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Topic and sentiment aware microblog summarization for twitter

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Abstract

Recent advances in microblog content summarization has primarily viewed this task in the context of traditional multi-document summarization techniques where a microblog post or their collection form one document. While these techniques already facilitate information aggregation, categorization and visualization of microblog posts, they fall short in two aspects: i) when summarizing a certain topic from microblog content, not all existing techniques take topic polarity into account. This is an important consideration in that the summarization of a topic should cover all aspects of the topic and hence taking polarity into account (sentiment) can lead to the inclusion of the less popular polarity in the summarization process. ii) Some summarization techniques produce summaries at the topic level. However, it is possible that a given topic can have more than one important aspect that need to have representation in the summarization process. Our work in this paper addresses these two challenges by considering both topic sentiments and topic aspects in tandem. We compare our work with the state of the art Twitter summarization techniques and show that our method is able to outperform existing methods on standard metrics such as ROUGE-1.

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Notes

  1. http://jung.sourceforge.net/

  2. http://vlado.fmf.uni-lj.si/pub/networks/pajek/

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Authors and Affiliations

  1. Laboratory for Systems, Software and Semantics (LS3), Ryerson University, Toronto, Canada

    Syed Muhammad Ali, Zeinab Noorian & Ebrahim Bagheri

  2. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Chen Ding

  3. College of Technological Innovation, Zayed University, Dubai, United Arab Emirates

    Feras Al-Obeidat

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  1. Syed Muhammad Ali
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  2. Zeinab Noorian
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  3. Ebrahim Bagheri
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  4. Chen Ding
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  5. Feras Al-Obeidat
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Corresponding author

Correspondence to Ebrahim Bagheri.

Appendix

Appendix

Table 8 shows the topics and their associated number of tweets that are used in our experiments. Note that the internal cohesion of all the topics is 1. Table 9 shows samples of summaries generated by different clustering algorithms along with the manual summary generated by our volunteers based on the topics from (Table 10). The set of extracted aspects are reported in Table 11, which are then assigned to respective aspects as reported in Table 12. Finally, we pick one representative tweet for each sentiment-aspect pair in order to generate a summary shown in Table 13.

Table 8 Topics and their associated tweets in our experiments
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Table 9 Sample generated summary for different clustering algorithms
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Table 10 Tweet corpus for the snowfall topic with associated sentiments
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Table 11 Aspects extracted from the word graph based on the tweets and their sentiments
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Table 12 Selected tweets for two different aspects
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Table 13 The set of summary tweets for the two aspects
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Ali, S.M., Noorian, Z., Bagheri, E. et al. Topic and sentiment aware microblog summarization for twitter. J Intell Inf Syst 54, 129–156 (2020). https://doi.org/10.1007/s10844-018-0521-8

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