Skip to main content
SpringerLink
Log in
Book cover

Sentiment Analysis and Ontology Engineering pp 341–377Cite as

Big Data Sentiment Analysis for Brand Monitoring in Social Media Streams by Cloud Computing

Part of the Studies in Computational Intelligence book series (SCI,volume 639)

Abstract

The rapid growth of the World Wide Web and social media allows users playing an active role in the contents’ creation process. Users can evaluate the brands’ reputation and quality exploiting the information provided by new marketing channels, such as social media, social networks , and electronic commerce (or e-commerce). Consequently, enterprises need to spot and analyze these digital data in order to improve their reputation among the consumers. The aim of this chapter is to highlight the common approaches of sentiment analysis in social media streams and the related issues with the cloud computing , providing the readers with a deep understanding of the state of the art solutions.

Keywords

  • Big data analyses
  • Brand monitoring
  • Cloud-based processing
  • Computational intelligence
  • Sentiment analysis
  • Social media stream

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. O’Reilly, T.: What is web 2.0—design patterns and business models for the next generation of software. Resource document. http://oreilly.com/web2/archive/what-is-web-20.html (2005). Accessed 15 Sept 2014

  2. Kietzmann, J.H., Hermkens, K., McCarthy, I.P., Silvestre, B.S.: Social media? Get serious! Understanding the functional building blocks of social media. Bus. Horizons 54(3), 241–251 (2011)

    CrossRef  Google Scholar 

  3. Noordhuis, P., Heijkoop, M., Lazovik, A.: Mining Twitter in the cloud: a case study. In: IEEE 3rd International Conference on Cloud Computing, pp. 107–114 (2010)

    Google Scholar 

  4. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I., Zaharia, M.: Above the clouds: a berkeley view of cloud computing. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Technical Report UCB/EECS, 28 (2009)

    Google Scholar 

  5. Shuai, Z., Shufen, Z., Xuebin, C., Xiuzhen, H.: Cloud computing research and development trend. In: 2nd International Conference on Future Networks, pp. 93–97 (2010)

    Google Scholar 

  6. Strauss, R.E., Schoder, D., Gebauer, J.: The relevance of brands for electronic commerce results from an empirical study of consumers in Europe. In: 34th Annual Hawaii International Conference on System Sciences, pp. 1–9 (2001)

    Google Scholar 

  7. Becker, K., Nobre, H., Kanabar, V.: Monitoring and protecting company and brand reputation on social networks: when sites are not enough. Glob. Bus. Econ. Rev. Inderscience Enterprises Ltd. 15(2/3), 293–308 (2013)

    CrossRef  Google Scholar 

  8. Terzi, N.: The impact of e-commerce on international trade and employment. Procedia—Soc. Behav. Sci. 24, 745–753 (2011)

    CrossRef  Google Scholar 

  9. Wang, L.: Post-crisis era of SMEs management innovation in E-commerce. In: 6th International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII), pp. 471–474 (2013)

    Google Scholar 

  10. Xiaofeng, L., Dong, L., Yuanxin, T.: Application of web data mining and data warehouse in e-commerce. In: 2nd International Conference on Software Technology and Engineering (ICSTE), pp. 376–379 (2010)

    Google Scholar 

  11. Ziegler, C.N., Skubacz, M.: Towards automated reputation and brand monitoring on the web. In: IEEE International Conference on Web Intelligence, pp. 1066–1072 (2006)

    Google Scholar 

  12. Li, L.: Study on the interactive relationship between customer’s emotional response and the brand trust – In the view of online shopping. In: IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 245–248 (2013)

    Google Scholar 

  13. Pang, B., Lee, L.: Opinion mining and sentiment analysis. In: Foundations and Trends in Information Retrieval, pp. 1–135 (2008)

    Google Scholar 

  14. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Human Lang. Technol. 5(1), 1–167 (2012)

    CrossRef  Google Scholar 

  15. Bothos, E., Apostolou, D., Mentzas, G.: Using social media to predict future events with agent-based markets. IEEE Intell. Syst. 99, 1, 1, 0

