Apologies for cross-posting.

*CFP: Workshop on Textual Customer Feedback Mining and Transfer Learning
(TCFMTL) in conjunction with IEEE BigData 2016*

The Workshop on Textual Customer Feedback Mining and Transfer Learning, in
conjunction with 2016 IEEE International Conference on Big Data (IEEE
BigData 2016), will be held in Washington D.C., USA. (Dec. 5-8, 2016)

For any business and organization, customer feedback, as the most direct
representative of user experience, is essential for improving the quality
of its products or services. The long history of breakthroughs and
advancements in communication technology and social media has been playing
a profound role in enhancing the capabilities of companies and
organizations to understand their customers, from speech to ancient cave
paintings to all sorts of social medias such as Skype, WhatsApp, Facebook,
Twitter, etc., from speech interviews to paper surveys to call center
services to online reviews or chats to feedback systems to social media
discussion platforms. Thus, mining information and gaining insights from
customer feedback through a variety of data sources like in-client systems,
surveys as well as publicly available data like online reviews and social
media discussions is crucial to the emerging tasks for department stores
and commercial companies in understanding and optimizing users’ thoughts
and attitudes towards their products and services. As increasingly high
volumes of electronic customer feedback become available to companies daily
in different forms such as elicited surveys, unsolicited comments,
suggestions, criticism as well as kudos, either in product or in social
media like Twitter/Reddit, more intelligent and automated big data analytic
mechanisms in customer feedback become critically in need. In addition,
textual customer feedback data is extremely noisy with lots of slang, dirty
words, incorrectly translated texts, and etc., due to the fact that users
all over the world have freedom to leave feedback. Text mining and
sentiment analytic technology has been a powerful weapon to solve these

Furthermore, in order to rapidly respond to the customer feedback and
direct the issues to the appropriate production and engineering departments
inside the companies, it is of great importance for them to understand what
topics are people talking about their service or products and how are
people feeling regarding different topics. The first problem is generally
addressed by various text mining techniques, such as clustering,
classification, topic modeling, text rule/ lexicon based approaches, and so
on. The second problem is mainly applied by some sentiment analysis
techniques, including latent semantic analysis, support vector machines,
and so on. Consequently, the emerging demands and challenges facilitate the
development and advancement of text mining and sentiment analysis
technology for customer feedback.

Recently, transfer learning has been widely discussed on the textual
customer feedback mining. Although widely applied on lots of scientific
research, conventional statistical machine learning revolves on a
simplified assumption that the training data, from which the algorithms
learn, are drawn i.i.d. from the same distribution as the test data, to
which the learned models are applied. This assumption, being broken down by
numerous real-world applications nowadays, especially with the emergence of
large-scale data from the private internal data, or the public Internet,
has fundamentally restricted the development of practical learning
algorithms. For example, intelligent recognition systems are trained to
recognize the identity of faces, to classify the category of objects, or to
understand the customer feedback; however, when deployed in the new
environment, they may confront strange faces with significantly different
facial appearances, objects in different shapes, colors, textures with
different background, or feedback for different products from different
groups of customers. Such issues attract substantial research attention in
the era of “Big Data”, as indicated by the National Academies report on
Frontiers in Massive Data Analysis:

“Data may have been collected according to a certain criterion, but the
inferences and decisions made may refer to a different sampling criterion.
This issue seems likely to be particularly severe in many massive data
sets, which often consist of many sub-collections of data, each collected
according to a particular choice of sampling criterion and with little
control over the overall composition”

The technical program will consist of, but is not limited to, the following
topics of interest:

-Text Analytics in Customer Feedback, Surveys and Reviews
-Text Mining of Social Media for Topic Trends
-Web-based Text Mining in Customer Reviews
-Sentiment Analysis in Customer Feedback, Surveys and Reviews
-Sentiment Analysis of Social Media for Customer Voice
-Big Data Science Behind Customer Feedback “Bot”
-Relational Text Mining
-Transfer Learning and Its Applications on Customer Feedback Analysis
-Transfer Learning and Social Media, e.g., Kin Relationship Understanding,
Knowledge Transfer between Different Web Sources: Twitter, Facebook,
YouTube, etc.
-Large-Scale Transfer Learning with Latent, Complex, or Noisy Source
-Large-Scale Transfer Learning with Multimodality, Multi-Source, or
Multi-View Data
-Integration of Transfer Learning and Human Knowledge, e.g., Active
Learning on Customer Feedback Analysis

Important Date:

Oct. 10, 2016: Due date for full workshop papers submission
Nov. 1, 2016: Notification of paper acceptance to authors
Nov. 15, 2016: Camera-ready of accepted papers
Dec. 5-8, 2016: Workshops


General Chairs
- Xin Deng, Data Scientist, Microsoft | Skype, Redmond, WA
- Yun Fu, Associate Professor, Northeastern University, MA
- Ross Smith, Principal Director of Engineering, Microsoft | Skype,
Redmond, WA
- Dong Xu, Professor, Department Chair, University of Sydney

Program Chairs
- Yu Cao, Associate Professor, Co-Director, UMass Center for Digital
Health, UMass Lowell, MA
- Amrita Ray, Senior Data Scientist, Microsoft | Skype, Redmond, WA
- Ming Shao, Assistant Professor, Umass Dartmouth, MA

Web and Publicity Chair
- Sheng Li, Northeastern University, MA
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