Capturing IT effort that is overlooked or misinterpreted by Key Performance Indicators. KPIs such as call duration are not necessarily the best way to measure the effectiveness your IT support staff. For example, a long phone call may mean that your agent is handling a complex issue--not having trouble resolving it. You can use Sentiment Analysis to identify the agents that are consistently involved in calls with a positive sentiment, so you can reward them and use them to mentor less experienced team members. By pulling sentiment data into your IT department's KPI reports, you can find correlations that might otherwise be hidden.

Though Arabic NLP has many challenges, it has seen many successes and developments.

In our current day and age, reviews are part of almost every product/service provided on the internet[14], as seen in [8] it is the primary way for a company to get an understanding concerning the amount of success their product has and as examined in [7] for the customer to build trust in purchasing or using a service of which only a description or a picture exits. Therefore, a need for a deeper understanding and analysis of those reviews are needed[9] for any individual who wishes to derive various consequences regarding a product. Standard methods for such insight derivation include sentiment analysis, around which we will formulate a new approach for review rating classification. Reviews across the internet mainly consist of text-based and rating-based formats, where in many cases, a combination of both is considered a single review; the method developed in this paper focuses on the ability to associate a review to a rating cluster based on sentiment proportions. We will define two main groups: one group consisting of a majority of reviews higher than three stars (in a 5-star ranking system) and another group of all reviews, which correspond to the less than three stars.

We introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one unified framework. Our results show that once trained, the attacking model is capable of generating high-quality adversarial examples substantially faster (one order of magnitude less in time) than state-of-the-art attacking methods. These examples also preserve the original sentiment according to human evaluation. Additionally, our framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods. Code is available at: https://github.com/ibm-aur-nlp/adv-def-text-dist.

Various robustness evaluation methodologies from different perspectives have been proposed for different natural language processing (NLP) tasks. These methods have often focused on either universal or task-specific generalization capabilities. In this work, we propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint enables practitioners to automatically evaluate their models from all aspects or to customize their evaluations as desired with just a few lines of code. To guarantee user acceptability, all the text transformations are linguistically based, and we provide a human evaluation for each one. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness. To validate TextFlint's utility, we performed large-scale empirical evaluations (over 67,000 evaluations) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. Almost all models showed significant performance degradation, including a decline of more than 50% of BERT's prediction accuracy on tasks such as aspect-level sentiment classification, named entity recognition, and natural language inference. Therefore, we call for the robustness to be included in the model evaluation, so as to promote the healthy development of NLP technology.

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In this tutorial, you will learn the proper ways to extract your personal data from your Linkedin account and use Python to analyze and draw useful insights from it. If you don't have a Linkedin account, please run as fast as you can to Linkedin page to create one. Actually it's not a good habit to not have a Linkedin account in this modern world…lol Linkedin is one of the biggest social network out there, and the chances are you are proud Linkedin member (if not create one now-please). Linkedin gives you access you to your data and you can download and analyze this data to draw insights from it. Linkedin has a clear guide as to how to download your data.

Understanding the information contained in the increasing repository of data is of vital importance to behavior sciences [34], which aim to predict human decision making and enable wide applications, such as mental health evaluation [14], business recommendation [33], opinion mining [54], and entertainment assistance [78]. Analyzing media data on an affective (emotional) level belongs to affective computing, which is defined as "the computing that relates to, arises from, or influences emotions" [38]. The importance of emotions has been emphasized for decades since Minsky introduced the relationship between intelligence and emotion [31]. One famous claim is "The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without emotions." Based on the types of media data, the research on affective computing can be classified into different categories, such as text [13, 72], image [75], speech [45], music [64], facial expression [24], video [56, 79], physiological signals [2], and multi-modal data [52, 41, 80]. The adage "a picture is worth a thousand words" indicates that images can convey rich semantics. Therefore, images are used as an important channel to express emotions. Image emotion analysis (IEA) has recently been paid much attention. As compared to analyzing the images' cognitive aspect that is related with objective content [15], such as object classification and semantic segmentation, IEA focuses on understanding what emotions can be induced by the images in viewers.

Scanned receipts OCR and key information extraction (SROIE) represent the processeses of recognizing text from scanned receipts and extracting key texts from them and save the extracted tests to structured documents. SROIE plays critical roles for many document analysis applications and holds great commercial potentials, but very little research works and advances have been published in this area. In recognition of the technical challenges, importance and huge commercial potentials of SROIE, we organized the ICDAR 2019 competition on SROIE. In this competition, we set up three tasks, namely, Scanned Receipt Text Localisation (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3). A new dataset with 1000 whole scanned receipt images and annotations is created for the competition. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, submission statistics, performance of submitted methods and results analysis.

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that is concerned with the interactions made between computers and natural language. Essentially, by analyzing and representing natural language computationally, computers are capable of understanding natural language and responding in a way similar to a human. As a beginner learning the ropes of any new technology, getting your hands dirty is an important part of the learning process. Although I believe theoretical knowledge is very crucial, I don't believe it's effective in isolation as the theory doesn't always translate into real-world scenarios. Taking a practical approach is by far the greatest way to keep testing yourself whilst gaining experience of what it's like to work in a real-world environment.