Cambridge Analytica may have become the byword for a scandal, but it's not entirely clear that anyone knows exactly what that scandal is. It's more like toxic word association: "Facebook", "data", "harvested", "weaponised", "Trump" and, in this country, most controversially, "Brexit". It was a media firestorm that's yet to be extinguished, a year on from whistleblower Christopher Wylie's revelations in the Observer and the New York Times about how the company acquired the personal data of tens of millions of Facebook users in order to target them in political campaigns. This week sees the release of The Great Hack, a Netflix documentary that is the first feature-length attempt to gather all the strands of the affair into some sort of narrative – though it is one contested even by those appearing in the film. "This is not about one company," Julian Wheatland, the ex-chief operating officer of Cambridge Analytica, claims at one point. "This technology is going on unabated and will continue to go on unabated.[…] There was always going to be a Cambridge Analytica. It just sucks to me that it's Cambridge Analytica."
The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers-- comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain--s representation, and many more.
CEOs and CFOs are decidedly more nervous when fielding questions about China during earnings calls this year. What's more, they are more likely to be deceptive with their answers. "Deception associated with questions on China has skyrocketed this quarter, up about 50% from last quarter and more than double a year ago," according to a study by text analytics provider Amenity Analytics. Amenity Analytics is one of a handful of companies that are applying natural language processing (NLP), sentiment analysis and machine learning to the financial sector, evaluating earnings calls and other public meetings to unearth information of value to an investor. It is also rare technology that offers a clear path to ROI.
Tech employees are taking a stand against migrant detention centers; a proposal asking tech companies to disclose the value of your data; and a live reading of the Mueller report. 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? This afternoon, 550 employees at the Boston-based ecommerce company Wayfair staged a walkout opposing sale of company furniture to migrant detention centers. Last week, Wayfair workers discovered an order for $200,000 worth of beds and other furniture reportedly placed by government contractor BCFS for a new detention center in Carrizo Springs, Texas.
From a trader's point of view, there is one commodity that is worth infinitesimally more than any other. And it's not cutting-edge technology, advanced technical analysis, or profound macroeconomic insight – although these are undoubtedly hugely valuable – it's information. Not just any information – after all, the world is filled with more information than even the most powerful computers could hope to store, and the most intelligent brains could hope to begin to comprehend. No, there's one type of information that has the potential to give traders a bigger edge than any other, and that's the latest information. Information that the rest of the market has yet to factor into their equations.
I hope you are enjoying the "Advanced Analytics Introduction" blog post series; here is a link to the previous segment (Step One) to provide some helpful background. In the previous installment, I provided an overview of the advanced analytics, data science and text analytics concepts. In this blog post, I review detailed definitions of text analytics and mining concepts to provide more context on this rapidly evolving market. In his book "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications", John Elder, Ph.D., characterized the text analytics concept best when he stated the following: The diagrams below also come from the same publication by Dr. Elder. In this first diagram, the text mining field is separated into seven "practice areas."
City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the biggest channels of public expression and is often used to communicate opinions and content related to City Logistics. The idea of this research is that analysing social media content could help in understanding the public perception of City logistics. This paper offers a methodology for collecting content from Twitter and implementing Machine Learning techniques (Unsupervised Learning and Natural Language Processing), to perform content and sentiment analysis. The proposed methodology is applied to more than 110 000 tweets containing City Logistics key-terms. Results allowed the building of an Interest Map of concepts and a Sentiment Analysis to determine if City Logistics entries are positive, negative or neutral.
Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models.
There's a relatively simple formula which describes "weak" or "narrow" artificial intelligence: AI ML TD HITL. To be more specific, this is the definition of supervised machine learning, which is the most common method to produce artificial intelligence. Strong AI – as defined by the Turing test – is when a human has a conversation with a machine and cannot tell it was not a human, based on the way it responds to questions. Over 90% of all human knowledge accumulated since the beginning of time, is unstructured data i.e. text, images, audio and video. The other 10% are numbers in tables which is what quantitative market researchers usually use.
Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of negation in customer service interactions, particularly applied to sentiment analysis. We define rules to identify true negation cues and scope more suited to conversational data than existing general review data. Using semantic knowledge and syntactic structure from constituency parse trees, we propose an algorithm for scope detection that performs comparable to state of the art BiLSTM. We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network. We propose an antonym dictionary based method for negation applied to a CNN-LSTM combination model for sentiment analysis. Experimental results show that the antonym-based method outperforms the previous lexicon-based and neural network methods.