Text Analytics for Android Project

Most advanced text analytics and text mining tasks include text classification, text clustering, building ontology, concept/entity extraction, summarization, deriving patterns within the structured data, production of granular taxonomies, sentiment and emotion analysis, document summarization, entity relation modelling, interpretation of the output. Already existing text analytics and text mining cannot develop text material alternatives (perform a multivariant design), perform multiple criteria analysis, automatically select the most effective variant according to different aspects (citation index of papers (Scopus, ScienceDirect, Google Scholar) and authors (Scopus, ScienceDirect, Google Scholar), Top 25 papers, impact factor of journals, supporting phrases, document name and contents, density of keywords), calculate utility degree and market value. However, the Text Analytics for Android Project can perform the aforementioned functions. To the best of the knowledge herein, these functions have not been previously implemented; thus this is the first attempt to do so. The Text Analytics for Android Project is briefly described in this article

She? The Role of Perceived Agent Gender in Social Media Customer Service

This paper investigated the role of perceived agent gender in customer behavior using a unique dataset from Southwest Airlines\u27 Twitter account. We inferred agent gender based on the first names provided by agents when responding to customers. We measured customer behavior using three outcomes: whether a customer decided to continue the service conversation upon receiving an agent’s initial response as well as the valence and arousal levels in their second tweet if the customer chose to continue the interaction. Our identification strategy relied on the Backdoor Criterion and hinged on the assumption that customer service requests are assigned to the next available agent, independent of agent gender. The findings revealed that customers were more likely to continue interactions with female agents than male agents and they were more negative in valence but less intense in arousal with the former group than with the latter

Text Analytics: the convergence of Big Data and Artificial Intelligence

The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers’ comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

In the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches

The situated common-sense knowledge in FunGramKB

It has been widely demonstrated that expectation-based schemata, along the lines of Lakoff's propositional Idealized Cognitive Models, play a crucial role in text comprehension. Discourse inferences are grounded on the shared generalized knowledge which is activated from the situational model underlying the text surface dimension. From a cognitive-plausible and linguistic-aware approach to knowledge representation, FunGramKB stands out for being a dynamic repository of lexical, constructional and conceptual knowledge which contributes to simulate human-level reasoning. The objective of this paper is to present a script model as a carrier of the situated common-sense knowledge required to help knowledge engineers construct more "intelligent" natural language processing systems.Periñán Pascual, JC. (2012). The situated common-sense knowledge in FunGramKB. Review of Cognitive Linguistics. 10(1):184-214. doi:10.1075/rcl.10.1.06perS18421410