View source for Session 6 - NLP for EM and visualization/Dashboards

'''Date:''' Friday October 26th

'''Papers:'''
* 1) Neppalli, Venkata K., et al., 2017, '''Sentiment analysis during Hurricane Sandy in emergency response''', International journal of disaster risk reduction, Vol. 21, PP: 213-222, [[:File:P1a.pdf]]. 
* 2) Burel, Grégoire, Hassan Saif, and Harith Alani, 2017, '''Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media''', International Semantic Web Conference, PP: 138-155, [[:File:P2a.pdf]].
* 3) Verma, Sudha, et al., 2011, '''Natural Language Processing to the Rescue? Extracting “Situational Awareness” Tweets During Mass Emergency ''', Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, PP:1--8, [[:File:P3a.pdf]]
* 4) Shen, Shi, et al., 2017, '''Information retrieval of a disaster event from cross-platform social media''', International journal of Information Discovery and Delivery, Vol. 45(4), PP: 220-226. [[:File:P4a.pdf]]
* 5) Francesco Tarasconi, Michela Farina, Antonio Mazzei, Alessio Bosca, 2017, '''The Role of Unstructured Data in Real-Time Disaster-related Social Media Monitoring''', IEEE International Conference on Big Data, PP:1--10, [[:File:P6a.pdf]].
* 6) Michael Aupetit et al., 2017, '''Interactive Monitoring of Critical Situational Information on Social Media''', Proceedings of the 14th ISCRAM Conference, PP:1--11, [[:File:P7a.pdf]]
* 7) Avvenuti, M., Cresci, S., Del Vigna, F., Fagni, T., & Tesconi, M., 2018, '''CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing''',  Information Systems Frontiers, PP: 1-19, [[:File:P11.pdf]]
* 8) Ngamassi et al., 2017, '''Social Media Visual Analytic Toolkits for Disaster Management: A Review of the Literature''', Proceedings of the 14th ISCRAM Conference, PP: 1--13, [[:File:P10a.pdf]] 


'''Suitable readings:''' 
* [https://spark.apache.org/docs/latest/ml-guide.html Machine Learning Library (MLlib) Guide]
* [https://www.edureka.co/blog/spark-streaming/ Spark Streaming Tutorial – Sentiment Analysis Using Apache Spark]
* [https://www.technologyreview.com/s/513696/deep-learning/ Artificial intelligence is finally getting smart]
* [https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners Machine learning]
* [https://databricks.com/blog/2014/10/15/application-spotlight-tableau-software.html Data visualization on Tableau with Spark]
* [https://spark.apache.org/docs/1.4.1/ml-guide.html#pipeline Spark MLlib]

* Paper presentations by you:
** Paper 1: '''Sebastian Øverhaug Larsen'''  [[Presentation slides from student:File:S6_PS1.pdf]]
** Paper 2: '''Taume Dery'''  [[Presentation slides from student:File:S6_PS2.pdf]]
** Paper 3: '''Raor Håkonsen Helland'''  [[Presentation slides from student:File:S6_PS3.pdf]]
** Paper 4: '''Andreas Iden'''  [[Presentation slides from student:File:S6_PS4.pdf]]
** Paper 5: '''Marius Falch Lillevik'''  [[Presentation slides from student:File:S6_PS5.pdf]]
** Paper 6: '''Kari Raudstein'''  [[Presentation slides from student:File:S6_PS6.pdf]]
** Paper 7: '''Espen Holst Wiik'''  [[Presentation slides from student:File:S6_PS7.pdf]]
** Paper 8: '''Sara Stegane'''  [[Presentation slides from student:File:S6_PS8.pdf]]


'''Slides:''' 
Session-6: [[:File:ML-IVD.pdf]]

'''Useful Links:'''
* [https://www.youtube.com/watch?v=5Zg-C8AAIGg The Beauty of Data Visualization by David]
*[https://www.youtube.com/watch?v=vFu7EFfC-Xc NLP for Social Media]