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Course Project

The course project is to give the students hands-on experience on solving some novel text mining problems. The project thus emphasizes either research-oriented problems or "deliverables." It is preferred that the outcome of your project could be publishable, e.g., your (unique) solution to some (interesting/important/new) problems, or tangible, e.g., some kind of prototype system that can be demonstrated. Group work is strongly encouraged, but not required.

General steps

  • Pick a topic
  • Form a team
  • Survey related work
  • Write a project proposal
  • Work on the project
  • Write a report
  • Present the project

Your project will be graded based on the following required components:

  • Project proposal (20%)
    • State your motivation, research problem, and expected outcome of your course project.
    • Due in the end of 5th week of semester.
    • Discussion with instructor prior to deadline is encouraged.
    • Due in the in the end of 5th week.
  • Project presentation (40%)
    • 20 minutes presentation about what you have done for this course project. Format could be tailored according to the nature of the project, e.g., slides presentation and/or system demo.
    • Performance will be graded by both instructor and peer students (no self-grading).
  • Project report (40%)
    • Detail written report of your project.
    • Quality requirement is the same as research papers, i.e., in formal written English and rigorous paper format.
    • Due on the last week of course before project presentation.

An official rubric for the final report and rubric for the project presentation can be found here.

Note that you are required to use the provided templates for your project proposal and final report. See the Resources page for the template and example file.

Pick a topic

You can either pick from a list of sample topics provided by the instructor or choose your own topic. You are suggested to starting thinking about the topic for your course project from the first day of the class, and discuss it with your fellow students. This is a good way to identify opportunities for collaborations.

Leveraging existing resources is especially encouraged as it allows you to minimize the amount of work that you have to do and focus on developing truly your ideas.

When picking a topic, try to ask yourself the following questions:

  • What is exactly the (research) problem that you want to solve? Will it matter if nobody realizes this problem?
  • What kind of changes could your project make to the others?
  • Is there any existing alternative? If so, why do you still want to do it? How is your idea different from theirs? Would people appreciate about the difference?
  • What would be the major challenge(s) in this problem? Any specific background or resource you have to solve the identified problem?
  • What is the minimum goal to be achieved during this semester? (Try to drop everything non-essential and only keep the part that is truly novel.)
  • How do you plan to demonstrate that method to be developed is indeed solving the pain? Empirical experimentation and/or demo are required, unless you are doing a purely theoretic work.

Keep in mind, you are required to address the above questions in your project proposal and final report.

Form a team

You are encouraged to work with other students as a team. Teams may consist of up to three total students. Teamwork not only gives your some experience on working with others, but also allows you to work on a larger (presumably more important) topic.

Note that it is your responsibility to figure out how to contribute to your group project, so you will need to act proactively and in a timely manner if your group leader has not assigned a task to you. The instructor will believe all team members actively contribute to the project and the same grade will be applied to the group member (unless special treatment is required by the group members).

Survey related work

While choosing a topic, it is very important to be aware of whether the problem you would like to tackle has already been solved. If so, you may want to figure out where exactly your novelty is and whether novelty leads to any benefit to others. Your goal is to go beyond, rather than simply duplicate, the existing work. To minimize your effort, you are encouraged to leverage existing algorithms, toolkits, and other useful resources as much as possible. The instructor can also help you check related work. Please feel free to discuss your plan with the instructor before finalizing your proposal.

Write a project proposal

You are required to write a two-page proposal before you actually go in depth on a topic. In the proposal, you should address the following questions and include the names of all the team members as authors. The order among authors' names do not matter.

  • What is the problem identified in the project?
  • Why is this problem important?
  • Is there any related work? How different is your idea from theirs?
  • What techniques/algorithms will you use/develop to solve the problem?
  • How will you evaluate your work?
  • List your potential contributions of this work.

