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Professional Certificate in Sentiment Analysis for Sent
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- Introduction to Sentiment Analysis and its Applications
- Text Preprocessing for Sentiment Analysis (Tokenization, Stemming, Lemmatization)
- Lexicon-Based Sentiment Analysis and its limitations
- Machine Learning for Sentiment Analysis (Naive Bayes, SVM, Logistic Regression)
- Deep Learning Methods for Sentiment Analysis (RNNs, LSTMs, Transformers)
- Sentiment Analysis using Python Libraries (NLTK, spaCy, TextBlob)
- Aspect-Based Sentiment Analysis
- Handling Sarcasm and Negation in Sentiment Analysis
- Evaluation Metrics for Sentiment Analysis (Accuracy, Precision, Recall, F1-score)
- Building a Sentiment Analysis Application (Case Study)
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Job Role Description Sentiment Analysis Specialist (NLP, Machine Learning) Develops and implements sentiment analysis models using Natural Language Processing (NLP) and machine learning techniques for UK-based clients.
High demand for expertise in Python and various machine learning libraries.
Data Scientist (Sentiment Analysis, Python) Extracts insights from large datasets leveraging sentiment analysis techniques, utilizing programming languages like Python to build predictive models and visualize data for business decisions.
Strong problem-solving skills are essential.
NLP Engineer (Sentiment Analysis, Deep Learning) Designs and implements natural language processing systems, including sophisticated sentiment analysis models using deep learning architectures.
Requires a deep understanding of linguistic structures and machine learning principles.
Social Media Analyst (Sentiment Analysis, Social Listening) Monitors social media platforms and analyzes sentiment expressed towards brands and products.
Uses sentiment analysis tools and techniques to extract actionable insights and drive strategic business decisions.
EntryRequirements
- BasicUnderstandingSubject
- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
- DedicationCompleteCourse
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- NotRegulatedAuthorized
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- ThreeFourHoursPerWeek
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