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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

      University of Pennsylvania

      Business and Financial Modeling

      Skills you'll gain: Risk Modeling, Financial Statements, Probability Distribution, Mathematical Modeling, Statistical Modeling, Portfolio Management, Regression Analysis, Business Modeling, Financial Modeling, Strategic Decision-Making, Risk Management, Presentations, Decision Making, Data Visualization, Microsoft PowerPoint, Predictive Modeling, Investment Management, Data-Driven Decision-Making, Spreadsheet Software, Google Sheets

      4.5
      Rating, 4.5 out of 5 stars
      ·
      11K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: New AI skills
      New AI skills
      G

      Google

      Google Data Analytics

      Skills you'll gain: Data Storytelling, Data Literacy, Data Visualization, Data Presentation, Data Ethics, Rmarkdown, Interactive Data Visualization, Interviewing Skills, Data Cleansing, Data Validation, Ggplot2, Tableau Software, Presentations, Spreadsheet Software, Data Analysis, Data Visualization Software, Stakeholder Communications, Dashboard, Sampling (Statistics), R Programming

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      167K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • G

      Google

      Google Advanced Data Analytics

      Skills you'll gain: Exploratory Data Analysis, Data Storytelling, Statistical Hypothesis Testing, Data Ethics, Data Presentation, Data Visualization Software, Sampling (Statistics), Regression Analysis, Feature Engineering, Data Transformation, Descriptive Statistics, Data Visualization, Tableau Software, Data Manipulation, Statistical Analysis, Probability Distribution, Statistical Methods, Applied Machine Learning, Object Oriented Programming (OOP), Data Analysis

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6K reviews

      Advanced · Professional Certificate · 3 - 6 Months

    • I

      IBM

      Introduction to Data Analytics

      Skills you'll gain: Big Data, Data Analysis, Statistical Analysis, Apache Hadoop, Data Wrangling, Apache Hive, Data Collection, Data Mart, Data Warehousing, Analytics, Apache Spark, Data Cleansing, Data Lakes, Extract, Transform, Load, Data Visualization Software

      4.8
      Rating, 4.8 out of 5 stars
      ·
      19K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Pennsylvania

      Business Analytics

      Skills you'll gain: People Analytics, Data-Driven Decision-Making, Business Analytics, Descriptive Analytics, Business Intelligence, Human Resource Strategy, Financial Data, Marketing Analytics, Analytics, Talent Management, Financial Analysis, Predictive Analytics, Peer Review, Business Analysis, Financial Statement Analysis, Financial Forecasting, Customer Insights, Workforce Planning, Demand Planning, Customer Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      18K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Analyst

      Skills you'll gain: Data Storytelling, Dashboard, Data Visualization Software, Plotly, Data Wrangling, Data Visualization, Generative AI, SQL, Interactive Data Visualization, Exploratory Data Analysis, Data Cleansing, Big Data, Jupyter, Matplotlib, Data Analysis, Statistical Analysis, Pandas (Python Package), Data Manipulation, Excel Formulas, Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      91K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • D

      DeepLearning.AI

      Deep Learning

      Skills you'll gain: Computer Vision, Deep Learning, Image Analysis, Natural Language Processing, Artificial Neural Networks, Tensorflow, Supervised Learning, Large Language Modeling, Artificial Intelligence and Machine Learning (AI/ML), Artificial Intelligence, Applied Machine Learning, PyTorch (Machine Learning Library), Machine Learning, Debugging, Performance Tuning, Keras (Neural Network Library), Python Programming, Machine Learning Algorithms, Analysis, Data Processing

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      146K reviews

      Intermediate · Specialization · 3 - 6 Months

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Engineering

      Skills you'll gain: NoSQL, Data Warehousing, Apache Hadoop, Extract, Transform, Load, Apache Airflow, Web Scraping, Linux Commands, Database Design, SQL, IBM Cognos Analytics, MySQL, Apache Spark, Data Pipelines, Apache Kafka, Database Management, Bash (Scripting Language), Data Store, Jupyter, Generative AI, Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      58K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • U

      University of Pennsylvania

      Finance & Quantitative Modeling for Analysts

      Skills you'll gain: Return On Investment, Financial Reporting, Capital Budgeting, Financial Statements, Financial Modeling, Mathematical Modeling, Statistical Modeling, Regression Analysis, Business Modeling, Income Statement, Financial Analysis, Risk Analysis, Cash Flows, Business Mathematics, Financial Planning, Corporate Finance, Predictive Analytics, Spreadsheet Software, Google Sheets, Microsoft Excel

      4.5
      Rating, 4.5 out of 5 stars
      ·
      17K reviews

      Beginner · Specialization · 3 - 6 Months

    • S

      Starweaver

      Certified Business Analysis Professional™ (CBAP®)

      Skills you'll gain: Process Analysis, Requirements Elicitation, Business Analysis, Risk Analysis, Backlogs, Requirements Management, Stakeholder Engagement, Business Process, User Requirements Documents, User Story, Business Requirements, Requirements Analysis, Business Modeling, Business Process Improvement, Business Intelligence, Business Risk Management, Business Strategies, Financial Analysis, Performance Measurement, Business Planning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      240 reviews

      Intermediate · Specialization · 3 - 6 Months

    • I

      IBM

      IBM Business Analyst

      Skills you'll gain: Data Storytelling, Requirements Elicitation, Risk Mitigation, Business Analysis, Business Process Modeling, Risk Analysis, Process Mapping, Process Optimization, Requirements Management, Risk Management, Business Requirements, Stakeholder Management, Work Breakdown Structure, Scope Management, Process Analysis, Stakeholder Engagement, Data Visualization Software, Excel Formulas, Microsoft Excel, Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      12K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: New AI skills
      New AI skills
      G

      Google

      Google Digital Marketing & E-commerce

      Skills you'll gain: Media Planning, Search Engine Marketing, Email Marketing, Data Storytelling, Social Media Strategy, Search Engine Optimization, Social Media Marketing, Content Creation, Order Fulfillment, Google Ads, Online Advertising, A/B Testing, Social Media Campaigns, Target Audience, Digital Marketing, E-Commerce, Loyalty Programs, Customer Retention, Customer experience strategy (CX), Campaign Management

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      39K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    Sentiment Analysis learners also search

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    1234…530

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

    • Business and Financial Modeling: University of Pennsylvania
    • Google Data Analytics: Google
    • Google Advanced Data Analytics: Google
    • Introduction to Data Analytics: IBM
    • Business Analytics: University of Pennsylvania
    • IBM Data Analyst: IBM
    • Deep Learning: DeepLearning.AI
    • IBM Data Engineering: IBM
    • Finance & Quantitative Modeling for Analysts: University of Pennsylvania
    • Certified Business Analysis Professional™ (CBAP®): Starweaver

    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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