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    • Sentiment Analysis

    Sentiment Analysis Courses Online

    Learn sentiment analysis for natural language processing. Understand how to analyze opinions and emotions in text data.

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    Explore the Sentiment Analysis Course Catalog

    • U

      Università Bocconi

      Food & Beverage Management

      Skills you'll gain: Food and Beverage, Hospitality Management, Restaurant Management, Product Quality (QA/QC), Global Marketing, Market Dynamics, Business Strategy, Consumer Behaviour, Value Propositions, Competitive Analysis, Innovation, Brand Management, Growth Strategies

      4.8
      Rating, 4.8 out of 5 stars
      ·
      4.9K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      Status: AI skills
      AI skills
      M

      Meta

      Meta Data Analyst

      Skills you'll gain: Data Storytelling, Business Metrics, Key Performance Indicators (KPIs), Data Management, Data Collection, Data Governance, Bayesian Statistics, Data Analysis, Descriptive Statistics, Statistical Hypothesis Testing, Information Privacy, Data Cleansing, Pandas (Python Package), Statistical Inference, Data Visualization Software, Spreadsheet Software, Correlation Analysis, Google Sheets, Exploratory Data Analysis, Data Modeling

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.4K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Neuroscience and Neuroimaging

      Skills you'll gain: Magnetic Resonance Imaging, Neurology, Medical Imaging, Anatomy, Radiology, Image Analysis, Data Analysis, Analysis, Data Manipulation, Experimentation, R Programming, Statistical Analysis, Psychology, Network Analysis, Data Processing, Regression Analysis, Research Design, Scientific Visualization, Time Series Analysis and Forecasting, Matlab

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.3K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Washington

      Machine Learning: Classification

      Skills you'll gain: Classification And Regression Tree (CART), Applied Machine Learning, Supervised Learning, Predictive Modeling, Text Mining, Machine Learning Algorithms, Data Cleansing, Scalability, Machine Learning, Natural Language Processing, Big Data, Probability & Statistics, Algorithms

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.7K reviews

      Mixed · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      G

      Google

      Foundations of Business Intelligence

      Skills you'll gain: Business Intelligence, Data Integration, Stakeholder Engagement, Dashboard, Data Modeling, Business Analytics, Real Time Data, Requirements Elicitation, Data Warehousing, Data-Driven Decision-Making, Data Analysis, Key Performance Indicators (KPIs), Data Pipelines, Project Implementation

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.8K reviews

      Advanced · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      Indian School of Business

      Trading Strategies in Emerging Markets

      Skills you'll gain: Financial Statements, Peer Review, Financial Statement Analysis, Financial Trading, Balance Sheet, Portfolio Management, Data Collection, Market Liquidity, Market Data, Investment Management, Financial Analysis, Performance Analysis, Financial Market, Market Dynamics, Project Design, Market Analysis, Business Planning, Securities Trading, Performance Measurement, Technical Analysis

      4.2
      Rating, 4.2 out of 5 stars
      ·
      2.7K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Cape Town

      Understanding Clinical Research: Behind the Statistics

      Skills you'll gain: Biostatistics, Statistical Hypothesis Testing, Statistical Methods, Probability & Statistics, Clinical Research, Statistical Analysis, Quantitative Research, Descriptive Statistics, Statistical Inference, Data Collection, Probability

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.6K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      D

      DeepLearning.AI

      AI for Medicine

      Skills you'll gain: Deep Learning, Statistical Analysis, Clinical Trials, Feature Engineering, Risk Modeling, Treatment Planning, Data Analysis, Precision Medicine, Decision Tree Learning, Predictive Modeling, Patient Treatment, Image Analysis, Machine Learning Methods, Applied Machine Learning, AI Personalization, Machine Learning, Random Forest Algorithm, Artificial Intelligence and Machine Learning (AI/ML), Data Processing, Medical Imaging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.4K reviews

      Intermediate · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of California San Diego

      Interaction Design

      Skills you'll gain: Design Research, Interaction Design, User Experience Design, Statistical Analysis, Usability, Ideation, User Research, Graphic and Visual Design, User Interface (UI) Design, Experimentation, Prototyping, Human Centered Design, A/B Testing, Usability Testing, User Centered Design, Mockups, Human Computer Interaction, Human Factors, Collaborative Software, Telecommuting

      4.5
      Rating, 4.5 out of 5 stars
      ·
      4K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Illinois Urbana-Champaign

      Microeconomics Principles

      Skills you'll gain: Supply And Demand, Economics, Market Dynamics, Business Economics, Policy Analysis, Business, Behavioral Economics, Public Policies, Business Operations, Price Negotiation, Socioeconomics, Consumer Behaviour, Economics, Policy, and Social Studies, Business Strategy, Market Research, Competitive Analysis, Resource Allocation, Revenue Management, Decision Making, Market Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.2K reviews

      Beginner · Specialization · 1 - 3 Months

    • Status: Free Trial
      Free Trial
      U

      University of Pennsylvania

      Introduction to Programming with Python and Java

      Skills you'll gain: Matplotlib, Object Oriented Design, Java, Object Oriented Programming (OOP), Data Analysis, Unit Testing, Pandas (Python Package), Eclipse (Software), Data Structures, Data Cleansing, Debugging, Pivot Tables And Charts, Data Visualization Software, Software Testing, Integrated Development Environments, Program Development, Programming Principles, Python Programming, Computer Programming, Computational Thinking

