sentiment


Deep Learning for Natural Language Processing (NLP) – using RNNs & CNNs

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Wouldn't it be cool if a computer could understand the actual human sentiment behind sarcastic texts that can sometimes even trump actual humans? Or what if computers could understand a human language so well that it can estimate a probability telling you how likely it is to encounter any random sentence that you give it? Or maybe it could generate completely fake code snippets of the Linux kernel that look so authentic that they are just as intimidating as the actual source code (well, unless you are a kernel programmer yourself)? What if computers could immaculately translate English to French or over 100 languages from all over the world? Or "see" an image and describe the items found in the photo?


How AI Insights Benefit Wealth management

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Artificial Intelligence (AI) can increase speed to market, firm's profitability, improve service offerings and wealth advisor-client relationships and lastly business outcomes. Since AI can provide recommendations to advisors and directly to the clients in setting goals and in investing there is a vast realm of possibilities to speed up processes that can take a human advisor far longer. In financing sector financial institutions are already harnessing the potential of AI to solve challenges related to investment advisory, risk management, regulatory compliance, and prediction of markets. With cognitive technologies, wealth management and advisory business have transformed vastly. AI can build the classification models in such a way that it can classify each investor to a particular investment strategy class, highly customized for investors with similar profiles and goals.


Sentiment Classification with Natural Language Processing on LSTM

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LSA itself is an unsupervised way of uncovering synonyms in a collection of documents.To start, we take a look how Latent Semantic Analysis is used in Natural Language Processing to analyze relationships between a set of documents and the terms that they contain. Then we go steps further to analyze and classify sentiment. We will review Chi Squared for feature selection along the way. We will use Recurrent Neural Networks, and in particular LSTMs, to perform sentiment analysis in Keras. Since, text is the most unstructured form of all the available data, various types of noise are present in it and the data is not readily analyzable without any pre-processing.


Using NLP and Machine Learning for Long Term Investing

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The following figure highlights the consistent and impressive performance of the NP Machine Learning model across the US market (Russell 3000) over the last 15 years.


A.I. Judges: The Future of Justice Hangs in the Balance

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In 1970, Lyudmila Terentyevna Aleksandrova lost her right hand. It happened at work, where she was employed by the Russian state. With her hand gone, she fought for a disability allowance that never materialized, batted about by district and regional courts. Eventually, after decades of frustration, she brought the case to the European Court of Human Rights, which ruled in 2007 that there had been a violation in Aleksandrova's right to a fair trial. Pay the money, it told Russia.


NLP and Sarcasm: What's the Deal? – Towards Data Science

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As humans, you and I can look at these two chats and determine that in the first the person appears to be sincere while in the other comes off sarcastic and cold simply due to the way it was punctuated. This may seem fairly obvious. Yet for chatbots and natural language processing algorithms, these two responses tend to appear identical. When taken in the literal sense, there's no reason to assume that both people had the same reaction. Removing our understanding of social cues hinders our ability to discern the true intention of the message.


The Best 25 Datasets for Natural Language Processing Gengo AI

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Where's the best place to look for free online datasets for NLP? We combed the web to create the ultimate cheat sheet, broken down into datasets for text, audio speech, and sentiment analysis. Sentiment140: a popular dataset, which uses 160,000 tweets with emoticons pre-removed. Twitter US Airline Sentiment: Twitter data on US airlines from February 2015, classified as positive, negative, and neutral tweets. Yelp Reviews: An open dataset released by Yelp, contains more than 5 million reviews.


Get Started with AI in 15 minutes by Building Text Classifiers on Airbnb Reviews

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Watson Natural Language Classifier (NLC) is a text classification (aka text categorization) service that enables developers to quickly train and integrate natural language processing (NLP) capabilities into their applications. Once you have the training data, you can set up a classification model (aka a classifier) in 15 minutes or less to label text with your custom labels. In this tutorial, I will show you how to create two classifiers using publicly available Airbnb reviews data. One of the more common text classification patterns I've seen is analyzing and labeling customer reviews. Understanding unstructured customer feedback enables organizations to make informed decisions that'll improve customer experience or resolve issues faster.


Machine Learning Projects: Challenges and Best Practices

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He was previously the founder of Figure Eight (formerly CrowdFlower). This blog post provides insights into why machine learning teams have challenges with managing machine learning projects. He also provides best practices on how to address these challenges. This post was provided courtesy of Lukas and originally appeared on Medium. I've watched lots of companies attempt to deploy machine learning -- some succeed wildly and some fail spectacularly.


Computers are becoming multilingual -- and NLP experts are in great demand

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In the middle of the highway, my father turned to our car navigation system for directions. Before chalking out a route from Calcutta to Santiniketan, the system asked our language preference -- would we want to be guided through the alleys, bridges and villages in English, Bengali, Hindi, Marathi, Malayalam, the list went on and on. We have come a long way since the days when the only way to communicate with devices was via the keyboard or keypad. But how does a computer, or any system built on technology, think, understand, and, in the case of car navigation systems, even speak in regional languages? Artificial intelligence, of course -- one of the most important parts of which is the concept of natural language processing or NLP.