When you think of artificial intelligence (AI), the word "emotion" doesn't typically come to mind. But there's an entire field of research using AI to understand emotional responses to news, product experiences, movies, restaurants, and more. It's known as sentiment analysis, or emotion AI, and it involves analyzing views – positive, negative, or neutral – from written text to understand and gauge reactions. Sentiment analysis can be used for survey research, social media analyses, and tracking psychological trends. Picture software that scans articles, reviews, ratings, and social media posts to determine sentiment changes for hotel guests.
Listening to what's being said about your brand can be invaluable for any business. Humans can identify positive and negative sentiments, identify slang, sarcasm, irony, and more. However, the enormous volumes of chatter on the internet make it difficult to determine the overall public sentiments. No need to get anxious, that is exactly what sentiment analysis tools are for. Sentiment analysis tools can help you compile and analyze everything that's being said about your brand.
Sentiment analysis is analytical technique that uses statistics, natural language processing, and machine learning to determine the emotional meaning of communications. Companies use sentiment analysis to evaluate customer messages, call center interactions, online reviews, social media posts, and other content. Sentiment analysis can track changes in attitudes towards companies, products, or services, or individual features of those products or services. Get the latest insights with our CIO Daily newsletter. One of the most prominent examples of sentiment analysis on the Web today is the Hedonometer, a project of the University of Vermont's Computational Story Lab.
In this course, you will learn NLP (natural language processing) with deep learning. This course will teach you word2vec and how to implement word2vec. You will also learn how to implement GloVe using gradient descent and alternating least squares. This course uses recurrent neural networks for named entity recognition. Along with that, you will learn how to implement recursive neural tensor networks for sentiment analysis. Let's see the topics covered in this course-
Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. It includes word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering. In our formative years, we master the basics of spoken and written language. However, the vast majority of us do not progress past some basic processing rules when we learn how to handle text in our applications. Yet unstructured software comprises the majority of the data we see.