Question 1

What is sentiment analysis and why is it important to learn about?

Accepted Answer

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.

Question 2

What kinds of careers can I pursue with a background in sentiment analysis?

Accepted Answer

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.”

Question 3

Can I learn about sentiment analysis by taking online courses on Coursera?

Accepted Answer

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.

Question 4

What kind of people are best suited for roles in sentiment analysis?

Accepted Answer

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.

Question 5

How do I know if learning sentiment analysis is right for me?

Accepted Answer

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.

Question 6

What are common career paths for someone in sentiment analysis?

Accepted Answer

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.

Question 7

What topics can I study that are related to sentiment analysis?

Accepted Answer

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.

Question 8

What are the benefits of taking an online Sentiment Analysis course?

Accepted Answer

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.

Question 9

What Sentiment Analysis courses are best for training and upskilling employees or the workforce?

Accepted Answer

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.

Sentiment Analysis Courses Online

Filter by

Subject
Required

Language
Required

Learning Product
Required

Level
Required

Duration
Required

Skills
Required

Subtitles
Required

Educator
Required

Explore the Sentiment Analysis Course Catalog

NLP: Twitter Sentiment Analysis

Natural Language Processing

Sentiment Analysis with Deep Learning using BERT

ChatGPT Advanced Data Analysis

Applied Social Network Analysis in Python

Data Analysis with R Programming

Natural Language Processing with Classification and Vector Spaces

Practical Time Series Analysis

Text Mining and Analytics

Social Network Analysis

Natural Language Processing with Probabilistic Models

Python for Data Science, AI & Development

Sentiment Analysis learners also search

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

Frequently Asked Questions about Sentiment Analysis

What is sentiment analysis and why is it important to learn about?‎

What kinds of careers can I pursue with a background in sentiment analysis?‎

Can I learn about sentiment analysis by taking online courses on Coursera?‎

What kind of people are best suited for roles in sentiment analysis?‎

How do I know if learning sentiment analysis is right for me?‎

What are common career paths for someone in sentiment analysis?‎

What topics can I study that are related to sentiment analysis?‎

What are the benefits of taking an online Sentiment Analysis course?‎

What Sentiment Analysis courses are best for training and upskilling employees or the workforce?‎

Other topics to explore