Mentor Areas
- Social Influence
- Word of Mouth
- Natural Language Processing
- Consumer Behavior
- Viral Marketing
Description:
Why do some songs, books and movies catch on and become popular while others fail? Why do some online articles suck us in and get lots of engagement while others don’t? We’re interested in using natural language processing, machine learning, and automated textual analysis to help answer these questions and related questions.
Ongoing projects involve analyzing song lyrics to predict Billboard rankings, analyzing movies scripts to plot the emotional arc of narratives and predict ratings and ticket sales, and analyzing online content to understand why certain articles get longer versus shorter reads. Students will work with Professor Jonah Berger and potentially some graduate students in our group. Ideal applicants will have strong programming skills, be highly motivated, and able to work independently as well as within a team.
Preferred Qualifications
While not required, ideal candidate will have some experience with at least some of the following:
- Experience programming in Python and R, especially with processing large amounts of text data.
- Experience in one or more of the following packages: Pandas, seaborn, NLTK, spaCy, numpy, scipy, scikit-learn, and statsmodels or their R counterparts (dplyr, ggplot, tidytext, etc.).
- Coursework in one or more of the following, or similar courses: statistics (STAT 417, 476), machine learning (CIS 519, 520, 521), computational linguistics (CIS 530), linguistics (LING 449).
- Bonus if you have: Experience with jupyter notebooks, for prototyping, exploratory data analysis, and reporting; experience in sentiment analysis and/or automated assessment of text readability/quality; bash scripting (e.g., for computing on Wharton’s High Performance Computing Cluster); Git for version control
Project Website
Learn more about the researcher and/or the project here. Faculty Profile
Details:
Preferred Student Year
Second-Year, Junior, Senior
Academic Term
Fall, Spring, Summer
I prefer to have students start during the above term(s).Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
No
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
No
Yes indicates that faculty are open to hiring work-study-eligible students.