Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), or Transformer-based models (e.g., BERT, GPT).
  • Experiment with different model architectures and hyperparameters to find the best-performing model.

Model Training

  • Split the dataset into training, validation, and test sets.
  • The sentiment analysis model on the training data while validating performance on the validation set.
  • Fine-tune the model based on validation performance and avoid overfitting using techniques such as regularization and early stopping.

Domain Adaptation and Transfer Learning

  • Fine-tune pre-trained language models (e.g., BERT, GPT) on domain-specific data to adapt them to the target domain.
  • Transfer knowledge learned from a large, general-purpose corpus to the specific task of sentiment analysis in the target domain.

Ensemble Methods

  • Combine predictions from multiple sentiment analysis models to improve overall performance through ensemble methods.
  • Use techniques such as bagging (e.g., Random Forests), boosting (e.g., Gradient Boosting Machines), or stacking to combine predictions from diverse models.

Interpretability and Explain Ability

  • Enhance model interpretability by analyzing feature importance, attention mechanisms, or saliency maps.
  • Provide explanations for model predictions using techniques such as LIME (Local Interpretable Model-Agnostic Explanations) or SHAP (Shapley Additive explanations).

Continuous Evaluation and Improvement

  • Continuously evaluate the sentiment analysis model on new data to assess its performance in real-world scenarios.
  • Monitor model drift and retrain the model periodically to adapt to changing trends and language usage patterns.
  • Incorporate user feedback and domain expertise to iteratively improve the model’s accuracy and relevance.

By following these steps and leveraging advanced techniques, an enhanced sentiment scoring model can provide more accurate and nuanced sentiment analysis across diverse domains and applications. 

Impact

NPS score improved 3 points per quarter post-sentiment implementation and action plan. Leadership was also able to refine behavioral coaching program to incorporate learnings from sentiment model.

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