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Toward understanding online sentiment expression: an interdisciplinary approach with subgroup comparison and visualization

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Abstract

Understanding users’ sentiment expression in social media is important in many domains, such as marketing and online applications. Is one demographic group inherently different from another? Does a group express the same sentiment both in private and public? How can we compare the sentiments of different groups composed of multiple attributes? In this paper, we take an interdisciplinary approach toward mining the patterns of textual sentiments and metadata. First, we look into several existing hypotheses in social science on the interplay between user characteristics and sentiments, as well as the related evidence in the field of social network data analysis. Second, we present a dataset with unique features (Facebook users chats and posts in multiple languages) and a procedure to process the data. Third, we test our hypotheses on this dataset and interpret the results. Fourth, under the subgroup discovery paradigm, we present an approach with two algorithms that generalizes single-attribute testing. This approach provides more detailed insight into the relationships among attributes and reveals interesting attribute value combinations with distinct sentiments. It also offers novel hypotheses for examination in future studies. Fifth, because the number of mined subgroup comparisons can be large, we develop an exploratory visualization tool that summarizes the comparisons and highlights meta-patterns.

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Notes

  1. https://code.google.com/p/cld2/.

  2. Namely English, Dutch, French, German, Italian, Polish, Portuguese, Russian, Spanish, Swedish and Turkish.

  3. Due to the limited space of this paper, we summarize and selectively report the results of the post hoc pairwise tests in Sect. 4. A complete report can be found at http://beaugogh.github.io/visualizations/mcells/data/pairwise.

  4. because of the usage of Mann–Whitney U test.

  5. We use “relation.” to denote the relationship status “in a relationship.”

  6. The tasks that are unique in Yi et al. (2007) are marked with \(*\).

  7. The online article Krzywinski (2009) gives examples on the deficiencies of tables showing data.

  8. https://en.wikipedia.org/wiki/Chord_diagram.

  9. http://beaugogh.github.io/visualizations/mcells/.

  10. https://github.com/mbostock/d3/wiki/Force-Layout.

  11. In a force-directed graph layout, heavily connected nodes form clusters.

  12. The questions Q3 and Q4 also fall under this task description, because Q3 inquires about the relationship between two columns, and Q4 inquires about the relationship between a set of comparisons and their corresponding items.

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Acknowledgments

We thank Prof. Thelwall for his support with the SentiStrength tool. The research presented in this paper was supported by the Strategic Basic Research (SBO) Program of the Flemish Agency for Innovation via Science and Technology (IWT) through the project SPION (Grant No. 100048), and from the organization Fund for Scientific Research for Flanders (FWO) through the project Data Mining for Privacy in Social Networks (Grant No. G068611N).

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Authors and Affiliations

  1. Department of Computer Science, KU Leuven, Leuven, Belgium

    Bo Gao & Bettina Berendt

  2. Department of Mathematics and Computer Science, TU Eindhoven, Eindhoven, Netherlands

    Joaquin Vanschoren

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  1. Bo Gao
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  2. Bettina Berendt
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  3. Joaquin Vanschoren
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Correspondence to Bo Gao.

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Gao, B., Berendt, B. & Vanschoren, J. Toward understanding online sentiment expression: an interdisciplinary approach with subgroup comparison and visualization. Soc. Netw. Anal. Min. 6, 68 (2016). https://doi.org/10.1007/s13278-016-0385-2

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