34 results for "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.‎