While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data. Such issues come to be manifest in performance problems when faced with out-of-distribution data in the field. One recent solution has been to use counterfactually augmented datasets in order to reduce any reliance on spurious patterns that may exist in the original data. Producing high-quality augmented data can be costly and time-consuming as it usually needs to involve human feedback and crowdsourcing efforts. In this work, we propose an alternative by describing and evaluating an approach to automatically generating counterfactual data for data augmentation and explanation. A comprehensive evaluation on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in model performance when compared to models training on the original data and even when compared to models trained with the benefit of human-generated augmented data.

Matt Canada has a background in graphic design, customer service and management. Sentiment analysis will indicate ways to build a better marketing campaign for your brand. In this article, we will look into four ways to leverage sentiment analysis tools to enhance your brand presence and excite customers. Sentiment analysis is a method to analyze emotions and reactions expressed through online communication - verbal or written. Also termed as'opinion mining' or'emotion AI', sentiment analysis executes data mining, fetches results, and skims out public opinion from within content pieces to help brands get informed of their customer experience.

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018.

CI leaders will shift 10% of their budgets to emotion analytics. Emotions are a more important driver of consumer decisions than rational thought and thus are the largest factor in brand energy, customer experience, and marketing effectiveness. But for the past decade, CI professionals have leaned into the precision of big data analytics instead of the traditionally unquantifiable territory of emotion. New techniques change this dynamic: AI-based text analytics tools such as Clarabridge and IBM Watson improve the precision of cruder sentiment analysis tools, while firms such as Nielsen and Realeyes bring biometric and facial analysis methodologies from the lab to the business world. As data analytics becomes commoditized, firms will shift 10% of the insights budget to emotion analytics to pilot new techniques in search of competitive advantage in the "why" behind consumer behavior, not just the "what" that data analytics addresses. Companies will reorganize to ensure CX and CI collaboration.

Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non-trivial for deep neural networks as continually training of incrementally available information inevitably results in catastrophic forgetting or interference. In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization. By performing network pruning with uncertainty regularization in an iterative manner, IPRLS can adapta single BERT model to work with continuously arriving data from multiple domains while avoiding catastrophic forgetting and interference. Specifically, we leverage an iterative pruning method to remove redundant parameters in large deep networks so that the freed-up space can then be employed to learn new tasks, tackling the catastrophic forgetting problem. Instead of keeping the old-tasks fixed when learning new tasks, we also use an uncertainty regularization based on the Bayesian online learning framework to constrain the update of old tasks weights in BERT, which enables positive backward transfer, i.e. learning new tasks improves performance on past tasks while protecting old knowledge from being lost. In addition, we propose a task-specific low-dimensional residual function in parallel to each layer of BERT, which makes IPRLS less prone to losing the knowledge saved in the base BERT network when learning a new task. Extensive experiments on 16 popular review corpora demonstrate that the proposed IPRLS method sig-nificantly outperforms the strong baselines for lifelong sentiment classification. For reproducibility, we submit the code and data at:https://github.com/siat-nlp/IPRLS.

Visual information extraction (VIE) has attracted increasing attention in recent years. The existing methods usually first organized optical character recognition (OCR) results into plain texts and then utilized token-level entity annotations as supervision to train a sequence tagging model. However, it expends great annotation costs and may be exposed to label confusion, and the OCR errors will also significantly affect the final performance. In this paper, we propose a unified weakly-supervised learning framework called TCPN (Tag, Copy or Predict Network), which introduces 1) an efficient encoder to simultaneously model the semantic and layout information in 2D OCR results; 2) a weakly-supervised training strategy that utilizes only key information sequences as supervision; and 3) a flexible and switchable decoder which contains two inference modes: one (Copy or Predict Mode) is to output key information sequences of different categories by copying a token from the input or predicting one in each time step, and the other (Tag Mode) is to directly tag the input sequence in a single forward pass. Our method shows new state-of-the-art performance on several public benchmarks, which fully proves its effectiveness.

All around us, developers now leverage machine learning capabilities in their applications to amplify human effort. Tensorflow enables developers to make mind blowing capabilities like tracking your pose with a web cam, object detection using images, sentiment analysis in text, and computer generated art/music. In this session, we'll explore the opportunities for web developers to create "plug and play" machine learning experiences using TensorFlow.JS and related JavaScript libraries. We'll explore ways that you can make an impact using pre-trained models from tfhub.dev. To learn more, check out https://www.tensorflow.org/js/ .

The annual production of data follows an exponential curve, the assimilation by a person or even by a group of persons of this data is no longer possible. To get the most out of it, it is necessary to be helped by computers. But as this data is mostly unstructured, classical algorithms are unable to do this job. Only Artificial Intelligence and in particular NLP with Sentiment Analysis can do it. We created ElligencIA with the aim of giving meaning to this ocean of data and taking advantage of this collective intelligence. ElligencIA, operational since January 1st 2021, is an AI consulting and solutions company for the BFSI.

Learn how to extract insights from natural language text, such as category, concepts, emotion, entities, keywords, sentiment, top positive sentences, and word clouds by using IBM Watson Natural Language Understanding and Watson Tone Analyzer. Watson Natural Language Understanding includes a set of text analytics features that can be used to extract meanings from unstructured data such as a text file. Watson Tone Analyzer understands emotions and communication styles in a text. By combining the capabilities of both services, you can extract meaningful insights in the form of a natural language understanding analysis report from a natural language transcript. The transcript used in this code pattern is generated from a video recording of the IBM Q1 2019 earnings meeting.

Why analytics are vital for prosperous businesses? Descriptive analytics: review data with stats to tell you what happened in the past. It helps a business understand better how it is performing by providing context to help stakeholders interpret information. This can be in the form of data visualizations like graphs, charts, reports, and dashboards. Predictive analytics takes past data and feeds it into a machine learning model that considers key trends and patterns.