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Certificate Programme in Sentiment Analysis Techniques
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- Introduction to Sentiment Analysis and its Applications
- Text Preprocessing for Sentiment Analysis: Tokenization, Stemming, Lemmatization
- Sentiment Classification Techniques: Machine Learning Approaches (Naive Bayes, SVM, Logistic Regression)
- Deep Learning for Sentiment Analysis: Recurrent Neural Networks (RNNs) and Transformers
- Sentiment Analysis using Lexicon-based Approaches and Rule-based Systems
- Handling Negation and Sarcasm in Sentiment Analysis
- Evaluation Metrics for Sentiment Analysis: Precision, Recall, F1-Score, Accuracy
- Building a Sentiment Analysis System: Case Study and Project Development
- Advanced Topics in Sentiment Analysis: Aspect-Based Sentiment Analysis, Cross-lingual Sentiment Analysis
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Career Role (Sentiment Analysis) Description Sentiment Analyst (Junior) Entry-level position focusing on data collection, cleaning, and basic sentiment analysis using established tools.
Gain practical experience in natural language processing (NLP) and machine learning .
NLP Engineer (Mid-level) Develop and improve sentiment analysis models, deploy them to production environments, and collaborate with data scientists on algorithm improvement.
Requires proficiency in Python and strong machine learning skills.
Data Scientist (Senior) – Sentiment Analysis Focus Lead the development and implementation of advanced sentiment analysis techniques, contributing to strategic business decisions.
Requires expertise in various statistical methods, deep learning , and big data analysis.
EntryRequirements
- BasicUnderstandingSubject
- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
- DedicationCompleteCourse
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- ThreeFourHoursPerWeek
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