Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England

The inclusivity of world Englishes

(Received 31 October 2009.)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79056/1/j.1467-971X.2009.01622.x.pd

Tracing the evolution of service robotics : Insights from a topic modeling approach

Acord transformatiu CRUE-CSICAltres ajuts: Helmholtz Association (HIRG-0069)Altres ajuts: Russian Science Foundation (RSF grant number 19-18-00262)Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory

Tracing the evolution of service robotics : Insights from a topic modeling approach

Altres ajuts: Acord transformatiu CRUE-CSICAltres ajuts: Helmholtz Association (HIRG-0069)Altres ajuts: Russian Science Foundation (RSF grant number 19-18-00262)Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory

Understanding the Power of Framing: The Role of Policy Context and Stakeholder Characteristics in the EU Feedback Mechanism

Postponed access: the file will be accessible after 2023-07-01MasteroppgaveSAMPOL350MASV-SAP

Topic modelling of Finnish Internet discussion forums as a tool for trend identification and marketing applications

The increasing availability of public discussion text data on the Internet motivates to study methods to identify current themes and trends. Being able to extract and summarize relevant information from public data in real time gives rise to competitive advantage and applications in the marketing actions of a company. This thesis presents a method of topic modelling and trend identification to extract information from Finnish Internet discussion forums. The development of text analytics, and especially topic modelling techniques, is reviewed and suitable methods are identified from the literature. The Latent Dirichlet Allocation topic model and the Dynamic Topic Model are applied in finding underlying topics from the Internet discussion forum data. The discussion data collection with web scarping and text data preprocessing methods are presented. Trends are identified with a method derived from outlier detection. Real world events, such as the news about Finnish army vegetarian meal day and the Helsinki summit of presidents Trump and Putin, were identified in an unsupervised manner. Applications for marketing are considered, e.g. automatic search engine advert keyword generation and website content recommendation. Future prospects for further improving the developed topical trend identification method are proposed. This includes the use of more complex topic models, extensive framework for tuning trend identification parameters and studying the use of more domain specific text data sources such as blogs, social media feeds or customer feedback

Advancing mental health care with AI-enabled precision psychiatry tools: A patent review

The review provides an overview of patents on AI-enabled precision psychiatry tools published between 2015 and mid-October 2022. Multiple analytic approaches, such as graphic network analysis and topic modeling, are used to analyze the scope, content, and trends of the retained patents. The included tools aim to provide accurate diagnoses according to established psychometric criteria, predict the response to specific treatment approaches, suggest optimal treatments, and make prognoses regarding disorder courses without intervention. About one-third of the tools recommend treatment options or include treatment administration related to digital therapeutics, pharmacotherapy, and electrotherapy. Data sources used to make predictions include behavioral data collected through mobile devices, neuroimaging, and electronic health records. The complexity of technology combinations used in the included devices has increased until 2021. The topics extracted from the patent data illuminate current trends and potential future developments in AI-enabled precision psychiatry. The most impactful patents and associated available products reveal relevant commercialization possibilities and likely future developments. Overall, the review highlights the potential of adopting AI-enabled precision psychiatry tools in practice

The determinants of legislative speechmaking on salient issues: the analysis of parliamentary debates on Theresa May’s Brexit withdrawal agreement using structural topic models

https://www.ester.ee/record=b5298515*es

Trademarks and Textual Data: A Broader Perspective on Innovation = Marques et données textuelles : Une perspective élargie sur l’innovation

Patente messen häufig technische Innovationen, während Handelsmarken Low-Tech und Dienstleistungen abdecken. In dieser Arbeit werden Textdaten von Marken untersucht, um verschiedene Rechte des geistigen Eigentums zu kombinieren. Textdaten ermöglichen zum Beispiel die Analyse großer Datenmengen, die Kombination verschiedener Quellen und datengestützte Erkenntnisse. Die Kombination von Handelsmarken und Patenten in den Bereichen Robotik (Hightech) und Schuhe (Lowtech) bietet eine breitere Abdeckung und Details zu Innovationen, die je nach Sektor variieren. Im Musikinstrumentensektor verdeutlichen Textdaten zu Marken, Patenten und Designs den laufenden technologischen Wandel. Patente beziehen sich auf Daten und Digitalisierungsthemen und werden von High-Tech-Firmen genutzt, während Handelsmarken die Signalverarbeitung und Videospiele von Spielfirmen abdecken. Designs fungieren als verbindendes Element. Eine Differenzierung zwischen Unternehmen und Tätigkeitsbereichen ist möglich. Zusammenfassend zeigt die These, dass die Integration von textuellen Markendaten die Innovationsabdeckung erweitert