    Google Scholar 

  16. Asur, S., Huberman, B.A.: Predicting the future with social media. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 492–499, 31 Aug–3 Sept 2010

    Google Scholar 

  17. Lassen, N.B., Madsen, R., Vatrapu, R.: Predicting iPhone Sales from iPhone Tweets. In: 2014 IEEE 18th International Enterprise Distributed Object Computing Conference (EDOC), pp. 81–90, 1–5 Sept 2014

    Google Scholar 

  18. Stieglitz, S., Linh, D.-X.: Political communication and influence through microblogging-an empirical analysis of sentiment in Twitter messages and retweet behavior. In: 2012 45th Hawaii International Conference on System Science (HICSS), pp. 3500–3509, 4–7 Jan 2012

    Google Scholar 

  19. Hoang, T.-A., Cohen, W.W., Lim, E.-P., Pierce, D., Redlawsk, D.P.: Politics, sharing and emotion in microblogs. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 282–289, 25–28 Aug 2013

    Google Scholar 

  20. Duan, W., Cao, Q., Yu, Y., Levy, S.: Mining online user-generated content: using sentiment analysis technique to study hotel service quality. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 3119–3128, 7–10 Jan 2013

    Google Scholar 

  21. Marrese-Taylor, E., Velasquez, J.D., Bravo-Marquez, F.: Opinion zoom: a modular tool to explore tourism opinions on the web. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 261–264, 17–20 Nov 2013

    Google Scholar 

  22. Tedeschi, A., Benedetto, F.: A cloud-based tool for brand monitoring in social networks. In: Proceedings of the IEEE International Conference on Future Internet of Things and Cloud (FiCloud-2014), pp. 541–546 (2014)

    Google Scholar 

  23. Social Media Monitoring Tools—Brandwatch, Brandwatch. Resource document. http://www.brandwatch.com/. Accessed 15 Sept 2014

  24. Synthesio. Resource document. http://synthesio.com/corporate/en. Accessed 15 Sept 2014

  25. Radian6. Resource document. http://www.exacttarget.com/products/social-media-marketing/radian6. Accessed 15 Sept 2014

  26. Sangani, C., Sundaram, A.: Sentiment analysis of app store reviews. Methodology 4(1)

    Google Scholar 

  27. Guzman, E., Maalej, W.: How do users like this feature? A fine grained sentiment analysis of app reviews. In: 2014 IEEE 22nd International Requirements Engineering Conference (RE), 25–29 Aug 2014, pp. 153–162

    Google Scholar 

  28. Subu Sangameswar: Big Data—An Introduction

    Google Scholar 

  29. McKinsey Global Insitute: Big Data techniques and technologies. In: Big Data: The Next Frontier for Innovation, Competition and Productivity, June 2011

    Google Scholar 

  30. James Kobielus Measuring the Business Value of Big Data. http://www.ibmbigdatahub.com/blog/measuring-business-value-big-data

  31. Mayer-Schönberger, V., Cukier, K.: Big Data: A Revolution that Will Transform How We Live, Work, and Think

    Google Scholar 

  32. IBM: Performance and Capacity Implications for Big Data. http://www.redbooks.ibm.com/redpapers/pdfs/redp5070.pdf

  33. Cambria, E., Rajagopal, D., Olsher, D., Das, D.: Big social data analysis, Chapter 13. In: Big Data Computing

    Google Scholar 

  34. Rahnama, A.H.A.: Distributed real-time sentiment analysis for big data social streams. In: 2014 International Conference on Control, Decision and Information Technologies (CoDIT), pp. 789–794, 3–5 Nov 2014

    Google Scholar 

  35. Apache Storm. https://storm.apache.org/

  36. Mukkamala, R.R., Hussain, A., Vatrapu, R.: Fuzzy-set based sentiment analysis of big social data. In: 2014 IEEE 18th International Enterprise Distributed Object Computing Conference (EDOC), pp. 71–80, 1–5 Sept 2014

    Google Scholar 

  37. Google prediction API. https://cloud.google.com/prediction/docs/sentiment_analysis

  38. Minanovic, A., Gabelica, H., Krstic, Z.: Big data and sentiment analysis using KNIME: Online reviews vs. social media. In: 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1464–1468, 26–30 May 2014

