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sentiment analysis

5 Ways AI is Changing Public Relations


The application of AI technologies has promoted a revolution in the business industry. When it comes to the public relations industry, AI applications also change the original workflow. Nowadays, to become a good PR participant, an employee is not only required to have practiced communications skills but also the ability to collaborate with AI-based platforms. This article will show you the new vision of public relations, exploring the 5 ways AI is changing the industry. With the digital transformation of business, social media now has become a critical battlefield for business operations.

Stemming vs Lemmatization in NLP: Must-Know Differences


This article was published as a part of the Data Science Blogathon. In the field of Natural Language Processing i.e., NLP, Lemmatization and Stemming are Text Normalization techniques. These techniques are used to prepare words, text, and documents for further processing. Languages such as English, Hindi consists of several words which are often derived from one another. Further, Inflected Language is a term used for a language that contains derived words. For instance, word "historical" is derived from the word "history" and hence is the derived word.

An easy tutorial about Sentiment Analysis with Deep Learning and Keras


Get comfortable, it's going to take you several minutes to read but hopefully, you'll stick with me along the whole article. I'm gonna walk you through a foundational task that you as data scientist/machine learning engineer must know how to perform because at some point of your career you'll be required to do so. In the context of this article, I'll assume you have a basic understanding of what I'm going to talk in the next lines. I'll be stacking layers of concepts as I move forward, keeping a very low-level language -- don't worry if you fell a little lost between lines, later I will probably clarify your doubts. The main idea is for you to understand what I'll be explaining.

Sentiment Analysis on Solar Energy with NLP and Python


Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. "When captured electronically, customer sentiment -- expressions beyond facts, that convey mood, opinion, and emotion -- carries immense… It's free, we don't spam, and we never share your email address.



If the question'What is sentiment analysis?' popped up in your mind as you clicked on this blog, I think you will find my first blog in this series interesting. Essentially, sentiment analysis is a natural language processing technique used to determine the emotional tone of textual data. It is primarily used to understand customer satisfaction, and gauge brand reputation, call center interactions as well as customer feedback and messages. There are various types of sentiment analysis that are common in the real world. In this part of my blog series, let me walk you through the implementation of sentiment analysis.

Physiological signals could be the key to 'emotionally intelligent' AI, scientists say: Researchers integrate biological signals with gold-standard machine learning methods to enable emotionally intelligent speech dialog systems


"Multimodal sentiment analysis" is a group of methods that constitute the gold standard for an AI dialog system with sentiment detection. These methods can automatically analyze a person's psychological state from their speech, voice color, facial expression, and posture and are crucial for human-centered AI systems. The technique could potentially realize an emotionally intelligent AI with beyond-human capabilities, which understands the user's sentiment and generates a response accordingly. However, current emotion estimation methods focus only on observable information and do not account for the information contained in unobservable signals, such as physiological signals. Such signals are a potential gold mine of emotions that could improve the sentiment estimation performance tremendously.

11 Best Natural Language Processing Online Courses


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-

Real Time Twitter Sentiment Analysis.


Every day a large number of social media users are produced who can be used to analyze their ideas on any event, film, product or politics. Common tools like Apache Storm analyze streams in micro-batch while novel tools like Apache Spark process data in real time to make analyzing and processing real-time data possible.

NLP and Sentiment Analysis for Beginners


This program will give you in-depth knowledge of how NLP and sentiment analysis helps you determine the emotional meaning of communications. This program will give you in-depth knowledge of how NLP and sentiment analysis helps you determine the emotional meaning of communications. You'll learn how NLP applications and Sentiment analysis help you to read, understand, and decode human words in a valuable manner. This program will walk you through different NLP algorithms, and you'll get practical knowledge on how to write code in Python, and implement NLP algorithms. This program will help you learn NLP, Sentiment Analysis, and Deep Learning from basic to advance.