"Sentiment" is a rather intriguing concept. It can mean attitude, feeling, bias, view, thought, and even something as deeply felt as emotion. Sentiment analysis that utilizes AI and machine learning has become a powerful tool for companies to understand how their customers and/or potential customers feel about their company. It can also be used for competitive analysis to take the temperature of a rival's products or services. "When you can't convince them with intellect, persuade them with sentiment," is author Amit Kalantri's recommendation.

Predictive machine learning models often lack interpretability, resulting in low trust from model end users despite having high predictive performance. While many model interpretation approaches return top important features to help interpret model predictions, these top features may not be well-organized or intuitive to end users, which limits model adoption rates. In this paper, we propose Intellige, a user-facing model explainer that creates user-digestible interpretations and insights reflecting the rationale behind model predictions. Intellige builds an end-to-end pipeline from machine learning platforms to end user platforms, and provides users with an interface for implementing model interpretation approaches and for customizing narrative insights. Intellige is a platform consisting of four components: Model Importer, Model Interpreter, Narrative Generator, and Narrative Exporter. We describe these components, and then demonstrate the effectiveness of Intellige through use cases at LinkedIn. Quantitative performance analyses indicate that Intellige's narrative insights lead to lifts in adoption rates of predictive model recommendations, as well as to increases in downstream key metrics such as revenue when compared to previous approaches, while qualitative analyses indicate positive feedback from end users.

Social media services like Facebook and Twitter will need to have databases of Russian users kept in Russia by July or face fines. The news was first reported by Interfax news agency, citing communications regulator Roskomnadzor as saying on Wednesday. Russia is considering legislation that would force foreign technology companies to open offices in Russia or face penalties such as advertising bans, as part of Moscow's wider efforts to exert greater control over Big Tech. Google and Facebook were fined on Tuesday for failing to delete content Moscow deems illegal, while Twitter has been the victim of a punitive slowdown since March. Facebook, Twitter and others must localise their databases of Russian users by July 1 or face a fine of up to 18 million roubles ($245,100) for non-compliance, the deputy head of Roskomnadzor Milos Wagner was cited as saying on Wednesday.

Its uniqueness lies in its'self-supervised', pre-training objective architecture. Unlike other models that infer on the meaning of a sentence by extracting small parts of it, Pegasus completely'masks' the sentence and tries to find it by reading the text before and after it. Pegasus is really good at data summarization, but it is also great at paraphrasing sentences. The model is extremely easy to use, doesn't require many dependencies and with just a few lines of code we'll have our augmented dataset ready for training. To be able to leverage our small dataset efficiently, we will be performing text Paraphrasing along with Synonym Replacement to come up with a dataset large and unique enough to train our Sentiment Analysis model with.

Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical for success, but it is incredibly labor intensive. That's where sentiment analysis comes in. The term refers to the automated process of identifying the emotion -- either positive, negative or neutral -- associated with text. While artificial intelligence refers to logical data analysis and response, sentiment analysis is akin to correctly identifying emotional communication.

Typically, information extraction (IE) requires a pipeline approach: first, a sequence labeling model is trained on manually annotated documents to extract relevant spans; then, when a new document arrives, a model predicts spans which are then post-processed and standardized to convert the information into a database entry. We replace this labor-intensive workflow with a transformer language model trained on existing database records to directly generate structured JSON. Our solution removes the workload associated with producing token-level annotations and takes advantage of a data source which is generally quite plentiful (e.g. database records). As long documents are common in information extraction tasks, we use gradient checkpointing and chunked encoding to apply our method to sequences of up to 32,000 tokens on a single GPU. Our Doc2Dict approach is competitive with more complex, hand-engineered pipelines and offers a simple but effective baseline for document-level information extraction. We release our Doc2Dict model and code to reproduce our experiments and facilitate future work.

At the beginning of the year, WhatsApp took the seemingly mundane step of updating its terms of use and privacy policy, mostly focused on the app's business offerings. The changes sparked a major backlash, though, because they inadvertently highlighted WhatsApp's years-old policy of sharing certain user data, like phone numbers, with parent company Facebook. Rather than change the policy that sparked the controversy, WhatsApp instead moved the deadline for users to accept it from the original date of February 8 to Saturday. If you haven't accepted the new policy by now, you'll start to see more pop-ups in WhatsApp outlining the changes with a big green Accept button at the bottom. If you tap it, WhatsApp will continue to share certain account data of yours with Facebook.

Artificial Intelligence and Machine Learning is now considered to be one of the biggest innovations . AI and ML used to be a fanciful concept from science fiction, but now it's becoming a daily reality. The growth of less expensive and more powerful processing, The nearly limitless quantity of available data and affordable data storage has propelled the growth of Machine Learning. Now, before we get into the applications, Let's start with the basic intro to Machine Learning. Machine learning is a branch of AI focused on building applications that improve automatically through experience and by the use of data.

Although sentiment analysis is an effective process that helps in a proper understanding of a text, a major roadblock to this was the presence of sarcasm in the text. Sarcasm is a hard nut to crack even in normal human communication, thus it can only be imagined what a predicament it might pose to a computer program to do the same. Since it poses a hurdle for the accuracy of sentiment analysis, experts began working on a suitable solution that could address this problem. One of the major challenges that come with the identification of sarcasm in the text is the lack of vocal tones and facial expressions. Thus, identifying sarcasm in the text becomes a task that is performed with a blindfold, making it quite hard.

The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems. But there are still only a few well-defined problems that serve as benchmarks for solutions in this area. To bridge this gap, we introduce two new datasets (Kleister NDA and Kleister Charity). They involve a mix of scanned and born-digital long formal English-language documents. In these datasets, an NLP system is expected to find or infer various types of entities by employing both textual and structural layout features. The Kleister Charity dataset consists of 2,788 annual financial reports of charity organizations, with 61,643 unique pages and 21,612 entities to extract. The Kleister NDA dataset has 540 Non-disclosure Agreements, with 3,229 unique pages and 2,160 entities to extract. We provide several state-of-the-art baseline systems from the KIE domain (Flair, BERT, RoBERTa, LayoutLM, LAMBERT), which show that our datasets pose a strong challenge to existing models. The best model achieved an 81.77% and an 83.57% F1-score on respectively the Kleister NDA and the Kleister Charity datasets. We share the datasets to encourage progress on more in-depth and complex information extraction tasks.