    Google Scholar 

  39. Apache Nutch. http://nutch.apache.org/

  40. HBase. http://hbase.apache.org/

  41. Apache Hadoop. https://hadoop.apache.org/

  42. KNIME. https://www.knime.org/

  43. Conejero, J., Burnap, P., Rana, O., Morgan, J.: Scaling archived social media data analysis using a Hadoop cloud. In: 2013 IEEE Sixth International Conference on Cloud Computing (CLOUD), pp. 685–692, 28 June–3 July 2013

    Google Scholar 

  44. Rajurkar, G.D., Goudar, R.M.: A speedy data uploading approach for Twitter trend and sentiment analysis using HADOOP. In: 2015 International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 580–584, 26–27 Feb 2015

    Google Scholar 

  45. Jiang, L., Ge, B., Xiao, W., Gao, M.: BBS opinion leader mining based on an improved PageRank algorithm using MapReduce. In: Chinese Automation Congress (CAC), 2013, pp. 392–396, 7–8 Nov 2013

    Google Scholar 

  46. Zhang, S., Zhang, S., Chen, X., Huo, X.: Cloud computing research and development trend. In: Second International Conference on Future Networks, 2010. ICFN’10, pp. 93–97, 22–24 Jan 2010

    Google Scholar 

  47. Das, N.S., Usmani, M., Jain, S.: Implementation and performance evaluation of sentiment analysis web application in cloud computing using IBM Blue mix. In: 2015 International Conference on Computing, Communication and Automation (ICCCA), pp. 668–673, 15–16 May 2015

    Google Scholar 

  48. Amazon Elastic Compute Cloud (EC2). http://aws.amazon.com/it/ec2/

  49. Google App Engine https://cloud.google.com/appengine/docs

  50. Microsoft Windows Azure. http://azure.microsoft.com/

  51. Krishna, P.V., Misra, S., Joshi, D., Obaidat, M.S.: Learning automata based sentiment analysis for recommender system on cloud. In: International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1–5, 7–8 May 2013

    Google Scholar 

  52. Das, N.S., Usmani, M., Jain, S.: Implementation and performance evaluation of sentiment analysis web application in cloud computing using IBM Blue mix. In: 2015 International Conference on Computing, Communication and Automation (ICCCA), pp. 668–673, 15–16 May 2015

    Google Scholar 

  53. Hashem, I.A.T., Yaqoob, I., Badrul Anuar, N., Mokhtar, S., Gani, A., Khan, S.U.: The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 47, 98–115 (2015). ISSN: 0306–4379

    Google Scholar 

  54. Talia, D.: Clouds for scalable big data analytics. Computer 46(5), 98–101 (2013)

    CrossRef  Google Scholar 

  55. Marozzo, F., Talia, D., Trunfio, P.: Large-scale data analysis on cloud systems. In: Special theme: Big Data, ERCIM News, Apr 2012

    Google Scholar 

  56. Liu, B., Blasch, E., Chen, Y., Shen, D., Chen, G.: Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier. In: 2013 IEEE International Conference on Big Data, pp. 99–104, 6–9 Oct 2013

    Google Scholar 

  57. OpenNebula: The Open Source Solution for Data Center Virtualization. http://opennebula.org/

  58. Liu, B., Blasch, E., Chen, Y., Shen, D., Chen, G.: Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier. In: 2013 IEEE International Conference on Big Data, pp. 99–104, 6–9 Oct 2013

    Google Scholar 

  59. Liu, B.: Sentiment analysis and subjectivity. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of Natural Language Processing, 2nd edn. (2010)

    Google Scholar 

  60. Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer (2006 and 2011)

    Google Scholar 

  61. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)

    CrossRef  Google Scholar 

  62. Klein, A., Altuntas, O., Häusser, T., Kessler, W.: Extracting investor sentiment from weblog texts: a knowledge-based approach. In: IEEE 13th Conference on Commerce and Enterprise Computing, pp. 1–9 (2011)

