About me

My name is Lin gong and I am a fourth year graduate student in the Department of Computer Science, Univeristy of Virginia, under the guidence of Dr.Hongning Wang. My research interest lies in data mining and machine learning, espcecilly in sentiment analysis and social influence analysis. I would like to explore how social network would influence human behaviors, e.g., the sentimental experssions, user preferences. I am currently working on personalized sentiment classification by leveraging social networks.

Contact Info.

Email: lg5bt@virginia.edu
Website: www.cs.virginia.edu/~lg5bt/
Address: 85 Engineer's Way, Rice Hall, Charlottesville, VA 22903


Dec, 2016

Our paper got accepted by WWW 2017.

May, 2016

Our paper got accepted by ACL 2016.

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!

April, 2016

I participated the CRA-W 2016, San Diego and presented my work in the poster section.

Oct, 2015

I participated the Grace Hopper 2015 in Houston, Texas.


Web Design Trends

Multi-task Model Adaptation for Personalized Sentiment Classification

Guided by Hongning Wang    Jan, 2016 - 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. In our proposed solution, 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 of opinions from individuals.

Collaborative Model Adaptation for Personalized Opinion Analysis

Guided by Hongning Wang    June, 2015 - Dec, 2015

Humans' opinions are variable: the same opinions can be expressed in various ways and the same expression can carry distinct sentimental polarities. Thus, a global sentiment classification model is incompetent to distinguish such diverse opinions while personalized models are limited to sparsity . we propose to build personalized sentiment classification models via adapting a global model to individual users in a collaborative manner. The learned sentiment models are shared across neighboring users to conquer data sparsity challenge.

Network-based Sentiment Analysis for Online Reviews

Guided by Hongning Wang    Nov, 2014 - May, 2015

Supervised learning methods suffer in performance with limited amount of labeled data. However, labeling data is quite expensive and requires expertise. By constructing network and sharing information among reviews, we can better propagate the sentiment in reviews with limited amount of labeled data. We utilized learning to rank technique to further purify the neighborhood by extracting more representative features from reviews, i.e., POS tags, aspect vector, topic distribution. Then, we adopted transductive learning method to propagate the sentiment labels among documents to boost the performance.

Analysis of Amazon User Reviews and Prices.

Cooperated with Lingjie Zhang, Bo Man    Sep, 2014-Dec, 2014

User reviews are gaining more and more attention. Retailors need reveiws to get feedback while users need reviews to acquire more information. On one side, retailors may perform comerical campaigns based on user's feedback, i.e., sales. On the other side, users' phurchase decision may reply on others' reviews laregely with more data available. Thus, we were curious are there any relationships between user reviews and prices. Read our slides to get a quick view of what we have done and more deatails are described in our report.


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

[2] 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]

[3] Gong Lin, Wang Chenghua, You Wenjue, Wan Yunqiang. The Design of Passive Intermodulation Test System Applied in LTE 2600, 2012 International Conference of Electrical and Electronics Engineering, published.[pdf]


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