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Automatic sentiment-oriented summarization of multi-documents using soft computing

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Abstract

This paper presents an automatic sentiment-oriented summarization of multi-documents using soft computing (called ASMUS). It integrates two main phases: sentiment analysis and sentiment summarization. Sentiment analysis phase includes multiple strategies to tackle the following drawbacks: (1) word coverage limit of an individual lexicon; (2) contextual polarity; (3) sentence types, while the sentiment summarization phase is a graph-based ranking model that integrates the sentiment information, statistical and linguistic methods to improve the sentence ranking result. We found that the current methods are suffering from the following problems: (1) they do not consider the semantic and syntactic information in comparison between two sentences when they share the similar bag-of-words (capturing meaning); (2) vocabulary mismatch problem (lexical gaps). Furthermore, ASMUS also considers content coverage and redundancy. We conduct the experiments on the Document Understanding Conference datasets. The results present the excellent outcomes of the ASMUS in sentiment-oriented summarization.

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Notes

  1. http://dev.mysql.com/doc/refman/5.5/en/fulltext-stopwords.html.

  2. http://jmlr.csail.mit.edu/papers/volume5/lewis04a/a11-smart-stop-list/english.stop.

  3. http://www.cs.cmu.edu/~mccallum/bow/rainbow/.

  4. We use the negation words collected in Kolchyna et al. (2015) as a basic set, such as “no”, “not”, “don’t”, “hardly”, “none”, “never”, “are not”, “was not”, “did not”, “seldom”, “nothing”, “isn’t”,….

  5. “Not only”, “not wholly”, “not all”, “not just”, “not quite”, “not least”, “no question”, ….

  6. Like “but”, “with the exception of”, “except that”, “except for”, “however”, “yet”, “unfortunately”,….

  7. We use the negation words collected in Abdi et al. (2016) as a basic set, such as “therefore”, “thus”, “consequently”, “hence”, “as a result”, “to conclude”, “in conclusion”, “as a result”,” in short”, ….

  8. https://en.wikipedia.org/wiki/Normalization_(statistics).

  9. http://stackoverflow.com/questions/10364575/normalization-in-variable-range-x-y-in-matlab.

  10. http://duc.nist.gov.

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Acknowledgements

The authors would like to thank Research Management Centre (RMC), Universiti Teknologi Malaysia (UTM) for the support in R&D, UTM Big Data Centre (BDC), for the inspiration in making this study a success. The authors would also like to thank the anonymous reviewers who have contributed enormously to this work.

Funding

This work is supported by The Ministry of Higher Education (MOHE) under “Q.J130000.21A2.03E53 - STATISTICAL MACHINE LEARNING METHODS TO TEXT SUMMARIZATIONS” and “13H82 (INTELLIGENT PREDICTIVE ANALYTICS FOR RETAIL INDUSTRY).”

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Authors and Affiliations

  1. UTM Big Data Centre (BDC), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Asad Abdi, Siti Mariyam Shamsuddin & Shafaatunnur Hasan

  2. Department of Computer Science and Engineering, Sejong University, Seoul, South Korea

    Jalil Piran

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  1. Asad Abdi
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Correspondence to Asad Abdi.

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Abdi, A., Shamsuddin, S.M., Hasan, S. et al. Automatic sentiment-oriented summarization of multi-documents using soft computing. Soft Comput 23, 10551–10568 (2019). https://doi.org/10.1007/s00500-018-3653-4

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