    Google Scholar 

  63. Singh, V.K., Piryani, R., Uddin, A., Waila, P.: Sentiment analysis of textual reviews; Evaluating machine learning, unsupervised and SentiWordNet approaches. In: 5th International Conference on Knowledge and Smart Technology, pp. 122–127 (2013)

    Google Scholar 

  64. Schölkopf, B.., Burges, C., Smola, A.: Making large-Scale SVM learning practical. In Advances in Kernel Methods—Support Vector Learning. MIT Press (1999)

    Google Scholar 

  65. Shawe-Taylor, J., Cristianini, N.: Support Vector Machines. Cambridge University Press (2000)

    Google Scholar 

  66. Scarpmaker.com. Common misspellings. Resource document. http://scrapmaker.com/view/language/common-misspellings.txt. Accessed 15 Sept 2014

  67. Yoshida, S., Kitazono, J., Ozawa, S., Sugawara, T., Haga, T., Nakamura, S.: Sentiment analysis for various SNS media using Naïve Bayes classifier and its application to flaming detection. In: IEEE Symposium on Computational Intelligence in Big Data (CIBD), pp. 1–6, 9–12 Dec 2014

    Google Scholar 

  68. Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall

    Google Scholar 

  69. Bo, P., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2002) (2002)

    Google Scholar 

  70. Troussas, C., Virvou, M., Junshean Espinosa, K., Llaguno, K., Caro, J.: Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning. In: Fourth International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–6, 10–12 July 2013

    Google Scholar 

  71. Colace, F., De Santo, M., Greco, L.: A probabilistic approach to tweets’ sentiment classification. In: Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), pp. 37–42, 2–5 Sept 2013

    Google Scholar 

  72. Akaichi, J., Dhouioui, Z., Lopez-Huertas Perez, M.J.: Text mining facebook status updates for sentiment classification. In: 17th International Conference System Theory, Control and Computing (ICSTCC), pp. 640–645, 11–13 Oct 2013

    Google Scholar 

  73. Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1555–1565 (2014)

    Google Scholar 

  74. Neethu, M.S., Rajasree, R.: Sentiment analysis in twitter using machine learning techniques. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–5, 4–6 July 2013

    Google Scholar 

  75. Zhang, H., Gan, W., Jiang, B.: Machine learning and lexicon based methods for sentiment classification: a survey. In: 2014 11th Web Information System and Application Conference (WISA), pp. 262–265, 12–14 Sept 2014

    Google Scholar 

  76. WordNet-Affect. http://wndomains.fbk.eu/wnaffect.html

  77. Strapparava, C., Valitutti, A.: WordNet affect: an affective extension of WordNet. LREC 4 (2004)

    Google Scholar 

  78. Jamoussi, S., Ameur, H.: Dynamic construction of dictionaries for sentiment classification. In: Third International Conference on Cloud and Green Computing (CGC), pp. 418–425, 30 Sept 2013–2 Oct 2013

    Google Scholar 

  79. Bosco, C., Patti, V., Bolioli, A.: Developing Corpora for sentiment analysis: the case of Irony and Senti-TUT. IEEE Intell. Syst. 28(2), 55, 63, Mar-Apr 2013

    Google Scholar 

  80. Madhoushi, Z., Hamdan, A.R., Zainudin, S.: Sentiment analysis techniques in recent works. In: Science and Information Conference (SAI), 28–30 July 2015, pp. 288–291 (2015)

    Google Scholar 

  81. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    Google Scholar 

  82. Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42(1–2), 177–196 (2001)

    Google Scholar 

  83. Hong, L.: Probabilistic latent semantic analysis. arXiv:1212.3900

  84. Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th International Conference on World Wide Web, pp. 111–120. ACM, Apr 2008

    Google Scholar 

  85. Liu, Y., Huang, X., An, A., Yu, X.: ARSA: a sentiment-aware model for predicting sales performance using blogs. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 607–614. ACM (2007)

    Google Scholar 

  86. Bagheri, A., Saraee, M., De Jong, F.: ADM-LDA: An aspect detection model based on topic modelling using the structure of review sentences. J. Inf. Sci. 40(5), 621–636 (2014)

