Massive power outages won't save California, Twitter misused your two-factor authentication data, and scientists now know where lightning strikes twice (as much as anywhere else). Here's the news you need to know, in two minutes or less. Want to receive this two-minute roundup as an email every weekday? Power shutoffs can't save California from wildfire hell On Wednesday night, PG&E started shuting off power for hundreds of thousands of California residents in an effort to prevent wildfires during a high-wind period. Though this may be necessary as a stopgap, shutoffs won't save California from wildfires entirely.

Using Facebook Geoinsights, UNICEF was able to confirm internet connectivity was still functioning in the affected area which opened up a new opportunity by working with WhatsApp in the aftermath of... Using Facebook Geoinsights, UNICEF was able to confirm internet connectivity was still functioning in the affected area which opened up a new opportunity by working with WhatsApp in the aftermath of the tsunami to quickly collect needs and provide information to stay alive. Photo credit to UNICEF/UN0240792/Wilander. Rido Saputra, 10 years old, stands in front of his home which was destroyed by a tsunami in Donggala Regency, Central Sulawesi.

I was given huge autonomy over my project -- which revolved around NLP and sentiment analysis -- and was treated like any other full time employee. Ushur was a perfect place to learn, grow, and jumpstart my career in Silicon Valley.

With so many different possibilities for implementing text analytics in your organization, you'll want to narrow down your use case before evaluating your options. Choose a source of text to analyze. Rich, unstructured customer feedback such as survey verbatims, product reviews, and support tickets often lay untapped within your organization. Choose data that you would read yourself if you had the time and resources to do so. Decide how much to analyze and how often.

In 2012 or so, if you'd asked most people in tech about'neural networks', if they had any answer at all they might well have said that it was an obscure idea from the 1980s that had never really worked - rather like VR. Then, in 2013, Imagenet gave us an explosive realisation that this could work now - again, rather like VR in 2013. Since then, the tech industry has been remaking itself around machine learning. There's a naive view that'Google will have all the data' or China will have all the AI' or'Data is the new oil', but it's more interesting to look at how many different kinds of deployment are now happening. The first phase was the creation of companies building platforms (or'primitives' or'substrates') for specific low-level ML applications - image recognition, voice recognition, sentiment analysis etc.

Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts.

Sometimes it happens that brands need to have a sentiment analysis. Knowing how people talk about your brand is essential. What do your community members think about your company? Do they praise you or do they mock of you? Are they sincerely impressed or that enthusiasm hides a sarcastic and brutal critic?

Abstract: With the rapid growth in the number of scientific publications, year after year, it is becoming increasingly difficult to identify quality authoritative work on a single topic. Though there is an availability of scientometric measures which promise to offer a solution to this problem, these measures are mostly quantitative and rely, for instance, only on the number of times an article is cited. With this approach, it becomes irrelevant if an article is cited 10 times in a positive, negative or neutral way. In this context, it is quite important to study the qualitative aspect of a citation to understand its significance. This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e.g., reference to a dataset, reading reference. Furthermore, the paper also presents two datasets (SentiCiteDB and IntentCiteDB) containing about 2,600 citations with their ground truth for sentiment and nature of citation. SentiCite along with other state-of-the-art methods for sentiment analysis are evaluated on the presented datasets. Evaluation results reveal that SentiCite outperforms state-of-the-art methods for sentiment analysis in scientific publications by achieving a F1-measure of 0.71. 1 INTRODUCTION Sentiment analysis is the process of computationally categorizing and identifying opinions present in a textual document or images. As a field, sentiment analysis has been gaining a lot of interest from the scientific community in recent years. The main motivation for this work comes from the author's observation that there is an unavailability of a system capable of automatically analyzing the sentiment present in citations of scientific publications.

Amazon's newest set of electronic devices was a trending topic in recent times. This use case leverages Data Mining, Natural Language Processing, Machine Learning, and Data Visualization, to build algorithms that perform sentiment analysis on online product reviews and help us understand the consumer sentiments on electronic products available on Amazon. The model can be helpful for any eCommerce business to ascertain the consumer sentiment towards its products and brands. Often, online reviews are large in numbers and are unstructured. Understanding the true intent of the consumers from their reviews can be a difficult task as there arise many barriers such as language ambiguity, sarcasm, irony, and the emojis (emotion icons).

Sentiment classification is an important process in understanding people's perception towards a product, service, or topic. Many natural language processing models have been proposed to solve the sentiment classification problem. However, most of them have focused on binary sentiment classification. In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task. Experiments show that our model outperforms other popular models for this task without sophisticated architecture. We also demonstrate the effectiveness of transfer learning in natural language processing in the process.