Claims processing and underwriting are two areas of insurance that could benefit from AI-based information extraction/document search software. That said, neither are developed use-cases for AI in insurance right now. This will likely change over time as AI becomes more accessible to businesses, perhaps with autoML or a shift in the culture of innovation at older enterprises. At that point, AI use-cases in insurance will likely move from the cost-saving benefits of document search applications to more complex machine learning systems that involve document search, machine vision, and prescriptive analytics, allowing for capabilities that drive growth, such as tailor-made insurance policies.

Sentiment analysis has a range of corpora available across multiple languages. For emotion analysis, the situation is more limited, which hinders potential research on cross-lingual modeling and the development of predictive models for other languages. In this paper, we fill this gap for German by constructing deISEAR, a corpus designed in analogy to the well-established English ISEAR emotion dataset. Motivated by Scherer's appraisal theory, we implement a crowdsourcing experiment which consists of two steps. In step 1, participants create descriptions of emotional events for a given emotion. In step 2, five annotators assess the emotion expressed by the texts. We show that transferring an emotion classification model from the original English ISEAR to the German crowdsourced deISEAR via machine translation does not, on average, cause a performance drop.

Hiring robots for the workplaces is a challenging task as robots have to cater to customer demands, follow organizational protocols and behave with social etiquette. In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment. The objective of this study is to analyze the effects of humanoid robots on customers at work environment, and see if it can handle social scenarios. We propose to evaluate these objectives through two modes, namely, survey questionnaire and customer feedback. We also propose a novel approach to analyze customer feedback data (text) using sentic computing methods. Specifically, we employ aspect extraction and sentiment analysis to analyze the data. From our framework, we detect sentiment associated to the aspects that mainly concerned the customers during their interaction. This allows us to understand customers expectations and current limitations of robots as employees.

Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Na\"ive Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focuses on how to accumulate knowledge during learning and leverage them for further tasks. Meanwhile, the demand for labelled data for training also is significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labelled data and computational cost to achieve the performance as well as or even better than the supervised learning.

The text analytics is a process of converting unstructured text to meaningful data to understand the customer demand, product description, and market trend. The increasing demand for insights from unstructured data is one of the major drivers for growth of the text analytics market. Due to increasing stiff competition in the market, it become important for the companies to analyze the demand of their customer, and marketing strategy, which raised the demand for unstructured data. The social media industry plays a major role for providing unstructured data to the companies. Moreover, the increasing demand for predictive analytics, growing requirement for social media analytics and changing business trends are some of the key drivers which fuels the market of text analytics globally.

In this episode, we talk to Nick Saretzky, Senior Director of Project Management at Tinder, about how dating apps started out with data, most recently with Tinder data. We discuss the benefits of driving change through data insights, and what user data Tinder has at its disposal. We also talk about the impact of dating apps on how people interact, and on the changing approach to modern relationships. Listen to this episode on Spotify, iTunes, and Stitcher. You can also catch up on the previous episode of the Hub & Spoken podcast, in which Jason spoke to Kerry Dawes, Director of Digital Customer Experience at The Rank Group, on the impact of data on the digital customer experience in gambling.

Sentiment analysis is a highly subjective and challenging task. Its complexity further increases when applied to the Arabic language, mainly because of the large variety of dialects that are unstandardized and widely used in the Web, especially in social media. While many datasets have been released to train sentiment classifiers in Arabic, most of these datasets contain shallow annotation, only marking the sentiment of the text unit, as a word, a sentence or a document. In this paper, we present the Arabic Sentiment Twitter Dataset for the Levantine dialect (ArSenTD-LEV). Based on findings from analyzing tweets from the Levant region, we created a dataset of 4,000 tweets with the following annotations: the overall sentiment of the tweet, the target to which the sentiment was expressed, how the sentiment was expressed, and the topic of the tweet. Results confirm the importance of these annotations at improving the performance of a baseline sentiment classifier. They also confirm the gap of training in a certain domain, and testing in another domain.

Intelligence Assistants in classroom is the natural next step from interactive whiteboards and multiple software solutions. Offering classroom collaboration, next-level interactivity, curriculum content, grading, automatic attendance, sentiment analysis, and device management, eins.ai is truly comprehensive.

Punchh, the leader in digital marketing solutions for physical retailers, today announced the launch of Punchh Deep Sentiment Analysis. The new product allows brands to extract valuable insights from customer reviews using Punchh's natural language comprehension engine built with industry-leading deep learning and artificial intelligence. Its natural language processing model achieves human-level performance, defined as more than 93 percent accurate, and features multi-language support. "In today's hyper-competitive climate, brands need to do everything they can to foster and nurture direct customer relationships, and paying attention to customer reviews is an essential part of that," said Shyam Rao, CEO of Punchh. "Manually reading every review is prohibitively time-consuming for most retailers, which leads to slower response times and poor customer experiences. Our solution uses AI and machine learning to help brands analyze reviews at scale and immediately identify critical information so they can focus on high-level insights and make quick decisions to strengthen customer relationships and increase loyalty."

The increasing use of social networks has made opinion mining an important field in the area of Natural Language Processing. The analysis of texts from the reader perspective tends to generate multi-label data since one can interpret the text using different contexts. In this paper, a new method for multi-label classification is proposed to identify reactions or emotions in texts. The new method uses data correlation to improve the class ensemble process used to create the classifiers. In addition to the new method, a new corpus of news written in Brazilian Portuguese labeled with user reactions is presented. Experiments performed with the new corpus and with two existing corpora have demonstrated that the proposed method generates statistically superior or equivalent results, requiring fewer classifiers or classes than traditional problem transformation methods.