    Google Scholar 

  87. Ding, W., Song, X., Guo, L., Xiong, Z., Hu, X.: A novel hybrid HDP-LDA model for sentiment analysis. In: International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM, pp. 329–336, 17–20 Nov 2013

    Google Scholar 

  88. Sowmiya, J.S., Chandrakala, S.: Joint sentiment/topic extraction from text. In: International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 611–615, 8–10 May 2014

    Google Scholar 

  89. Usha, M.S., Indra Devi, M.: Analysis of sentiments using unsupervised learning techniques. In: International Conference on Information Communication and Embedded Systems (ICICES), pp. 241–245, 21–22 Feb 2013

    Google Scholar 

  90. Hu, X., Tang, J., Gao, H., Liu, H.: Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 607–618. International World Wide Web Conferences Steering Committee, May 2013

    Google Scholar 

  91. Terrana, D., Augello, A., Pilato, G.: Automatic unsupervised polarity detection on a Twitter data stream. In: IEEE International Conference on Semantic Computing (ICSC), pp. 128–134, 16–18 June 2014

    Google Scholar 

  92. Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning. The MIT Press (2006)

    Google Scholar 

  93. Prakash, V.J., Nithya, D.L.: A Survey on Semi-Supervised Learning Techniques. arXiv:1402.4645 (2014)

    Google Scholar 

  94. Zhu,, X.: Semi-supervised learning literature survey (2008)

    Google Scholar 

  95. Yang, B.: Semi-supervised Learning for Sentiment Classification

    Google Scholar 

  96. He, Y., Zhou, D.: Self-training from labeled features for sentiment analysis. Inf. Process. Manage. 47(4), 606–616 (2011). ISSN: 0306–4573

    Google Scholar 

  97. Hong, S., Lee, J., Lee, J.-H.: Competitive self-training technique for sentiment analysis in mass social media. In: 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on Soft Computing and Intelligent Systems (SCIS), pp. 9–12, 3–6 Dec 2014

    Google Scholar 

  98. Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Mach. Learn. 39(2–3), 103–134 (2000)

    CrossRef  MATH  Google Scholar 

  99. Yang, M., Tu, W., Lu, Z., Yin, W., Chow, K.P.: LCCT: A Semi-supervised model for sentiment classification. In: The Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), June 2015. ACL, USA (2015)

    Google Scholar 

  100. Xia, R., Wang, C., Dai, X., Li, T.: Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation

    Google Scholar 

  101. Yang, Z., Liu, Z., Liu, S., Min, L., Meng, W.: Adaptive multi-view selection for semi-supervised emotion recognition of posts in online student community. Neurocomputing 144, 138–150 (2014). ISSN: 0925–2312

    Google Scholar 

  102. Hajmohammadi, M.S., Ibrahim, R., Selamat, A.: Cross-lingual sentiment classification using multiple source languages in multi-view semi-supervised learning. Eng. Appl. Artif. Intell. 36, 195–203 (2014). ISSN: 0952–1976

    Google Scholar 

  103. Lazarova, G., Koychev, I.: Semi-supervised multi-view sentiment analysis. In: Computational Collective Intelligence, pp. 181–190. Springer International Publishing (2015)

    Google Scholar 

  104. Yong, R., Nobuhiro, K., Yoshinaga, N., Kitsuregawa, M.: Sentiment classification in under-resourced languages using graph-based semi-supervised learning methods. IEICE Trans. Inf. Syst. 97(4), 790–797 (2014)

    Google Scholar 

  105. Goldberg, A.B., Zhu, X.: Seeing stars when there aren’t many stars: graph-based semi-supervised learning for sentiment categorization’. In: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing (TextGraphs-1). Association for Computational Linguistics, USA, pp. 45–52 (2006)

    Google Scholar 

  106. Lu, T.J.: Semi-supervised microblog sentiment analysis using social relation and text similarity. In: 2015 International Conference on Big Data and Smart Computing (BigComp), pp. 194–201, 9–11 Feb 2015

    Google Scholar 

  107. Balahur, A.: Sentiment analysis in social media texts. In: 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 120–128 (2013)

    Google Scholar 

  108. Habernal, I., Ptácek, T., Steinberger, J.: Sentiment analysis in Czech social media using supervised machine learning. In: 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 65–74 (2013)

