Aim: M2 aims to give students insight into network and unstructured data types, as well as state-of-the-art approaches to map and analyse these data. Insights and techniques gained in this module will allow students to approach real-world problems in marketing (Who are the main influencers among our customers?), management (Can we identify new discourses in the communication within our organisation?), business economics (Can language patterns be used to understand R&D intensity across companies?), political science (How is a political candidate perceived by a certain demographic, based on their social network statements?), and sociology (How is a person’s behaviour and characteristics affected by their social network?).
With accelerating digitalisation of the modern world, we capture and store a growing amount of relational and unstructured (e.g. text) data. The former type of data encodes social, biological, physical and other complex systems as a collection of actual or potential relations between some entities. These can be users in an online social network, companies in a cluster, or research articles in a database linked via some association metric. Exploring such networks allows unveiling latent and general structural patterns, to understand how the interaction between elements reflects on their attributes, or how information flows through these systems. Indeed, envisioning and analysing complex systems such as national economies, natural ecosystems, or social interactions as networks have brought fresh wind to a broad range of academic disciplines and professional sectors alike. Working with relational data is not difficult, but it certainly requires some rethinking.
The other type of data, unstructured data, come in many varieties. The one that is arguably most attractive for social science analytics is text. Language encodes a vast range of meanings, entities, and relations. Natural language processing (NLP) has considerably advanced in the past years, making unstructured text suitable for machine learning.
The link between networks and unstructured data is given by the fact that unstructured data usually encode something that is closer to a depiction of reality than traditional structured data. Thus, it will typically contain information on some objects with their attributes as well as relational features linking the objects. Understanding the relational dimension is therefore essential to working with unstructured data.
Upon completion, students will have built a solid knowledge foundation within network theory and analysis, computational linguistics and broader (unstructured) data processing. The module is application-focused, and thus students will gain a variety of skills to utilise relational and unstructured text data for analysis purposes.
Lectures will be complemented by online resources and e-learning tools such as podcasting, online tutorials, and mini-assignments, as integral parts of the teaching methodology to enhance student engagement outside the classroom. Physical face-to-face time will be centred around the tacit and interactive components of the problem-solving processes.
|Prøvens navn||Module 2: Network Analysis and Natural Language Processing|
Skriftlig og mundtligPortfolio exam:
60% obtained through various graded (and supervised peer-graded) problem sheets and mini-assignments throughout the module.
40% final internal evaluation seminar with oral presentation, peer-evaluation (opponent group), internal critique and discussion departing from the final assignment and presentation.
|Vurderingskriterier||Vurderingskriterierne er angivet i Universitetets eksamensordning|
|Engelsk titel||M2: Network Analysis and Natural Language Processing|
|Studienævn||Studienævn for Økonomi|
|Institut||Institut for Økonomi og Ledelse|
|Fakultet||Det Samfundsvidenskabelige Fakultet|