# Making Sense of Unstructured Text Data

## TL;DR

This paper introduces an end-to-end system that converts unstructured text data from social media into structured graphs by extracting key indicative words, enhancing network analysis and cross-domain entity resolution.

## Contribution

The work presents a novel method for transforming unstructured text into structured graphs using automatic keyword extraction, improving upon hashtag-based approaches.

## Key findings

- Automatic keywords outperform user-annotated hashtags in entity resolution
- System effectively constructs context+content networks from free-text data
- Improved accuracy in social media network analysis

## Abstract

Many network analysis tasks in social sciences rely on pre-existing data sources that were created with explicit relations or interactions between entities under consideration. Examples include email logs, friends and followers networks on social media, communication networks, etc. In these data, it is relatively easy to identify who is connected to whom and how they are connected. However, most of the data that we encounter on a daily basis are unstructured free-text data, e.g., forums, online marketplaces, etc. It is considerably more difficult to extract network data from unstructured text. In this work, we present an end-to-end system for analyzing unstructured text data and transforming the data into structured graphs that are directly applicable to a downstream application. Specifically, we look at social media data and attempt to predict the most indicative words from users' posts. The resulting keywords can be used to construct a context+content network for downstream processing such as graph-based analysis and learning. With that goal in mind, we apply our methods to the application of cross-domain entity resolution. The performance of the resulting system with automatic keywords shows improvement over the system with user-annotated hashtags.