Everybody has heard about fake news. Fake news is a neologism that can be formally defined as a type of yellow journalism or propaganda that consists of deliberate disinformation or hoaxes spread via traditional print and broadcast news media or online social media. It is also commonly used to refer to fabricated or junk news, with no basis in fact, but presented as being factually accurate. The reason for putting someone's efforts in creating fake news is mainly to cause financial, political or reputational damage to people, companies or organizations, using sensationalist, dishonest, or outright fabricated headlines to increase readership and dissemination among readers using viralization. In addition, clickbait stories, a special type of fake news, earn direct advertising revenue from this activity.

Although there's quite some research done on irony detection, there's still not enough data generated to use for the actual training. The solution can be a large-scale irony dataset, which will allow to create complex training models and provide more accurate sentiment analysis. In order to address the lack of irony data the study analyzes more than 2 million tweets and proposes a novel model to transfer non-ironic sentences to ironic sentences in an unsupervised way. This is the first research that generates ironic sentences to achieve a high irony accuracy with well-preserved sentiment and content.

Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to "predict the future through analysing the past" – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans. Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new "alternative" data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.

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?