In April 2019, following the Easter Sunday bomb attacks, the Government of Sri Lanka had to shut down Facebook and YouTube for nine days to stop the spreading of hate speech and false news, posted mainly in the local languages Sinhala and Tamil. This came about simply because these social media platforms did not have the capability to detect and warn about the provocative content. India's Ministry of Human Resource Development (MHRD) wants lectures on Swayama and NPTELb--the online teaching platforms--to be translated into all Indian languages. Approximately 2.5 million students use the Swayam lectures on computer science alone. The lectures are in English, which students find difficult to understand.

Sign in to report inappropriate content. Our CTO & Co- Founder, Jesus Rodriguez explores the benefits and challenges of traditional techniques such as sentiment analysis, topic/entity extraction or tone analysis methods when it comes to analyzing crypto-assets. We also show how detailed social media analysis techniques can show relevant insights about the behavior of the crypto markets.

BLOOMINGTON, Ind., Oct. 22, 2019 (GLOBE NEWSWIRE) -- Megaputer Intelligence, Inc. will share an innovative new tool for building training datasets for use in machine learning during a presentation at the Text Analytics Forum '19 held in Washington, DC on November 7. Dr. Sergei Ananyan, CEO of Megaputer Intelligence, Inc., will present a cutting-edge topic entitled, "NLP & Rule-Based Approach for Fact Extraction: Launchpad for Machine Learning Techniques" on Thursday, November 7 at 11:15 AM EST. The Text Analytics Forum will host the presentation at the JW Marriott in Washington, DC as part of its comprehensive programming, running from Nov 4-7. The content of the presentation is designed for people interested in discovering how to achieve higher accuracy from machine learning, relieve the burden of needing experts to manually create a gold standard training dataset, and illuminate the black box surrounding machine learning as much as possible with insight into today's latest technological advances. Professionals such as text analysts, data scientists, DBAs, information knowledge architects, knowledge organizers, taxonomists, ontologists, CIOs, CKOs, research scientists, and data quality managers will benefit greatly from this technique to overcome well-known challenges of machine learning. One fundamental obstacle for using machine learning (ML) to accurately extract facts from free-text documents is that it requires huge quantities of pre-categorized data for training a model.

Text analysis is a process that derives meaning from written communication. In the context of the customer experience, text analysis means checking the text that you write or write about. Find the patterns and topics you are interested in, then take practical action based on what you learn. This market report is a thorough analysis of the existing situation and the anticipated condition for Text Analytics Software Market. Investigation for gathering the content for this report is done in-depth and meticulously.

Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC. This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC. First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses. Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses. Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection. Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.

Around 20% of the attorney's time is consumed by their legal research on previous judgments, case files and recordings. Especially in Patent application filing where the prior art search involves manual keyword-based searching for related patents. A good part of the attorney's time is spent on such mundane and repetitive work that they don't have time to invest in the more innovative and creative aspects of patent applications. We are working on building a semantic analysis tool that will do a semantic search on all the documents to pre-select existing closely related to the new cases for the attorney to review. The ability to search within a defined boundary and also across the web would help attorneys to increase their efficiency.

If so, you've come to the right place. This guide will briefly explain what sentiment analysis is, and introduce companies that provide sentiment annotation tools and services. Sentiment analysis is the process of identifying the emotion and/or opinion within unstructured text. The text can be in the form of customer reviews, social media posts, and more. This process allows you to accurately gauge customer opinion about your brand, products, or services.

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.