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T-Mobile, a Dutch telecom company, sought a CRM chatbot solution to enhance customer support by detecting user intents and analyzing sentiment in Dutch. This chatbot aimed to identify customer issues and provide appropriate responses efficiently.
֍ Objective: Classify user queries.
֍ Tech Stack: Keras, LSTM, GRU, Gensim Word2Vec.
֍ Process: Data preprocessing (tokenization, lemmatization). Model training using
LSTM/GRU for capturing context and Word2Vec for embeddings.
֍ Objective: Analyze sentiment (positive, negative, neutral).
֍ Tech Stack: Scikit Learn, Keras.
֍ Process: Similar preprocessing. Initial rule-based model, followed by deep learning
models (LSTM/GRU).
֍ Data Preprocessing: Tokenization, stemming, stop-word removal.
֍ Model Architecture:
❖ Intent Detection & Sentiment Analysis:
➢ LSTM/GRU layers for sequence modeling.
➢ Dense layers for classification.
֍ Training & Evaluation: Cross-validation, accuracy, precision, recall, F1-score.
֍ Integration: Embedded into T-Mobile's CRM.
֍ Infrastructure: Scalable for real-time interactions, continuous updates.
֍ Enhanced accuracy in responding to customer queries.
֍ Improved customer satisfaction through precise intent and sentiment analysis.
֍ Efficient issue resolution, reducing response times and improving service quality
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