A data-harvesting competition that offered football fans the chance to win £50m is at the centre of new questions about pro-Brexit campaigning before the 2016 EU referendum. Last week the select committee for digital, culture, media and sport released a letter Facebook sent to the Electoral Commission in which it said that two campaigns, Vote Leave and BeLeave, used three sets of data to target audiences, noting that they covered "the exact same audiences". The two campaign groups are under investigation over whether there was collusion and coordination during the referendum campaign, circumventing spending limits. Under British electoral law, it is illegal for campaigns to work together in any way unless they declare their spending jointly, which Vote Leave and BeLeave did not. Both organisations deny any collusion.

The industrial revolution replaced workers with machines, forcing more of them towards the services sector. The digital revolution is now attacking this area through chatbots that aim to be at least as good as entry-level customer service representatives or shopping assistants. Gartner says that by 2020, the customer will manage 85% of their interactions with a company without dealing with a human. The goal is to create conversational instances which don't sound or behave like robots, but as human-like as possible. To achieve this ambitious target, the chatbot needs to understand language, context, tone and even subtle nuances like sarcasm. The tool which can enhance this is sentiment analysis, a process which automatically extracts both the topic and the feeling from the sentence or voice input.

Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions). Learn why it's useful and how to approach the problem. There are Rule-Based and ML-Based approaches. The details are really important - training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set.

The future of work will depend highly on soft skills. No matter how AI for recruitment and talent assessment is leveraged in the future, a candidate's high-order thinking and EQ will stay vital, something which the robots simply can't replace or automate! This accurate AI-powered tool (beyond IBM Watson) gives you full picture of a candidate's soft skill background (based on the Big 5 personality test, DISC OCEAN, mood graphs, sentiment analysis, digital footprint analysis, behavior score, and much more) to help recruiters spot and process the right'candidates' who would add to their diverse, inclusive company culture. Get a free assessment report, at: https://frrole.ai/deepsense-app/ You just need the twitter handle/ email ID of the individual to get started.

This is part IV of our tour through the papers from the Re-coding Black Mirror workshop exploring future technology scenarios and their social and ethical implications. In 2016, the world witnessed the storming of social media by social bots spreading fake news during the US Presidential elections… researchers collected Twitter data over four weeks preceding the final ballot to estimate the magnitude of this phenomenon. Their results showed that social bots were behind 15% of all accounts and produced roughly 19% of all tweets… What would happen if social media were to get so contaminated by fake news that trustworthy information hardly reaches us anymore? Fake news and hoaxes have been around a long time, but the nature of social media pours fuel on the fire. Any user can create and relay content with little third-party filtering or fact-checking; many adults get their news on social media; and research has shown that people exposed to fake news tend to believe it.

This track includes data-related tasks such as analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy, with special focus on social data on the web. Hence, the broader context of the track comprehends AI, web mining, information retrieval, natural language processing, and sentiment analysis. As the web rapidly evolves, web users are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the social web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction. The primary aim of this track is exploring the new frontiers of big data computing for opinion mining and sentiment analysis through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the web.

Event Extraction (EE) is a challenging Information Extraction task which aims to discover event triggers of specific types along with their arguments. Most recent research on Event Extraction relies on pattern-based or feature-based approaches, trained on annotated corpora, to recognize combi- nations of event triggers, arguments, and other contextual in- formation. However, as the event instances in the ACE corpus are not evenly distributed, some frequent expressions involving ACE event triggers do not appear in the training data, adversely affecting the performance. In this paper, we demon- strate the effectiveness of systematically importing expert-level patterns from TABARI to boost EE performance. The experimental results demonstrate that our pattern-based sys- tem with the expanded patterns can achieve 69.8% (with 1.9% absolute improvement) F-measure over the baseline, an advance over current state-of-the-art systems.

We seek to determine the effectiveness of using location-based social media to predict the outcome of the 2016 presidential election. To this aim, we create a dataset consisting of approximately 3 million tweets ranging from September 22nd to November 8th related to either Donald Trump or Hillary Clinton. Twenty-one states are chosen, with eleven categorized as swing states, five as Clinton favored and five as Trump favored. We incorporate two metrics in polling voter opinion for election outcomes: tweet volume and positive sentiment. Our data is labeled via a convolutional neural network trained on the sentiment140 dataset. To determine whether Twitter is an indicator of election outcome, we compare our results to the election outcome per state and across the nation. We use two approaches for determining state victories: winner-take-all and shared elector count. Our results show tweet sentiment mirrors the close races in the swing states; however, the differences in distribution of positive sentiment and volume between Clinton and Trump are not significant using our approach. Thus, we conclude neither sentiment nor volume is an accurate predictor of election results using our collection of data and labeling process.

This track is intended to present works ranking from logical, mathematical, and statistical models in syntax, semantics (logic of objects, topological theories of time and space, lexical associations, etc.) and discourse as foundations of the design and analysis to knowledge processing and natural language processing systems and especially to information extraction.

The U.S. Justice Department and the FBI are investigating Cambridge Analytica, a now-defunct political data firm embroiled in a scandal over its handling of Facebook Inc user information. Prosecutors have sought to question former Cambridge Analytica employees and banks that handled its business, the newspaper said, citing an American official and others familiar with the inquiry. Cambridge Analytica said earlier this month it was shutting down after losing clients and facing mounting legal fees resulting from reports the company harvested personal data about millions of Facebook users beginning in 2014. Allegations of the improper use of data for 87 million Facebook users by Cambridge Analytica, which was hired by President Donald Trump's 2016 U.S. election campaign, have prompted multiple investigations in the United States and Europe. The investigation by the Justice Department and FBI appears to focus on the company's financial dealings and how it acquired and used personal data pulled from Facebook and other sources, the Times said.