Abstract
In this chapter, we present an approach to learn a signed social network automatically from online news articles. The vertices in this network represent people and the edges are labeled with the polarity of the attitudes among them (positive, negative, and neutral). Our algorithm accepts as its input two social networks extracted via unsupervised algorithms: (1) a small signed network labeled with attitude polarities (see Tanev, Proceedings of the MMIES’2007 Workshop Held at RANLP’2007, Borovets, Bulgaria. pp. 33–40, 2007) and (2) a quotation network, without attitude polarities, consisting of pairs of people where one person makes a direct speech statement about another person (see Pouliquen et al., Proceedings of the RANLP Conference, Borovets, Bulgaria, pp. 487–492, 2007). The algorithm which we present here finds pairs of people who are connected in both networks. For each such pair (P1, P2) it takes the corresponding attitude polarity from the signed network and uses its polarity to label the quotations of P1 about P2. The obtained set of labeled quotations is used to train a Naïve Bayes classifier which then labels part of the remaining quotation network and adds it to the initial signed network. Since the social networks taken as the input are extracted in an unsupervised way, the whole approach including the acquisition of input networks is unsupervised.
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Notes
- 1.
The system is restricted to only one person per quotation. It is assumed that the first person mentioned in the quotation is the main person to whom the quotation refers.
- 2.
The original definition was actually formulated for directed graphs and made use of the notion of “semi-cycle”, that is a closed directed walk of at least three nodes on the graph which is traversed ignoring the direction of the edges.
- 3.
Namely, if (v i , v k ) and (v j , v k ) are both positive, we enforce “+” on (v i , v j ), while if (v i , v k ) and (v j , v k ) have conflicting signs, then we enforce “–” on (v i , v j ).
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Acknowledgments
We would like to thank the whole team working on the Europe Media Monitor for providing the valuable news data. Their research and programming effort laid the foundation which made this experimental work possible.
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Tanev, H., Pouliquen, B., Zavarella, V., Steinberger, R. (2010). Automatic Expansion of a Social Network Using Sentiment Analysis. In: Memon, N., Xu, J., Hicks, D., Chen, H. (eds) Data Mining for Social Network Data. Annals of Information Systems, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6287-4_2
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