According to Taweh Beysolow, "Natural Language Processing (NLP) is a subfield of computer science that is focused on allowing computers to understand language in a'natural' way, as humans do." NLP has evolved so rapidly gaining traction in its applications inn artificial intelligence (AI). In this project, we will explore one of the most exciting NLP applications i.e. We will build a machine learning model that can categorize tweets as positive (pro-vaccine), negative (anti-vaccine) or neutral. Stay tuned and let's jump into the project.

Sentiment analysis of free-text documents is a common task in the field of text mining. In sentiment analysis predefined sentiment labels, such as "positive" or "negative" are assigned to texts. Texts (here called documents) can be reviews about products or movies, articles, tweets, etc. In this article, we show you how to assign predefined sentiment labels to documents, using the KNIME Text Processing extension in combination with traditional KNIME learner and predictor nodes. A set of 2000 documents has been sampled from the training set of the Large Movie Review Dataset v1.0.

So let's start with a bag of words

Are you ready to kickstart your Advanced NLP course? Are you ready to deploy your machine learning models in production at AWS? You will learn each and every steps on how to build and deploy your ML model on a robust and secure server at AWS. Prior knowledge of python and Data Science is assumed. If you are AN absolute beginner in Data Science, please do not take this course. This course is made for medium or advanced level of Data Scientist.

One year ago Google artificial intelligence researcher Timnit Gebru tweeted, "I was fired" and ignited a controversy over the freedom of employees to question the impact of their company's technology. Thursday, she launched a new research institute to ask questions about responsible use of artificial intelligence that Gebru says Google and other tech companies won't. "Instead of fighting from the inside, I want to show a model for an independent institution with a different set of incentive structures," says Gebru, who is founder and executive director of Distributed Artificial Intelligence Research (DAIR). The first part of the name is a reference to her aim to be more inclusive than most AI labs--which skew white, Western, and male--and to recruit people from parts of the world rarely represented in the tech industry. Gebru was ejected from Google after clashing with bosses over a research paper urging caution with new text-processing technology enthusiastically adopted by Google and other tech companies.

Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.

Oracle has announced availability of Oracle Cloud Infrastructure (OCI) AI services, a collection of services that make it easier for developers to apply AI services to their applications without requiring data science expertise. The new OCI AI services give developers the choice of leveraging out-of-the-box models that have been pretrained on business-oriented data or custom training the services based on their organization's own data. The six new services help developers with a range of complex tasks from language to computer vision, and time-series forecasts. Companies today need AI to accelerate innovation, assess business conditions, and deliver new customer experiences. However, they frequently run into implementation issues ranging from a scarcity of data science expertise, difficulties in training models on relevant business data to getting their platform to work in a live environment or breaking down data silos.

Background: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. Objective: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. Methods: We collected 31,100 English tweets containing COVID-19 vaccine–related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. Results: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. Conclusions: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.

Machine learning for natural language processing or NLP and text analytics involves using machine learning algorithms and AI to understand the meaning of text documents. The role of machine learning and AI in NLP and text analytics is to accelerate the underlying and NLP features that turn this unstructured text into usable data and insights. Let's see the top NLP algorithms to explore in 2021. NLP stands for Natural Language Processing which is a subfield of Artificial Intelligence research. It is focused on the development of models and protocols that will help you in interacting with computers based on natural language.

Since the beginning of the COVID pandemic, there have been more than 700000 scientific papers published on the subject. A human researcher cannot possibly get acquainted with such a huge text corpus — and therefore some help from AI is highly needed. In this post, we will show how we...