About me

My name is Lin Gong and I graduated from the Department of Computer Science, Univeristy of Virginia in July, 2019. My research interest lies in data mining and machine learning, especially in sentiment analysis and social network analysis. I would like to explore how user-generated data such as textual reviews and social connections help better understand their intents.

Contact Info:

Email: lg5bt@virginia.edu
Website: www.cs.virginia.edu/~lg5bt/


Oct, 2019

Our paper got accepted by WSDM 2020 in Houston, United States.

July, 2019

I graduated from UVA and joined Walmart Labs in Sunnyvale, California.

Oct, 2018

Our paper got accepted by WSDM 2019 in Melbourne, Australia.

Aug, 2018

I participated KDD 2018 in London, United Kindom.

May, 2018

Our paper got accepted by KDD 2018 in London, United Kingdom.

Oct, 2017

I participated the Grace Hopper 2017 in Orlando, Florida.

May, 2017

We are the winners of Yelp Data Challenge Round Eight!

Dec, 2016

Our paper got accepted by WWW 2017 in Perth, Australia.

May, 2016

Our paper got accepted by ACL 2016 in Berlin, German.

April, 2016

Our proposal "Predicting Community-Level Criminal Behaviors by Estimating Human Attitudes from Social Media" was selected for the Presidential Fellowship in Data Science!


Joint Network Embedding and Topic Embedding

Guided by Hongning Wang    May, 2018 - Jan, 2019

Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users’ social connections and text content, to facilitate joint modeling of different modalities, via a proba- bilistic generative framework.

Web Design Trends

Holistic User Behavior Modeling

Guided by Hongning Wang    Jan, 2017 - Jan, 2018

We focus on self-consistence across multiple modalities of user-generated data to model user intents. A probabilistic generative model is developed to integrate two companion learning tasks of opinionated content modeling and social network structure modeling for users. Individual users are modeled as a mixture over the instances of paired learning tasks and the tasks are clustered to capture the homogeneity among users.

Web Design Trends

Clustered Model Adaptation for Personalized Sentiments

Guided by Hongning Wang    July, 2016 - Dec, 2016

We propose to capture humans’ variable and idiosyncratic sentiment via building personalized sentiment classification models at a group level. We formalize personalized sentiment classification as a multi-task learning problem. In particular, to exploit the clustering property of users’ opinions, we impose a non-parametric Dirichlet Process prior over the personalized models, in which group members share the same customized sentiment model adapted from a global classifier.

Web Design Trends

Multi-task Model Adaptation for Personalized Sentiment Classification

Guided by Hongning Wang    Oct, 2015 - June, 2016

Motivated by the findings in social science that people’s opinions are diverse and variable while together they are shaped by evolving social norms, we perform personalized sentiment classification via shared model adaptation over time. A global sentiment model is constantly updated to capture the homogeneity in which users express opinions, while personalized models are simultaneously adapted from the global model to recognize the heterogeneity.


[0] Dissertation. Lin Gong. Insights: From Social Psychology to Computational User Modeling.[pdf][link]

[1] Lin Gong, Lu Lin, Weihao Song and Hongning Wang. JNET: Learning User Representations via Joint Network Embedding and Topic Embedding. The 13th ACM International Conference on Web Search and Data Mining(WSDM 2020), 2020.[pdf][git][data]

[2] Lu Lin, Lin Gong and Hongning Wang. Learning Personalized Topical Compositions with Item Response Theory. The 12th ACM International Conference on Web Search and Data Mining(WSDM 2019), 2019.[pdf]

[3] Lin Gong and Hongning Wang. When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data. The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), p1455-1464, 2018.[pdf][git][data][video]

[4] Lin Gong, Benjamin Haines and Hongning Wang. Clustered Model Adaptation for Personalized Sentiment Analysis. The 26th International World Wide Web Conference (WWW 2017), p937-946, 2017. [pdf][code][documentation]

[5] Lin Gong, Mohammad Al Boni and Hongning Wang. Modeling Social Norms Evolution for Personalized Sentiment Classification. The 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016), p855-865, 2016. [pdf][code][documentation]


The modeling method of parallel power amplifier based on improved BP neural network. (China, No. 201210443798.8)