Real-time monitoring of sentiment in business related Wikipedia
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|Conference paper (help)|
|Real-time monitoring of sentiment in business related Wikipedia|
|Authors:||Finn Årup Nielsen, Michael Etter, Lars Kai Hansen|
|Citation:||missing booktitle : 2013|
|Web:||DuckDuckGo Bing Google Yahoo! — Google PDF|
|Article:||Google Scholar PubMed|
|Restricted:||DTU Digital Library|
The system monitors the IRC stream from Wikimedia and detects changes on English Wikipedia article related to specific companies. With a word list-based approach using the AFINN sentiment-annotated word list each change and revision is scored for sentiment.
The paper is submitted to a conference.
 Wikimedia Research Newsletter summary
 Too good to be true? Detecting COI, Attacks and Neutrality using Sentiment Analysis
Finn Årup Nielsen, Michael Etter and Lars Kai Hansen presented a technical report on an online service which they created to conduct real-time monitoring of Wikipedia articles of companies. It performs sentiment analysis of edits, filtered by companies and editors. Sentiment analysis is a new applied linguistics technology which is being used in a number of tasks ranging from author profiling to detecting fake reviews on online retailers. The form of visualization provided by this tool can easily detect deviation from linguistic neutrality. However, as the authors point out, this analysis only gives a robust picture when used statistically and is more prone to mistakes when operating within a limited scope.
The service monitors recent changes using an IRC stream and detects company-related articles from a small hand-built list. It then retrieves the current version using the MediaWiki API and performs sentiment analysis using the AFINN sentiment-annotated word list. The project was developed by integrating a number of open source components such as NLTK and CouchDB. Unfortunately, the source code has not been made available and the service can only run queries on the shortlisted companies which will limit the impact of this report on future Wikipedia research. However, it seems to have potential as a tool for detecting COI edits that tend to tip neutrality by adding excess praise or attacks which tip the content in the other direction. We hope the researchers will open-source this tool like their prior work on the AFINN data-set, or at least provide some UI to query articles not included in the original research.
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