Tweet Enrichment for Effective Dimensions Classification in Online Reputation Management
Keywords:Online Reputation Management, Text Classification, Query expansion, Collection Enrichment
Online Reputation Management (ORM) is concerned with the monitoring of public opinions on social media for entities such as commercial organisations. In particular, we investigate the task of reputation dimension classification, which aims to classify tweets that mention a business entity into different dimensions (e.g. "financial performance'' or "products and services''). However, producing a general reputation dimension classification system that can be used across businesses of different types is challenging, due to the brief nature of tweets and the lack of terms in tweets that relate to specific reputation dimensions. To tackle these issues, we propose a robust and effective tweet enrichment approach that expands tweets with additional discriminative terms from a contemporary Web corpus. Using the RepLab 2014 test collection, we show that our tweet enrichment approach outperforms effective baselines including the top performing submission to RepLab 2014. Moreover, we show that the achieved accuracy scores are very close to the upper bound that our approach could achieve on this collection.