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.

We use an enhanced methodology combining specific forms of AI techniques, opinion mining and artificial mathematical intelligence (AMI), with public data on the spread of the coronavirus SARS-CoV-2 and the incidence of COVID-19 disease in Colombia during the first three months since the first reported positive case. The results obtained, together with conceptual tools coming from the global taxonomy of fundamental cognitive mechanisms emerging in AMI and with suitable contextual information from Colombian public health and mainstream social media, allowed us to stating specific preventive guidelines for a better restructuring of initial safe and stable life conditions in Colombia, and in an extended manner in similar Latin American Countries. More specifically, we describe three major guidelines: 1) regular creative visualization and effective planning, 2) the continuous use of constructive linguistic frameworks, and 3) frequent and moderate use of kinesthetic routines. They should be understood as effective tools from a cognitive and behavioural perspective, rather than from a biological one. Even more, the first two guidelines should be acknowledged in integral cooperation with the third one regarding the global effect of COVID-19 in human beings as a whole, this includes the mind and body.

Customers of machine learning systems demand accountability from the companies employing these algorithms for various prediction tasks. Accountability requires understanding of system limit and condition of erroneous predictions, as customers are often interested in understanding the incorrect predictions, and model developers are absorbed in finding methods that can be used to get incremental improvements to an existing system. Therefore, we propose an accountable error characterization method, AEC, to understand when and where errors occur within the existing black-box models. AEC, as constructed with human-understandable linguistic features, allows the model developers to automatically identify the main sources of errors for a given classification system. It can also be used to sample for the set of most informative input points for a next round of training. We perform error detection for a sentiment analysis task using AEC as a case study. Our results on the sample sentiment task show that AEC is able to characterize erroneous predictions into human understandable categories and also achieves promising results on selecting erroneous samples when compared with the uncertainty-based sampling.

Computer science researchers at the University of Central Florida have developed a sarcasm detector. 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.

JADS organizes this educational program in collaboration with EAISI part of TU/e. The program combines a practical approach, working through example AI projects thereby showing how to successfully execute an AI project, with building a solid understanding of the fundamental principles underlying #machinelearning. This newly developed educational program is aimed at management and senior professionals who recognize the opportunities of Data Science and AI.

Multi-party dialogues are common in enterprise social media on technical as well as non-technical topics. The outcome of a conversation may be positive or negative. It is important to analyze why a dialogue ends with a particular sentiment from the point of view of conflict analysis as well as future collaboration design. We propose an explainable time series mining algorithm for such analysis. A dialogue is represented as an attributed time series of occurrences of keywords, EMPATH categories, and inferred sentiments at various points in its progress. A special decision tree, with decision metrics that take into account temporal relationships between dialogue events, is used for predicting the cause of the outcome sentiment. Interpretable rules mined from the classifier are used to explain the prediction. Experimental results are presented for the enterprise social media posts in a large company.

Vader stands for Valence Aware Dictionary and sEntiment Reasoner. It is a lexicon and rule based tool for sentiment analysis. It is specifically attuned to sentiments expressed in social media. It is used for analyzing the sentiment of text which contains both positive and negative polarity. The main function of VADER is to quantify how much of positive or negative emotion is present in the text. It can also measure the intensity of emotion.

Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects. In this paper, we take a causal view to addressing this issue. We propose a simple yet effective method, namely, Sentiment Adjustment Figure 1: The causal graph of ABSA. We build our (SENTA), by applying a backdoor adjustment causal model over three main variables: target feature to disentangle those confounding factors. X, predictions Y and confounding factor C. Experimental results on the Aspect Robustness Our goal is to alleviate confounding factors, which is Test Set (ARTS) dataset demonstrate caused by X C, Y C. that our approach improves the performance while maintaining accuracy in the original test set