Multimodal Sentiment Analysis of Instagram Using Cross-media Bag-of-words Model

Instagram, one of social media sharing services has increasing growth of use and popularity during recent years. Photos or videos shared by Instagram users are challenging to be mined and analyzed for some purposes. One type of studies can be applied to Instagram data is sentiment analysis, a field of study that learn and analyze people opinion, sentiment, and (or) evaluation about something. Sentiment analysis applied to Instagram can be used as analytics tool for some business purposes such as user behavior, market intelligence and user evaluation. This research aimed to analyze sentiment contained on Instagrams post by considering two modalities: images and English text on its caption. The Cross-media Bag-of-Words Model (CBM) was applied for analyzing the sentiment contained on Instagrams post. CBM treated text and image features as a unit of vector representation. These cross-media features then classified using logistic regression to predict sentiment values which categorized into three classes: positive, negative and neutral. Simulation results showed that the combination of unigram text features and 56-length images features achieves the highest accuracy. The accuracy achieved is 87.2%. Keywords : Instagram, sentiment analysis, Cross-media Bag-of-Words Model (CBM), logistic regression, classification Bibliography [1] D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang, “Large-scale visual sentiment ontology and detectors using adjective noun pairs,” in Proceedings of the 21st ACM International Conference on Multimedia, ser. MM '13. New York, NY, USA: ACM, 2013, pp. 223–232. [2] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, “Liblinear: A library for large linear classification,” J. Mach. Learn. Res., vol. 9, pp. 1871– 1874, Jun. 2008. [3] E. Ferrara, R. Interdonato, and A. Tagarelli, “Online popularity and topical interests through the lens of instagram,” in Proceedings of the 25th ACM Conference on Hypertext and Social Media, ser. HT '14. New York, NY, USA: ACM, 2014, pp. 24–34. [4] N. Gunawardena, J. Plumb, N. Xiao, and H. Zhang, “Instagram hashtag sentiment analysis,” in University of Utah CS530/CS630 Conference of Machine Learning 2013. Universiti of Utah CS530/CS630 Conference of Machine Learning 2013, 2013. [5] J. Han, Data Mining: Concepts and Techniques. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2005. [6] M. Hu and B. Liu, “Mining and summarizing customer reviews,” inProceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2004, pp. 168–177.[7] Y. Hu, L. Manikonda, and S. Kambhampati, “What we instagram: A first analysis of instagram photo content and user types,” International AAAI Conference on Weblogs and Social Media, 2014. [8] L. S. Huey and R. Yazdanifard, “How instagram can be used as a tool in social networking marketing,” Help College of Art and Technology Malaysia, Tech. Rep., 2014. [9] D. Jurafsky and J. H. Martin, Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition(2nd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2009. [10] S. G. K and S. Joseph, “Text classification by augmenting bag of words ( bow ) representation with co-occurrence feature,” IOSR Journal of Computer Engineering (IOSR-JCE), vol. 16, pp. 34–38, 1 2014. [11] B. B. Kachru, The Alchemy of English: The Spread, Functions and Models of Non-native Englishes. Champaign: University of Illinois Press, 1990. [12] S. S. Keerthi and C.-J. Lin, “Asymptotic behaviors of support vector machines with gaussian kernel,” Neural Comput., vol. 15, no. 7, pp. 1667–1689, Jul. 2003. [13] M. Koppel and J. Schler, “The importance of neutral examples for learning sentiment,” in In Workshop on the Analysis of Informal and Formal Information Exchange during Negotiations, 2005.[14] A. Kowcika, A. Gupta, K. Sondhi, N. Shivhre, and R. Kumar, “Sentiment analysis for social media,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, 7 2013. [15] F.-F. Li and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02, ser. CVPR '05. Washington, DC, USA: IEEE Computer Society, 2005, pp. 524–531. [16] B. Liu, Sentiment Analysis and Opinion Mining. Morgan and Claypool Publisher, 2012. [17] W.-Y. Ma and K.-J. Chen, “A bottom-up merging algorithm for chinese unknown word extraction,” in Proceedings of the Second SIGHAN Workshop on Chinese Language Processing - Volume 17, ser. SIGHAN '03. Stroudsburg, PA, USA: Association for Computational Linguistics, 2003, pp. 31–38. [18] Z. McCune, “Consumer production in social media networks: A case study of the instagram iphone app,” Ph.D. dissertation, Dr. John Thompson, 2011. [19] W. Medhata, A. Hassanb, and H. Korashyb, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, 2014. [20] L.-P. Morency, R. Mihalcea, and P. Doshi, “Toward multimodal sentiment analysis: Harvesting opinion from the web,” in International Conference on Multimodal Interface, 2011. [21] A. Pak and P. Paroubek, “Twitter as a corpus for sentiment analysis and opinion mining,” in Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), N. C. C. Chair), K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, and D. Tapias, Eds. Valletta, Malta: European Language Resources Association (ELRA), may 2010.[22] B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Foundation and Trends in Information Retrieval, vol. 2, no. 1-2, p. 4, 2008. [23] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: Sentiment classification using machine learning techniques,” in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, ser. EMNLP '02. Stroudsburg, PA, USA: Association for Computational Linguistics, 2002, pp. 79–86. [24] C.-Y. J. Peng, K. L. Lee, and G. M. Ingersol, “An introduction to logistic regression analysis and reporting,” The Journal of Educational Research, vol. 96, no. 1, September/October 2002. [25] V. V. Piyush Bansal, Romil Bansal, “Towards deep semantic analysis of hashtags,” 37th European Conference on Information Retrieval, 2015. [26] R. Plutchik, Emotion: A Psycho-evolutionary Synthesis. Harper and Row, 1980. [27] S. Poria, A. Hussain, and E. Cambria, “Beyond text based sentiment analysis: Towards multi-modal systems,” University of Stirling, Stirling FK9 4LA, UK, Tech. Rep., 2013. [Online]. Available: http://www.cs.stir.ac.uk/~spo/publication/resources/cogcomp.pdf [28] E. Praseyto, Data Mining Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Andi, 2012.[29] A. Qazi, R. G. Raj, M. Tahir, E. Cambria, and K. B. S. Syed, “Enhancing business intelligence by means of suggestive reviews,” The Scientific World Journal, vol. 2014, June 2014. [30] R. Schapire, “Machine learning algorithms for classification,” Princeton University, Tech. Rep. [31] S. Siersdorfer, E. Minack, F. Deng, and J. Hare, “Analyzing and predicting sentiment of images on the social web,” in Proceedings of the International Conference on Multimedia, ser. MM '10. New York, NY, USA: ACM, 2010, pp. 715–718. [32] T. H. Silva, P. O. S. V. de Melo, J. M. Almeida, J. Salles, and A. A. F. Loureiro, “A comparison of foursquare and instagram to the study of city dynamics and urban social behavior,” in Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing, ser. UrbComp '13. New York, NY, USA: ACM, 2013, pp. 4:1–4:8. [33] P. N. Stuart Russell, Artificial Intelligence A Modern Approach, M. Hirsch, Ed. New Jersey: Pearson Education, 2010. [34] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Comput. Linguist., vol. 37, no. 2, pp. 267– 307, Jun. 2011. [35] C.-F. Tsai, “Bag-of-words representation in image annotation: A review,” ISRN Artificial Intelligence, p. 19, 2012. [36] A. J. Viera and J. M. Garrett, “Understanding interobserver agreement: The kappa statistic,” Family Medicine, vol. 37, no. 5, pp. 360–363, May 2005. [37] M. Wang, D. Cao, L. Li, S. Li, and R. Ji, “Microblog sentiment analysis based on cross-media bag-of-words model,” in Proceedings of International Conference on Internet Multimedia Computing and Service, ser. ICIMCS '14. New York, NY, USA: ACM, 2014, pp. 76:76–76:80. [38] A. Westerski, “Sentiment analysis: Introduction and the state of the art overview,” Universidad Politecnica de Madrid, Spain, Tech. Rep., 2009. [39] F. Yu, L. Cao, R. Feris, J. Smith, and S.-F. Chang, “Designing category-level attributes for discriminative visual recognition,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013. [40] L. Yu and H. Liu, “Efficiently handling feature redundancy in high-dimensional data,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD '03. New York, NY, USA: ACM, 2003, pp. 685–690. [41] J. Yuan, S. Mcdonough, Q. You, and J. Luo, “Sentribute: Image sentiment analysis from a mid-level perspective,” in Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, ser. WISDOM '13. New York, NY, USA: ACM, 2013, pp. 10:1– 10:8

Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres

Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere from remote sensing observations. The large range of issues related to these interactions, and in particular to three-dimensional interactions, motivated the development of accurate radiative tools able to compute all types of radiative metrics, from monochromatic, local and directional observables, to integrated energetic quantities. In the continuity of this community effort, we propose here an open-source library for general use in Monte Carlo algorithms. This library is devoted to the acceleration of path-tracing in complex data, typically high-resolution large-domain grounds and clouds. The main algorithmic advances embedded in the library are those related to the construction and traversal of hierarchical grids accelerating the tracing of paths through heterogeneous fields in null-collision (maximum cross-section) algorithms. We show that with these hierarchical grids, the computing time is only weakly sensitivive to the refinement of the volumetric data. The library is tested with a rendering algorithm that produces synthetic images of cloud radiances. Two other examples are given as illustrations, that are respectively used to analyse the transmission of solar radiation under a cloud together with its sensitivity to an optical parameter, and to assess a parametrization of 3D radiative effects of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2

Online Misinformation: Challenges and Future Directions

Misinformation has become a common part of our digital media environments and it is compromising the ability of our societies to form informed opinions. It generates misperceptions, which have affected the decision making processes in many domains, including economy, health, environment, and elections, among others. Misinformation and its generation, propagation, impact, and management is being studied through a variety of lenses (computer science, social science, journalism, psychology, etc.) since it widely affects multiple aspects of society. In this paper we analyse the phenomenon of misinformation from a technological point of view.We study the current socio-technical advancements towards addressing the problem, identify some of the key limitations of current technologies, and propose some ideas to target such limitations. The goal of this position paper is to reflect on the current state of the art and to stimulate discussions on the future design and development of algorithms, methodologies, and applications