How to merge three different methods for information filtering ?
This work addresses online reputation management for entities using Twitter, but it is incremental as it builds on existing methods and benchmarks.
The paper tackled the problem of monitoring Twitter for online reputation by addressing ambiguous entity names, polarity detection, and topic detection, and found that merging three competitive methods improved filtering performance, with specific gains in evaluation measures.
Twitter is now a gold marketing tool for entities concerned with online reputation. To automatically monitor online reputation of entities , systems have to deal with ambiguous entity names, polarity detection and topic detection. We propose three approaches to tackle the first issue: monitoring Twitter in order to find relevant tweets about a given entity. Evaluated within the framework of the RepLab-2013 Filtering task, each of them has been shown competitive with state-of-the-art approaches. Mainly we investigate on how much merging strategies may impact performances on a filtering task according to the evaluation measure.