Advertisement

Tweets Competitive Sentimental Analysis of Android Mobile Brands to Understand Customer Experience

  • Umair Liaquat Ali
  • Tahir Ali
  • Imran AhmadEmail author
  • Shahid Kamal
Conference paper
  • 497 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 932)

Abstract

With the dawn of the social media era the world has connected more than ever, every opinion, news and discussion is now online. Public opinion data is freely available and accessible through the API of the provider. Data mining, text mining and sentimental analysis provide insight of data. Companies hold official pages on micro-blogging websites like Twitter. This helps them to introduce products and keep in touch with customers. We choose the three Android phone selling brands which are Samsung, Oppo &, Nokia and do our analysis on the tweets posted on official page as a response to officially posted tweets or mentioned using hashtags “#” or mentioned tag “@”. We performed a competitive analysis on our finding to find similarities & differences. In the end, we provided recommendations on how to make a better competitive analysis strategy to win the market both on social media forum and in the sale market.

Keywords

Twitter Sentiment analysis Natural language processing techniques Tweets mining Tweets sentimental analysis Social media 

References

  1. 1.
    Zhang, C., Zeng, D., Li, J., Wang, F., Zuo, W.: Sentiment analysis of Chinese documents: from sentence to document level. J. Am. Soc. Inform. Sci. Technol. 60, 2474–2487 (2009)Google Scholar
  2. 2.
    Bai, X.: Predicting consumer sentiments from online text. Decis. Support Syst. 50, 732–742 (2010).  https://doi.org/10.1016/j.dss.2010.08.024Google Scholar
  3. 3.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1-2) (2008)Google Scholar
  4. 4.
    Zagal, J., Tomuro, N., Shepitsen, A.: Natural language processing in game studies research: an overview. Simul. Gaming 43, 356–373 (2012)Google Scholar
  5. 5.
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
    Rooney, D.: Knowledge, economy, technology and society: the politics of discourse. Telematics Inf. 22, 405–422 (2005)Google Scholar
  11. 11.
    He, W., Zha, S.H.: Insights into the adoption of social media mashups. Internet Res. 24, 160–180 (2014)Google Scholar
  12. 12.
    Holzner, S.: Facebook Marketing: Leverage Social Media to Grow Your Business. Pearson Education, London (2008)Google Scholar
  13. 13.
    De Vries, L., Gensler, S., Leeflang, P.S.: Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing. J. Interact. Mark. 26, 83–91 (2012)Google Scholar
  14. 14.
  15. 15.
    Campbell, D.G.: Egypt Unshackled: Using Social Media the System. Cambria Books, Amherst (2011)Google Scholar
  16. 16.
    Gayo-Avello, D., Metaxas, P.T., Mustafaraj, E.: Limits of electoral predictions using twitter. In: Proceedings of the International Conference on Weblogs and Social Media, Barcelona, Spain, vol. 21, pp. 490–493. AAAI (2011)Google Scholar
  17. 17.
    Fisher, B., Miller, H.: Social media analytics (2011). http://www.microtech.net/sites/default/files/socialmediaanalytics.pdf. Accessed 21 Sept 2014
  18. 18.
    Kim, S., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the International Conference on Computational Linguistics (COLING 2004), East Stroudsburg, PA, p. 1367 (2004)Google Scholar
  19. 19.
    He, W., Yan, G.: Mining blogs and forums to understand the use of social media in customer co-creation. Comput. J. (2014).  https://doi.org/10.1093/comjnl/bxu038
  20. 20.
    Vishwanath, J., Aishwarya, S.: User suggestions extraction from customer reviews. Int. J. Comput. Sci. Eng. 3, 1203–1206 (2011)Google Scholar
  21. 21.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), Philadelphia, PA, pp. 417–424 (2002)Google Scholar
  22. 22.
    Wong, K., Xia, Y., Xu, R., Wu, M., Li, W.: Pattern-based opinion mining for stock market trend prediction. Int. J. Comput. Process. Lang. 21, 347–361 (2008)Google Scholar
  23. 23.
    Mohammad, S.: From once upon a time to happily ever after: tracking emotions in mail and books. Decis. Support Syst. 53, 730–741 (2012)Google Scholar
  24. 24.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011)Google Scholar
  25. 25.
    Stieglitz, S., Dang-Xuan, L.: Social media and political communication: a social media analytics framework. Soc. Netw. Anal. Min. 3, 1277–1291 (2013)Google Scholar
  26. 26.
    Cavnar, W.B., Trenkle, J.M.: N-gram-based text categorization, Ann Arbor, MI, pp. 161–175 (1994)Google Scholar
  27. 27.
    Stieglitz, S., Dang-Xuan, L.: Emotions and information diffusion in social media sentiment of microblogs and sharing behavior. J. Manag. Inf. Syst. 29, 217–248 (2013)Google Scholar
  28. 28.
    He, W., Tian, X., Chen, Y., Chong, D.: Framework for conducting competitive analysis on social media (2016)Google Scholar
  29. 29.
  30. 30.
    Tsur, O., Rappoport, A.: What’s in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 643–652. ACM, New York (2012)Google Scholar
  31. 31.
    Honeycutt, C., Herring, S.: Beyond microblogging: conversation and collaboration via Twitter. In: Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS 2009), pp. 1–10 (2009)Google Scholar
  32. 32.
    Miao, Q., Li, Q., Zeng, D.: Fine-grained opinion mining by integrating multiple review sources. J. Am. Soc. Inf. Sci. Technol. 61, 2288–2299 (2010)Google Scholar
  33. 33.
    Hu, N., Bose, I., Koh, N.S., Liu, L.: Manipulation of online reviews: an analysis of ratings, readability, and sentiments. Decis. Support Syst. 52(3), 674–684 (2012). ISSN 0167-9236Google Scholar
  34. 34.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of HLT-EMNLP-2005 (2005)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Umair Liaquat Ali
    • 1
    • 2
    • 3
    • 4
  • Tahir Ali
    • 1
    • 2
    • 3
    • 4
  • Imran Ahmad
    • 1
    • 2
    • 3
    • 4
    Email author
  • Shahid Kamal
    • 1
    • 2
    • 3
    • 4
  1. 1.University of Central PunjabLahorePakistan
  2. 2.Gulf University of Science and TechnologyKuwait CityKuwait
  3. 3.Riphah International UniversityLahorePakistan
  4. 4.ICITGomal UniversityDIKhanPakistan

Personalised recommendations