Intuitively, the proposal should read like the introduction part of a regular research paper. Briefly state the background/motivation, what has been done, what is missing, how do you plan to solve it, how do you plan to prove the usefulness of your method, and summarize your contribution(s).

Work on the project

You should leverage any existing tools or methods as much as possible. For example, consider using the Lucene toolkit for indexing and searching in a large text corpus; using Stanford NLP parser or OpenNLP toolkit for text analysis; using MALLET or WEKA for classification or clustering. There are also many tools available on the Internet. See the resources page for some useful pointers. Discuss any problems or issues with your teammates or classmates. If you need special support, please let the instructor know.

Consider documenting your work regularly. This way, you will already have a lot of things written down by the end of the semester. In addition, we strongly suggest using version control for your project! Nothing is more frustrating than losing a lot of your hard work, especially if it's close to a deadline.

Present the course project

At the end of the semester, each project team is expected to present their project in class. The purpose of this presentation is

  • Let you know about others' projects.
  • Give you some opportunity to practice presentation skills, which are very important for a successful career in both academia and industry.
  • Obtain some feedback from others about your project.

In general, the structure of your presentation should be prepared like a conference presentation. So it should touch all the following aspects (text in parenthesis states the instructor's expectation):

  • What is the background/motivation of your work? What research question will you address? (Learn how to attract public attention.)
  • Why is this problem important? (Learn to how persuade others.)
  • Is there any existing work? How novel is yours? (Learn how to sell your ideas.)
  • How did you solve this problem? (Learn how to deliver your solution.)
  • How good was your method? (Learn how to quantitatively/qualitatively evaluate your work.)
  • Any ideas for further improvement? (Learn how to look ahead.)

Think about how you can best present your work so as to make it as easy as possible for your audience to understand your main messages. Try to be concise, to the point. Pictures, illustrations, and examples are generally more effective than text for explaining your project. Try to show screen shots and/or plots of your experimental results. Watching some top conference presentations (e.g., KDD, SIGIR, ICML) on VideoLectures will be beneficial.

In order to be fair to all members in the same group, the instructor will randomly pick team members for question answering during the presentation.

Write a project report

You should write your report as if you were writing a regular conference paper. You should address the same questions as those you have addressed in the proposal and presentation, only with more details. Pay special attention to the challenges that you have solved and your detailed solutions. Basic sections to be included in the report should be the same as those in a conference paper, e.g., abstract, introduction, related work, method, experiment and conclusion. If you are developing a demo system or toolkit, your report should follow the format of a demo paper.

You are required to use LaTeX for your project report. See the Resources page for the template and example file. The project report must be at most ten pages with that format (no minimal requirement, as long as you feel it is sufficient to prove the merit of your work).

Sample topics for course project

The instructor has selected a set of representative course projects from former UIUC students. From this selection, you can get a broad spectrum of topics and a basic sense of what would be regarded as feasible topics for our course project. Note, their ideas nor solutions are not necessarily perfect. You are encouraged to come up with better ideas and solutions.

Warning, please do not disclose or distribute the following project reports outside this class; their original authors hold all the reserved copyrights!

  • Text mining

    • "SideEffectPTM", an Unsupervised Topic Model to Mine Adverse Drug Reactions from Health Forums.
    • "Better Hotel Reviews Report", improve hotel ratings by giving users the ability to remove unwanted reviews from consideration.
    • "Buy || !Buy: To Buy Or Not?", help consumers make intelligent buying decisions based on customer reviews posted on e-commerce websites.
    • "Understand Function of Location Entity", detect location related entities within a city from microblogs.
    • "KickUpper", a tool for making better crowdfunding projects.
    • "Article Topic Sentiment", perform like/dislike for an individual topic in an article.
    • "AskSnoo", verify social content against an established information source.
    • "Re:Search", identify professors to work with.
    • "Check the news", verify the accuracy and validity of different news sources regarding financial status of companies.

