Kremlin analysts could have used Twitter as a source of military intelligence to inform their actions in the 2014 Russia–Ukraine conflict, a study has found. University of California experts showed that location-tagged tweets by Ukraine residents could have been used to map out sentiments towards Russia in real-time. The map they made of pro-Kremlin regions turned out to bear a striking resemblance to the actual areas to which Russia dispatched its special forces. Specifically, this included Crimea and regions in the far east of Ukraine -- where the incoming forces would have been most likely to be seen as liberators. In contrast, the data could also reveal those areas where dispatching forces would have lead to greater resistance and corresponding casualties and costs.
How do you test an application which constantly listens to the customers, learns their behaviour and create personalised engagements based out of learnings!! Today data plays a vital role in every decision making and hence making sense of the data to derive useful insights for our customers is a key for success. Sentiment Analysis is the process of classifying the data into positive, negative or neutral implemented using natural language processing (NLP) and Machine Learning techniques that helps in analysing the data to gauge public opinion, market research, monitor brand and product reputation, and understand customer experiences and is mostly offered as Sentiment Analysis as-a-Service . In this talk we will discuss the Challenges are around analysing, explicit and implict opinions, sarcasm, comparative opinions, Multilingual, Emojis, defination on neutral to just name a few and the strategies to test such applications with a use case on Airlines Sentiment (trained with tweets about airlines to identify between positive, neutral, and negative tweets).
Exploratory data analysis is one of the most important parts of any machine learning workflow and Natural Language Processing is no different. But which tools you should choose to explore and visualize text data efficiently? In this article, we will discuss and implement nearly all the major techniques that you can use to understand your text data and give you a complete(ish) tour into Python tools that get the job done. In this article, we will use a million news headlines dataset from Kaggle. Now, we can take a look at the data. The dataset contains only two columns, the published date, and the news heading. For simplicity, I will be exploring the first 10000 rows from this dataset.
Sentiment is a huge driving factor in the cryptocurrency market. But it is a metric which is very hard to measure. Sentiment analysis has been on the rise for the past few years. With the introduction of new packages, sentiment analysis can be done more quickly and efficiently than ever. In this post, you'll see why looking at the mood on the social media is not a great idea for sentiment analysis.
Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes. Given a trained recurrent network, we would like to reverse engineer it--to obtain a quantitative, interpretable description of how it solves a particular task. Even for simple tasks, a detailed understanding of how recurrent networks work, or a prescription for how to develop such an understanding, remains elusive. In this work, we use tools from dynamical systems analysis to reverse engineer recurrent networks trained to perform sentiment classification, a foundational natural language processing task. Given a trained network, we find fixed points of the recurrent dynamics and linearize the nonlinear system around these fixed points.
In this particular case, in only 10 seconds and with the small dataset provided, the CLI tool was able to run quite a few iterations, meaning training multiple times based on different combinations of algorithms/configuration with different internal data transformations and algorithm's hyper-parameters. Finally, the "best quality" model found in 10 seconds is a model using a particular trainer/algorithm with any specific configuration. Depending on the exploration time, the command can produce a different result. The selection is based on the multiple metrics shown, such as Accuracy. The first and easiest metric to evaluate a binary-classification model is the accuracy, which is simple to understand.
Using machine learning to comb through exercise-related tweets, researchers identified regional and gender differences in exercise types and intensity levels, providing insights into possible interventions that target certain communities, according to the findings of a study published in BMJ Open Sport & Exercise Medicine. The machine-learning method also allowed researchers to see how different populations feel about different kinds of exercise. The findings revealed that walking was the most popular physical activity for both men and women across all regions. Men and women also mentioned performing gym-based activities at similar rates, with men mentioning such activities in approximately 4.68% of tweets, compared to 4.13% for women. Among these tweets, CrossFit was the most popular among men's tweets, showing up in approximately 14.91%.
This edition of the conference on'Financial Evolution AI, Machine Learning & Sentiment Analysis' by UNICOM Seminars interrogates and explores the implications of AI & ML in the financial services industry. 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.
Security expert Bob Diachenko, along with Comparitech, has discovered more than 267 million Facebook user IDs, phone numbers and names in an unsecured database. The huge trove of data is likely the result of an illegal scraping operation or Facebook API abuse by a group of hackers in Vietnam. The exposed data could be used by threat actors to conduct large-scale SMS spam and phishing campaigns. "A database containing more than 267 million Facebook user IDs, phone numbers, and names was left exposed on the web for anyone to access without a password or any other authentication." "Comparitech partnered with security researcher Bob Diachenko to uncover the Elasticsearch cluster.