    Google Scholar 

  109. Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: the good the bad and the omg! In: 5th International Conference on Weblogs and Social Media, pp. 538–541 (2011)

    Google Scholar 

  110. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data, pp. 30–38. Workshop on Languages in Social, Media (2011)

    Google Scholar 

  111. Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, pp. 1–12 (2009)

    Google Scholar 

  112. Bifet, A., Frank, E.: Sentiment knowledge discovery in Twitter streaming data. In: 3th International Conference on Discovery Science, pp. 1–15 (2010)

    Google Scholar 

  113. Zhao, J., Dong, L., Wu, J., Xu, K.: Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In: 18th International Conference on Knowledge Discovery and Data Mining, pp. 1528–1531 (2012)

    Google Scholar 

  114. Ptaszynski, M., Maciejewski, J., Dybala, P., Rzepka, R., Araki, K.: CAO: a fully automatic emoticon analysis system based on theory of kinesics. IEEE Trans. Affect. Comput. 46–59 (2010)

    Google Scholar 

  115. Wikipedia. List of emoticons. Resource document. http://en.wikipedia.org/wiki/List_of_emoticons. Accessed 15 Sept 2014

  116. NoSlang.com. Slang Dictionary—Text Slang, Internet Slang, and Abbreviations A guide to everyday acronyms and obscure abbreviations. Resource document. http://www.noslang.com/dictionary/. Accessed 15 Sept 2014

  117. Chatslang.com. Social Media slang—Chat slang terms used in social media. Resource document. http://www.chatslang.com/terms/social_media. Accessed 15 Sept 2014

  118. Beal, V.: Twitter dictionary: a guide to understanding Twitter Lingo. Resource document. http://www.webopedia.com/quick_ref/Twitter_Dictionary_Guide.asp. Accessed 15 Sept 2014

  119. Roberts, K., Roach, M.A., Johnson, J., Guthrie, J., Harabagiu, S.M.: EmpaTweet: annotating and detecting emotions on Twitter. In: Language Resources and Evaluation Conference, pp. 3806–3813 (2012)

    Google Scholar 

  120. Balahur, A.: OPTWIMA: Comparing knowledge-rich and knowledge-poor approaches for sentiment analysis in short informal texts. In: The Second Joint Conference on Lexical and Computational Semantics, pp. 460–465 (2013)

    Google Scholar 

  121. Hemalatha, I., Varma, G.S., Govardhan, A.: Preprocessing the informal text for efficient sentiment analysis. Int. J. Emerg. Trends Technol. Comput. Sci. 58–61 (2012)

    Google Scholar 

  122. Serban, O., Pauchet, A., Rogozan, A., Pecuchet, J.P.: Semantic propagation of contextonyms using SentiWordNet. Resource document. http://asi.insa-rouen.fr/enseignants/apauchet/Files/Publications/WACAI12b.pdf. Accessed 15 Sept 2014

  123. Census.gov. Resource document.http://www.census.gov/genealogy/www/data/1990surnames/dist.all.last. Accessed 15 Sept 2014

  124. Strategic Name development. A Brand Naming Company. Resource document. http://www.namedevelopment.com/brand-names.html. Accessed 15 Sept 2014

  125. Torunoglu, D., Eryigit, G.: A cascaded approach for social media text normalization of Turkish. In: 5th Workshop on Language Analysis for Social Media, pp. 62–70 (2014)

    Google Scholar 

  126. Han, B., Baldwin, T.: Lexical normalization of short text messages: makn sens a # Twitter. In: 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 368–378 (2011)

    Google Scholar 

  127. Miller, F.P., Vandome, A.F., McBrewster, J.: Levenshtein Distance. ISBN: 9786130216900

    Google Scholar 

  128. Wikipedia. List of common misspellings. Resource document. http://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings. Accessed 15 Sept 2014

  129. Stanford University. Stanford Log-linear Part-Of-Speech Tagger. Resource document. http://nlp.stanford.edu/software/tagger.shtml. Accessed 15 Sept 2014

