Skip to main content
SpringerLink
Log in

Sentiment analysis of movie reviews: finding most important movie aspects using driving factors

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The opinion conveyed by the user towards the movie can be understood by sentiment analysis of the movie review. In the current work we focus on finding the aspects of a movie review which direct its polarity the most. This is achieved using certain driving factors, which are scores given to the various movie aspects. Generally its found that aspects with high driving factors affect the review polarity the most.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Basaria ASH, Hussina B, Anantaa IGP, Zeniarjab J (2012) Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. In: Malaysian Technical Universities conference on engineering & technology (MUCET 2012) part 4: information and communication technology

  • Bro J, Ehrig H (2010) Generating a context-aware sentiment lexicon for aspect-based product review mining. In: IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology

  • Kang H, Yoo SJ, Han D (2012) Senti-lexicon and improved Naive Bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst Appl 39(5):6000–6010

  • Large Movie Review Dataset (2015) Acquired from stanford AI lab. http://ai.stanford.edu/~amaas/data/sentiment

  • Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies

  • Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learningtechniques. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), Philadelphia, pp 79–86

  • Parkhe V, Biswas B (2014) Aspect based sentiment analysis of movie reviews: finding the polarity directing aspects. In: Proceedings of international conference on soft computing and machine intelligence 2014

  • Sentiment Analysis (2015) Wikipedia-the free encyclopedia. http://wikipedia.org/wiki/Sentiment_analysis

  • SentiWordNet (2015) Lexical resource for opinion mining. http://sentiwordnet.isti.cnr.it

  • Singh VK, Piryani R, Uddin A, Waila P (2013) Sentiment analysis of movie reviews a new feature-based heuristic for aspect-level sentiment classification. In: Proceedings of the 2013 international muli-conference on automation, communication, computing, control and compressed sensing, Kerala-India, IEEE Xplore, pp 712–717

  • Stanford Part-Of-Speech Tagger (2015) Stanford natural language processing group. http://nlp.stanford.edu/software/tagger.shtml

  • Taboada M, Brook J, Stede M (2009) Genre-based paragraph classification for sentiment analysis. In: Proceedings of SIGDIAL 2009: the 10th annual meeting of the special interest group in discourse and dialogue, University of London, Queen Mary, pp 62–70

  • Thet TT, Na J-C, Khoo CSG (2010) Aspect-based sentiment analysis of movie reviews on discussion boards. J Inf Sci 36(6):823–848

  • Yu J, Zha Z-J, Wang M, Chua T-S (2011) Aspect ranking: identifying important product aspects from online consumer reviews. In: Proceedings of the 49th annual meeting of the association for computational linguistics, Portland, Oregon, pp 1496–1505, 19–24 June 2011

Download references

Acknowledgments

The above work is an extension of previous work published in ISCMI 2014 (Parkhe and Biswas 2014). Proper citations have been included for the same in the above work for transparency purposes.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, IIT-(BHU), Varanasi, India

    Viraj Parkhe & Bhaskar Biswas

Authors
  1. Viraj Parkhe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bhaskar Biswas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Viraj Parkhe.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest

Informed consent

Also Informed consent was obtained from all individual participants included in the study. This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by S. Deb, T. Hanne and S. Fong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Parkhe, V., Biswas, B. Sentiment analysis of movie reviews: finding most important movie aspects using driving factors. Soft Comput 20, 3373–3379 (2016). https://doi.org/10.1007/s00500-015-1779-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-015-1779-1

Keywords

Search

Navigation