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14.2 - Analyzing, Modeling, and Summarizing Social Media and Linked Data Sets

Project - Summary


  • Utilize data from multiple source systems for analysis purposes, by integrating both internal data and large volumes of data from outside systems (third party, social media). This integration empowers internal business users with the capability of analyzing customer info, product info, financial info, procurement info, weather data, etc. from multi-dimension and multi-view perspectives in different aggregation and granular levels.
  • Develop effective and efficient data mining models, tools and techniques to improve business intelligence, by addressing real-world issues such as customer opinion, improving personalized service, and reducing customer attrition and improving cross-selling and up-selling in customer relationship management.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Xiaohua Hu PI Not available Not available Drexel University
Xiaoli Song Graduate Student Not available Not available Drexel University

Project - Impact and Uses/Benefits

We developed the proposed hybrid HDP-LDA model, and it can improve the performance of sentiment analysis in e-Business application. We tested our prototype system in many open data sets, however, the approach can be easily generalized to the IAB members' data sets. The outcomes of the project provide techniques for easily processing big data in analytic environments. The results of the study improve productivity for extracting greater value from big unstructured data. The modules of the system are implemented in Java languages. The complete API will be provided in a software package.

Project - Deep Dive

Project - Documents

FilenameFilesizeLast modified
14.2_year_3_executive_summaries_combined.pdf441.9 KiB2019/08/22 11:26
14.2_year_3_cvdi_ip_letter_combined.pdf769.4 KiB2019/08/22 11:26
14.2_year_3_final_report.pdf1.2 MiB2019/08/22 10:21
projects/year3/14.2.txt · Last modified: 2019/08/22 10:22 by sally.johnson