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Affective social network—happiness inducing social media platform

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Abstract

We propose a human emotion regarding social network, namely, the Affective Social Network. Our suggestion firstly builds a user’s emotion profile in terms of the personality, mood and emotion by analysing the user’s activities in social network. This subsequently builds an Emotional Relationship Matrix (ERM) which represents the depth of the emotional relationship based on the emotion profile. From our proposal, the more elaborate services based on the current user’s emotional state can be provided to users. By considering emotional aspects in a social network, we can effectively answer which users or media contents will show the best results for inducing users’ emotional states. From the experiments, we verified that message containing emotional words and users’ relationship in a social network has significant correlations.

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Notes

  1. It means to forward a tweet (short message) to one’s followers on Twitter.

  2. http://en.wikipedia.org/wiki/Personality

  3. http://en.wikipedia.org/wiki/Emotion

  4. Because of the changes in Twitter’s new terms of services, no one can share data containing tweets any more.

  5. We filtered only 308 users who were generated at least one tweet a day in average.

  6. Stanford POS Tagger: http://nlp.stanford.edu/software/tagger.shtml

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Authors and Affiliations

  1. Intelligent E-Commerce Lab., Dept. of Computer Science & Information Engineering, INHA University, #253 YongHyun-dong Nam-Ku, Incheon, Korea

    Hyun-Jun Kim

  2. Graduate School of Education, INHA University, #253 YongHyun-dong Nam-Ku, Incheon, Korea

    Seung-Bo Park

  3. Dept. of Computer Science & Information Engineering, INHA University, #253 YongHyun-dong Nam-Ku, Incheon, Korea

    Geun-Sik Jo

Authors
  1. Hyun-Jun Kim
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  2. Seung-Bo Park
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  3. Geun-Sik Jo
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Corresponding author

Correspondence to Hyun-Jun Kim.

Appendix: Automatic Emotion Annotator

Appendix: Automatic Emotion Annotator

Automatic Emotion Annotation is consist of following 3 steps:

  1. 1)

    Dialogue Parsing: A dialogue received from a subtitle is parsed by the POS tagger,Footnote 6 and then, nouns, verbs, adjectives, and adverbs are extracted from a dialogue. Unemotional words or stop words, such as pronoun, be verb, and article, are erased.

  2. 2)

    Emotional State Calculation (ESC): An emotional state is measured by labeling automatically each emotional concept and calculating each emotional value for parsed words through the process, as shown on center of Fig. 3.

  3. 3)

    Annotation: An emotional state is annotated in a dialogue.

And two emotional vectors (\(\mathit{EV}_a\), \(\mathit{EV}_b\)) are added for only the same emotional concept as given by (5).

$$ \mathit{EV}_a+\mathit{EV}_b = <\mathit{ev}_{a_1}+\mathit{ev}_{b_1}, \mathit{ev}_{a_2}+\mathit{ev}_{b_2},...,\mathit{ev}_{a_n}+\mathit{ev}_{b_n}> $$
(5)

A Top Emotional Concept Word (TECW), such as happiness, liking and dislike has an emotional value of 8. The conceptual distance (dist) from the word to the TECW is calculated by (6). An emotional value (ev) of a word subtracts the conceptual distance from the emotional value of the TECW (Table 5).

$$ \begin{array}{rll} \mathit{dist}(\mathit{word}) &=& \mathit{dist}(\mathit{word}) + 2, \quad \mathit{if} \quad \mathit{TECW} = \mathit{hypernym} \\ \mathit{dist}(\mathit{word}) &=& \mathit{dist}(\mathit{word}) + 1, \quad \mathit{if} \quad \mathit{TECW }= \mathit{synonym} \\ \mathit{ev}(\mathit{word}) &=& \mathit{ev}(\mathit{TECW}) - \mathit{dist}(\mathit{word}) \end{array} $$
(6)
Table 5 Notations for the automatic emotion annotator
Full size table

Algorithm 1 described details of the emotional state calculation for a dialog.

figure d

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Kim, HJ., Park, SB. & Jo, GS. Affective social network—happiness inducing social media platform. Multimed Tools Appl 68, 355–374 (2014). https://doi.org/10.1007/s11042-012-1157-2

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