About Me

My name is Wenbo Wang, a last year Computer Science Ph.D student at Kno.e.sis Center, Wright State University. My broad research interests include: Text Mining, Natural Language Processing and Social Computing. My current focus is on Emotion Identification, Social Media Analytics and Sentiment Analysis. My PhD advisor is Dr. Amit Sheth.

In my spare time, I pay attention to inconvenience and trouble in the daily life. I like thinking about how to reduce them with the help of technology.


  • Research Assistant, Department of Computer Science, Wright State University, 2012 Jul. - present.
  • Intern, Microblog Search, Bing Social (Microsoft), 2013 Summer
  • Intern, Data Integration, Yahoo!, 2010 Summer - 2011 Spring
  • Teaching Assistant, Department of Computer Science, Wright State University, 2011 Mar. - 2012 Jun.



Emotion Identification: identifying people's emotions (e.g., joy, sadness, anger, love, etc.) has implications in many fileds: personal retrospection, suicide prevention, workplace productivity, etc. Focuse on identifying people's emotions from their texts at the sentence level. Specifically:

  • Which features are effective for identifying emotions? (paper1, paper2)
  • How to improve the performance by combining the rule-based approach with the Machine Learning-based approach? (paper)
  • How to harness Twitter 'big data' to tackle this problem? (paper)
  • How to identify emotions from text in different domains, e.g., blogs, news, tweets, etc (in submission)

Social Media Analytics: large amount of social media data provides a great opportunity to analyze people's activities and opinions in real time. Specifically:

  • Keywords: NLTK, Twitter Streaming data, Cohen’s Kappa, LIWC, Gender identification, Sentiment analysis
  • Examine the characteristics of cursing activities on Twitter, involving the analysis of about 51 million tweets and about 14 million users. Explore a set of questions that have been recognized as crucial for understanding cursing in offline communications by prior studies, including the ubiquity, utility, and contextual dependencies of cursing. (paper)
  • Study the spectrum of Twitter users who participate in the on-line discussion of elections, and examine the predictive power of different user groups. Characterize users across both user participation dimensions such as engagement degree, tweet mode, and content type, and demographic dimensions such as political preference, etc. (paper)
  • Identify regrettable tweets from normal individual users. Explore the contents of a set of tweets deleted by sample normal users to understand the regrettable tweets. Develop classifiers to effectively distinguish such regrettable tweets from normal tweets. (paper)

Topic Specific Sentiment Analysis: Automatically extract sentiment expressions for a given target (e.g., movies, person) from a corpus of unlabeled tweets. Specifically (paper):

  • Recognize a diverse and richer set of sentiment-bearing expressions in tweets, including formal and slang words/phrases.
  • Assess the target-dependent polarity of each sentiment expression. The polarity of sentiment expression is determined by the nature of its target
  • Provide a novel formulation of assigning polarity to a sentiment expression as a constrained optimization problem over the tweet corpus
  • Example words and phrases: "want my money back", "must see", "luv"

Twitris+: 360-degree Social Media Analysis (demo): A Semantic Social Web application with real-time monitoring and multi-faceted analysis of social signals to provide insights and a framework for situational awareness, in-depth event analysis and coordination, emergency response aid, reputation management. My work:

  • Integrate my emotion identification work into the system
  • System performance optimization

Publications (Google Scholar)


  • Program Commitee: HT 2014 LBR+DC, HT 2014, WI 2014, WI 2013
  • External Reviewer: WebSci 2015, ICWSM 2015, ICWSM 2014, ICWSM 2013, ISMIS 2014, NLDB2014, IC3 2014, RAMSS (Workshop) 2013, WWW 2013, NLDB 2013, WWW 2012, ICWSM 2012, LSM 2012 (Workshop), ER 2012, ESWC 2011, AAAI 2010, ICWSM 2010


  • Lu Chen, Wenbo Wang, Amit Sheth. "Topic-specific Sentiment Extraction", U.S. Patent No. 20,140,358,523. 4 Dec. 2014.
  • Wenbo Wang, Lei Duan. "Temporal User Engagement Features", U.S. Patent No. 20,150,120,753. 30 Apr. 2015.




  • Email: [firstname][lastname]@gmail.com OR [firstname]@knoesis.org
  • LinkedIn: link