  • Visualization

  • Social Network

    • "Find-A-Friend", finds potential best friend by analyzing the online profile programmatically.
    • "BuddyMeet", a platform for students in U of I to explore and initiate events/activities.
    • "Destination Finder", destination recommendation based on social suggestions.

  • Developments

    • "Review Union", ReviewUnion mobile application which fetches the the product information from Walmart and Amazon to help customers compare product information from different website and also have a quick reference to review and feedback of that product.

Topics proposed by the instructor

  • Automatic tutor for English writing: Good English writing skill is always a significant challenge for a non-English speaker. And it is also even stressful for a native speaker in specific scenarios, e.g., formal scientific writing. This project aims to develop automatic tools based on language models to beautify an amateur's English writings. For example, language models trained on twitter data would make an ordinary user's tweet looks more like being written by a experienced twitter user; and language models trained on scientific publications would make an amateur's paper read like an expert's work.

  • Spatial temporal analysis of opinions: Social opinions provide a gold mine for researchers to understand the explore public's opinion towards a specific entity, e.g., products and celebrities, or a service, e.g., hotels and restaurants. This project aims to extend an existing aspect-based opinion mining system, ReviewMiner, for supporting spatial-temporal analysis of opinions. Specifically, we want to visualize the opinions: display the temporal dynamics of opinion across different entities (e.g., from twitter stream or reviews), render the opinions on a map, and support user interaction with such spatial-temporal analysis of opinions.

  • Social influence v.s. homophily: Users in Yelp write reviews about businesses and make friends who share similar tastes and preferences. However it is unknown whether users become friends because they visited the same restaurants before (i.e., homophily); or they visited the same restaurants because they were friend (i.e., )influence. Distinguishing these two factors are very important for social network based recommendations.

  • Query intent classification: Current product search system can only support simply keyword search, e.g., "canon 5d3". It is preferred if the system can support some simple semantic search, e.g., "cheap digit camera with high resolution." The system should be able to correctly map the specifiers of "cheap" and "high" to corresponding aspects of the product, e.g., price and image quality, and return all the results matching such criteria. One can imagine this as a translation process and opinionated review text documents provide nice resource to estimate such translation model.

Presentation Schedule

Order Name Project Title
1 Wasi Uddin Ahmad, Md Masudur Rahman Language Model based Privacy Protection in Personalized Web Search
2 Matt Hawthorn, Nikhil Mascarenhas, Shannon Mitchell Clustering Documents in Topic Space: Worth the Effort?
3 Jason Lewris, Don Chesworth Movie Box Office Prediction Engine
4 Katherine Schinkel, Marcus Rosti The Automated Script Reviewer
5 Adam Rosenberg, Leandra Irvine, Gus Logsdon Automated Tweet Content Quality Assessment
6 Ziding Liu, Fanjia Kong, Haoyu Li Automated Essay Scoring
7 Hope McIntyre, Brian Sachtjen, Nick Venuti Contextual Shifts of Political Buzzwords
8 William Middleton, Jason Adams, Ian Clark, Antoine Rigaut Identifying Political Leaning in the News
9 Paul Cherian, Aditya Bindra, Benjamin Haines Sentiment Analysis to Predict S&P 500 Behavior
10 Deyuan Guo, Elaheh Sadredini Rule-based POS Tagging on the Micron’s Automata Processor
11 Lihua Cai, Yue Hu Understand The Semantics of Mobile Phone Text Messages
12 Dinesh Surapaneni Peter answers: Artificially intelligent computer system to solve multiple choice question in scientific field
13 Jinlong Feng, Renqin Cai Modeling Correspondence for Text Retrieval
14 Anish Tondwalkar, Juan Pablo Arrivillaga, Prateek Agrawal Automated Tutor for English Writing
15 Zhengkun Li, Hao Wu, Ning Yu Faculty Homepage Crawling and Research Interests Analysis
16 Huazheng Wang, Yuxiang Xiang, Yuting Wang Text based Recommendation System in Online Setting