AI Specialist to Build Sentiment Analysis Model
Skills Required
Description
Understanding customer sentiment at scale requires advanced AI models that can process text data efficiently. This role will focus on building a sentiment analysis system that classifies text into categories such as positive, negative, or neutral.
The AI specialist will work with Python, Pandas, and TensorFlow, leveraging deep learning to create models that can adapt to different datasets and industries.
Data cleaning will be a critical step in the process. Raw text often contains noise—typos, slang, or irrelevant information—that must be handled carefully to avoid skewed results.
Model design should consider both traditional NLP techniques and modern deep learning approaches. Combining these can lead to better accuracy and robustness.
Core objectives include:
Designing a sentiment analysis model optimized for high accuracy
Preparing datasets through cleaning and preprocessing
The system should be capable of handling real-world data such as customer reviews, social media posts, and survey responses.
Performance metrics will be closely monitored. Precision, recall, and F1 scores will determine whether the model is suitable for production use.
Training models efficiently will require thoughtful hardware and time management. Large datasets can be computationally intensive, so optimization is necessary.
The model should also be explainable. Stakeholders must understand why certain classifica...