The goal is to be able to make SENTIMENT ANALYSIS prediction/detection of what the user is writing in a very UI interactive app (BLAZOR based) in the client side and running an ML.NET model (Sentiment analysis based on binary-classification) in the server side. From ML.NET perspective, the goal is to optimize the ML.NET model executions in the server by sharing the ML.NET objects used for predictions across Http requests and being able to implement very simple code to be used by the user when predicting, like the following line of code that you could write on any ASP.NET Core controller's method or custom service class: The object predictionEnginePool will be injected in the controller's constructor or into you custom class. Internally, it is optimized so the object dependencies are cached and shared across Http requests with minimum overhead when creating those objects. Blazor allows you to run C# code in the client side, as shown in the architecture figure. For this sample we chose to run the ML.NET model in the server side, so the model is protected within the service.

This post is co-authored by Tina Coll, Senior Product Marketing Manager, Azure Cognitive Services and Anny Dow, Product Marketing Manager, Azure Cognitive Services. Azure Cognitive Services brings artificial intelligence (AI) within reach of every developer without requiring machine learning expertise. All it takes is an API call to embed the ability to see, hear, speak, understand, and accelerate decision-making into your apps. Enterprises have taken these pre-built and custom AI capabilities to deliver more engaging and personalized intelligent experiences. We're continuing the momentum from Microsoft Build 2019 by making Personalizer generally available, and introducing additional advanced capabilities in Vision, Speech, and Language categories.

The General Language Understanding Evaluation (GLUE) benchmark is widely used to evaluate Natural Language Processing (NLP) models. Although GLUE includes a range of English sentence-pairing, word prediction and other NLP tasks, it cannot evaluate the performance of Chinese NLP models. Now, a group of NLP researchers and enthusiasts, including graduates from Tsinghua University, Peking University, and Zhejiang University, have introduced ChineseGLUE, a benchmark designed to encourage the development and assessment of Chinese language models. GLUE was introduced in 2018 by researchers from New York University, University of Washington and DeepMind. Since then, new pretrained language models such as Google's BERT have rapidly improved performance in Natural Language Understanding (NLU), a NLP research area with a focus on machine reading comprehension through sentiment analysis and grammatical judgment, etc.

Rising demand for artificial intelligence-powered chatbots with sentiment analysis -- a fancy word for emotion -- is creating new growth opportunities for businesses in the area, China Daily reports. "An increasing number of businesses are asking for chatbots with more functions than just being conversation assistants. They want chatbots that have a better sense of empathy, are more interactive, and are able to transform consumer emotions into data and conduct sentiment analysis," said Xu Yiya, vice-president of Xiao-i Robot Technology Co. Ltd., an AI customer service provider. Xu said sentiment analysis can help chatbots better understand consumer needs. Increasing demand for AI-powered chatbots with sentiment analysis is creating new business opportunities in the booming AI sentiment analysis market, which is estimated to see a 21% annual increase from 2019 to 2025, according to market analysis company QYReports, as business owners are worrying that customers may not be satisfied talking to emotionless chatbots.

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.

Distributed denial of service (DDoS) attacks, which are designed to prevent legitimate users from accessing specific network systems, have become increasingly common over the past decade or so. These attacks make services such as Facebook, Reddit and online banking sites extremely slow or impossible to use by exhausting network or server resources (e.g., bandwidth, CPU and memory). Researchers worldwide have been trying to develop techniques to prevent DDoS attacks or rapidly intervene in order to reduce their negative effects. An important step in counteracting such attacks is the prompt collection of feedback from users to determine their impact and come up with targeted solutions. With this in mind, a team of researchers at the University of Maryland have developed a machine-learning model that could help to determine the scale of impact of DoS attacks as they are happening based on tweets posted by users.

This article provides a brief introduction to natural language using spaCy and related libraries in Python. The complementary Domino project is also available. This article and paired Domino project provide a brief introduction to working with natural language (sometimes called "text analytics") in Python using spaCy and related libraries. Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. Think about it: how does the "operating system" for business work?

The SKLearn lab will have a tutorial for sentiment analysis and mnist (via a Google Colab Notebook) with emphasis on how to improve performance, then time for students to try their own classifiers on a separate sentiment analysis task. The PyTorch lab willhave a tutorial on PyTorch and how to build feed-forward nets for the same tasks as in the Sklearn lab (with emphasis on how to improve performance), and time for students to try to build their own network for the separate sentiment analysis task.

I recently built a movie recommender that takes as input a user written passage about liked and/or disliked movies. At the onset of the project I figured that determining which movies users' liked and disliked would be simple. After all, using text to determine whether someone likes or dislike a movie doesn't seem too ambitious. With the variety of packages readily available for sentiment analysis in python, there had to be something available out of the box to do this job. As it turns out, using text to determine whether someone likes vs dislikes a movie, or any named entity, is deceivingly complex.

The Forrester Wave is a guide for organizations weighing their technology procurement options. Forrester uses a transparent publicly available methodology to compare leading market players so decision-makers can make well-informed choices without spending months conducting their own research. The principal inputs in a Forrester Wave evaluation are an executive strategy briefing, a product demo session, a questionnaire, and customer references. Micro Focus was among select companies Forrester invited to participate in its Q2 2018, Forrester Wave evaluation of AI-based text analytics platforms. In this evaluation, Micro Focus was cited as a Leader in text analytics.