  130. McPherson, M., Smith-Lovin, L., Cook J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 415–444 (2001)

    Google Scholar 

  131. Lazarsfeld, P.F., Merton, R.K.: Friendship as a social process: a substantive and methodological analysis. In: Berger, M., Abel, T., Page, C.H. (eds.) Freedom and Control in Modern Society, pp. 8–66. Van Nostrand, New York (1954)

    Google Scholar 

  132. Chen, C., Gao, D., Li, W., Hou, Y.: Inferring topic-dependent influence roles of Twitter users. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR’14). ACM, New York, NY, USA, pp. 1203–1206 (2014)

    Google Scholar 

  133. Twitaholic. Resource document: http://twitaholic.com. Accessed 15 Sept 2014

  134. Leavitt, A., Burchard, E., Fisher, D., Gilbert, S.: The influentials: new approaches for analyzing influence on Twitter. Web Ecology project. http://www.webecologyproject.org/wpcontent/uploads/2009/09/influence-report-final.pdf (2009)

  135. Mtibaa, A., May, M., Diot, C., Ammar, M.: PeopleRank: social opportunistic forwarding. In: INFOCOM, Proceedings IEEE, pp. 1–5, 14–19 Mar 2010

    Google Scholar 

  136. Weng, J., Lim, E.-P., Jiang, J., He: TwitterRank: finding topic-sensitive influential Twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining (ACM WSDM) (2010)

    Google Scholar 

  137. Moreno, F., Gonzalez, A., Valencia, A. Computing the minimum number of new friends required in a social network to get the highest PageRank. Int. J. Complex Syst. Sci. 1, 37–42 (2012)

    Google Scholar 

  138. Deng, H., Han, J., Li, H., Ji, H., Wang, H., Lu, Y.: Exploring and inferring user-user pseudo-friendship for sentiment analysis with heterogeneous networks. Stat. Anal. Data Mining: ASA Data Sci. J. 7(4), 308–321 (2014)

    Google Scholar 

  139. Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’11). ACM, New York, NY, USA, pp. 1397–1405 (2011)

    Google Scholar 

  140. Hu, X., Tang, L., Tang, J., Liu. H.: Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (WSDM’13). ACM, New York, NY, USA, pp. 537–546 (2013)

    Google Scholar 

  141. Ahsan, M., Singh, T., Kumari, M.: Influential node detection in social network during community detection. In: International Conference on Cognitive Computing and Information Processing (CCIP), pp. 1–6, 3–4 Mar 2015

    Google Scholar 

  142. Zhang, J., Ma, X., Liu, W., Bai, Y.: Inferring community members in social networks by closeness centrality examination. In: 2012 Ninth Web Information Systems and Applications Conference (WISA), pp. 131–134, 16–18 Nov 2012

    Google Scholar 

  143. Bermingham, A., Conway, M., McInerney, L., O’Hare, N., Smeaton, A.F.: Combining social network analysis and sentiment analysis to explore the potential for online radicalisation. In: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Miningi, IEEE Computer Society, Washington, DC, USA, pp. 231–236 (2009)

    Google Scholar 

  144. Mei, Y., Zhong, Y., Yang, J.: Finding and analyzing principal features for measuring user influence on Twitter. In: 2015 IEEE First International Conference on Big Data Computing Service and Applications (BigDataService), pp. 478–486, 30 Mar 2015–2 Apr 2015

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics, University of Rome “Roma TRE”, via Vito Volterra, 62, 00146, Rome, Italy

    Francesco Benedetto

  2. Department of Engineering, University of Rome “Roma TRE”, via Vito Volterra, 62, 00146, Rome, Italy

    Antonio Tedeschi

Authors
  1. Francesco Benedetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonio Tedeschi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Francesco Benedetto .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. and Technology, National Taiwan University of Scien, Taipei, Taiwan

    Shyi-Ming Chen

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Benedetto, F., Tedeschi, A. (2016). Big Data Sentiment Analysis for Brand Monitoring in Social Media Streams by Cloud Computing. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30319-2_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30317-8

  • Online ISBN: 978-3-319-30319-2

  • eBook Packages: EngineeringEngineering (R0)

search

Navigation