Natural Language Processing is considered one of the many critical aspects of making intelligent systems. By training your solution with data gathered from the real-world, you can make it faster and more relevant to users, generating crucial insight about your customer base. In this article, we will be taking a look at how Python offers some of the most useful and powerful libraries for leveraging the power of Natural Language Processing into your project and where exactly do they fit in. Often recognized as a professional-grade Python library for advanced Natural Language Processing, spaCy excels at working with incredibly large-scale information extraction tasks. Built using Python and Cython, spaCy combines the best of both languages, the convenience from Python and the speed from Cython to deliver one of the best-in-class NLP experiences. Stanford CoreNLP is a suite of tools built for implementing a Natural Language Processing into your project.

Basic Sentiment Analysis with TensorFlow Welcome to this project-based course on Basic Sentiment Analysis with TensorFlow. Welcome to this project-based course on Basic Sentiment Analysis with TensorFlow. In this project, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic sentiment analysis problem. By the end of this 2-hour long project, you will have created, trained, and evaluated a Neural Network model that, after the training, will be able to predict movie reviews as either positive or negative reviews – classifying the sentiment of the review text. Welcome to this project-based course on Basic Sentiment Analysis with TensorFlow.

Having understood the context and starting point, now we will go a little further with the interaction between our two individuals and their open relationship (still maintaining their anonymity, of course, as "Él" (He) and "Ella" (She)), analyzing the diversity of vocabulary and performing sentiment analysis based on the expressed emojis. Okay, so going back, using the same libraries, same defined variables, and the same txt file so far, let's continue. You will remember that in the first part, using the stopwords() function, we discriminate the words whose meaning is little or nothing relevant. Based on this and looking for words that are repeated only by the same user, we can measure the diversity of vocabulary. So we will obtain as a result the following plot where we can see that She is the one who has the greatest diversity of lexicon.

Arvind Gopalakrishnan is a part of the AIM Writers Programme.… Data mining is taking turns in the industry like anything, but have you ever heard of Opinion Mining? Leveraging customer opinion as quantifiable data is a concept of future to a layman but with Natural Language Processing, the world can finally process and completely absorb customer feedback. Often data is associated with quantity-based statistics with numbers and metrics floating around, however, with natural language processing (NLP), qualitative factors like customer feedback can be processed and used as quantifiable data. For example, if a specific mobile phone models witness a higher number of sales in a given year, the manufacturers tend to incorporate features of that mobile phone to increase the sales of other models where they somehow miss to make upgrades properly basis the customer feedback.

The FBI and Justice Department are assisting the Altoona Police Department's investigation after a noose was found last month at a work site on the Facebook Data Center property in Altoona, Iowa. Altoona police officials say they contacted the FBI on June 19, the day the noose was found. The date coincided with Juneteenth, the annual holiday celebrating the end of slavery. Interviews are still being conducted in the investigation, according to Altoona Police Department Public Information Officer Alyssa Wilson. While federal investigators were already involved with the incident, as of Thursday, all information in the case will be filtered through the FBI's Omaha office.

Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in the form of backdoors during training. The goal of a backdoor is to corrupt the performance of the trained model on specific sub-tasks (e.g., by classifying green cars as frogs). A range of FL backdoor attacks have been introduced in the literature, but also methods to defend against them, and it is currently an open question whether FL systems can be tailored to be robust against backdoors. In this work, we provide evidence to the contrary. We first establish that, in the general case, robustness to backdoors implies model robustness to adversarial examples, a major open problem in itself. Furthermore, detecting the presence of a backdoor in a FL model is unlikely assuming first order oracles or polynomial time. We couple our theoretical results with a new family of backdoor attacks, which we refer to as edge-case backdoors. An edge-case backdoor forces a model to misclassify on seemingly easy inputs that are however unlikely to be part of the training, or test data, i.e., they live on the tail of the input distribution. We explain how these edge-case backdoors can lead to unsavory failures and may have serious repercussions on fairness, and exhibit that with careful tuning at the side of the adversary, one can insert them across a range of machine learning tasks (e.g., image classification, OCR, text prediction, sentiment analysis).

It is stated that data scientists spend almost 70% of their time on data cleaning. It is one of the most tedious tasks. The model's performance is directly proportional to how clean your data is. Here cleaning includes removing duplicate data, unnecessary elements, and handling missing data. We will perform a couple of standard cleaning techniques before we preprocess the text.

By Natalia Modjeska, MBA, PhD, helps organizations make sense of AI/ML. Recently I had the opportunity to attend the inaugural Emotion AI Conference, organized by Seth Grimes, a leading analyst and business consultant in the areas of natural language processing (NLP), text analytics, sentiment analysis, and their business applications. The conference was attended by about 70 people (including presenters and panelists) from industry and academia in the US, Canada, and Europe. Given the conference topic, what is emotion AI, why is it relevant, and what do you need to know about it? Read on to find out (warning: this is a long-ish article), but first, some background. We humans are highly emotional beings, and emotions impact everything we do, even if we are not, for the most part, aware of it.

The Github repo for this post contains a notebook and the data needed to generate some of the charts in this post, as well as a sample of the Plotly chart and CSV table of the results. The code can be easily tweaked if you wish to generate results for multiple speeches in one go. The data comprises six official speech transcripts taken from the websites of the Singapore Government as well as the Prime Minister's Office. These speeches focused on the Government's plans to deal with the challenges from Covid-19, and are set to frame the broader debate for Singapore's upcoming election. Some excessively long chunks of text were broken up into smaller paragraphs for a fairer assessment of the sentiment, but the vast majority of the speeches were analysed in their original form.

This work proposes a structured approach to baselining a model, identifying attack vectors, and securing the machine learning models after deployment. This method for securing each model post deployment is called the BAD (Build, Attack, and Defend) Architecture. Two implementations of the BAD architecture are evaluated to quantify the adversarial life cycle for a black box Sentiment Analysis system. As a challenging diagnostic, the Jigsaw Toxic Bias dataset is selected as the baseline in our performance tool. Each implementation of the architecture will build a baseline performance report, attack a common weakness, and defend the incoming attack. As an important note: each attack surface demonstrated in this work is detectable and preventable. The goal is to demonstrate a viable methodology for securing a machine learning model in a production setting.