      4.5
      Rating, 4.5 out of 5 stars
      ·
      1.8K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free Trial
      Free Trial
      U

      University of Colorado Boulder

      Advanced Business Analytics

      Skills you'll gain: Data Storytelling, Data Presentation, Data Governance, Data Visualization, Data Storage Technologies, Predictive Analytics, Analytics, Business Analytics, Data Storage, Big Data, Risk Analysis, Databases, Data Modeling, Business Analysis, Business Intelligence, Data Visualization Software, Data Analysis, Decision Making, Data Cleansing, Predictive Modeling

      4.6
      Rating, 4.6 out of 5 stars
      ·
      5.2K reviews

      Intermediate · Specialization · 3 - 6 Months

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    In summary, here are 10 of our most popular sentiment analysis courses

    • Food & Beverage Management: Università Bocconi
    • Meta Data Analyst: Meta
    • Neuroscience and Neuroimaging: Johns Hopkins University
    • Machine Learning: Classification: University of Washington
    • Foundations of Business Intelligence: Google
    • Trading Strategies in Emerging Markets: Indian School of Business
    • Understanding Clinical Research: Behind the Statistics: University of Cape Town
    • AI for Medicine: DeepLearning.AI
    • Interaction Design: University of California San Diego
    • Microeconomics Principles: University of Illinois Urbana-Champaign

    Frequently Asked Questions about Sentiment Analysis

    Sentiment analysis is the use of computer software to identify opinions expressed in text and classify them, for example as positive, negative, or neutral. These applications can provide very important data for businesses that need to monitor perceptions of their products or brands on social media and other online channels, as well as for organizations seeking to understand public opinion about prominent figures, issues, or current events.

    Sentiment analysis is one of the most common applications of natural language processing (NLP), which is the use of artificial intelligence (AI) and related algorithmic approaches to allow computers to understand, interpret, and even communicate using human language. Sentiment analysis uses machine learning algorithms and deep learning approaches using artificial neural networks to conduct the machine translation and analysis of text, typically using TensorFlow or Python programming.‎

    Sentiment analysis and other natural language processing (NLP) skills are a valuable asset for careers in data science, with a growing number of businesses and other organizations looking to take advantage of this technology to help understand their customers or the general public. From conducting analysis of brand perceptions on social media to creating chatbots that can understand and respond appropriately to users’ emotions, sentiment analysis can be harnessed for an expanding range of emerging applications.

    An understanding of how to use the insights generated by sentiment analysis is also important for today’s digital marketing professionals, who need to understand how their brands and products are being discussed on social media networks like Twitter, Facebook, and Instagram and adjust their communications strategies appropriately. According to the Bureau of Labor Statistics, marketing managers earned a median annual salary of $135,900 in 2019, and “advertising managers who can navigate the digital world should have the best prospects.”‎

    Yes! There are a wealth of opportunities to learn about all kinds of data science skills on Coursera, including courses and Specializations spanning multiple courses about natural language processing (NLP) and sentiment analysis. You can learn from top-ranked institutions like University of Washington, University of Michigan, and deeplearning.ai, or by completing step-by-step tutorials alongside experienced instructors with Coursera’s Guided Projects. Regardless of how you choose to learn, taking courses online lets you build valuable skills in sentiment analysis on a flexible schedule that fits into your existing studies or career.‎

    People who are native American English speakers—or speak the language native to the country they'll be working in—are typically best suited for sentiment analysis roles when evaluating social media posts and consumer reviews. People who have fairly advanced technical skills, such as programming and machine learning can also do well in sentiment analysis roles. People who enjoy looking at data sets and uncovering patterns, as well as those who enjoy solving complex problems can typically perform well in sentiment analysis. Additionally, those who enjoy using social media and who are comfortable with apps can be well suited for these types of roles.‎

    If you want to be able to review social media posts to determine if customers are happy with products or the performance of customer service agents, learning sentiment analysis could be right for you. It’s mainly used in business when business owners and managers want to know more about customer opinions than can be seen on the surface. It’s a type of natural language processing that can also be right for you if you’re interested in AI. It may also be helpful for you if you want to know what your customers think of your other business practices, such as your prices.‎

    Data science, AI, and algorithm research are three common career paths for someone in sentiment analysis. These fields all use this type of machine learning to reveal patterns. Investment analysts and statisticians are also common careers for people in sentiment analysis. These skills can also typically be applied to positions like business intelligence analysis, investor relations analysis, and workforce science. Additionally, statisticians and entrepreneurs are career paths that someone who has studied sentiment analysis could pursue.‎

    Machine learning, AI, and natural language processing are some topics you can study that are related to sentiment analysis. Python, TensorFlow, and quanteda are some computer programs you could learn that are related to sentiment analysis. You could also learn about text mining and sequence models that use tools like attention models, recurrent neural networks, gated recurrent units (GRUs), and long short-term memory (LSTM) to answer sentiment analysis questions. Additionally, topics like marketing analysis and psychology can be related to this topic if you plan to pursue work in the marketing field.‎

    Online Sentiment Analysis courses offer a convenient and flexible way to enhance your knowledge or learn new Sentiment Analysis skills. Choose from a wide range of Sentiment Analysis courses offered by top universities and industry leaders tailored to various skill levels.‎

    When looking to enhance your workforce's skills in Sentiment Analysis, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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