Research Assistant (time-limited assignment)
The primary responsibilities of the Research Assistant will be to extract data from Twitter and analyze it using Natural Language Processing (NLP) techniques.
The Research Assistant will be responsible for collecting, cleaning, and analyzing large amounts of data from Twitter, and will work closely with the research team to ensure that data collection and analysis is conducted in a timely and accurate manner.
WHAT YOU WILL DO
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- Collecting and cleaning data from Twitter using various tools and software.
- Analyzing and interpreting Twitter data using NLP techniques.
- Collaborating with the research team to develop research questions and hypotheses.
- Creating and maintaining databases of collected Twitter data.
- Preparing reports and presentations on findings from Twitter data analysis.
- Assisting with other research-related tasks as needed.
YOUR TEAM WILL INCLUDE
N / A
WHAT EDUCATION AND SKILLS YOU WILL NEED
- Master's degree in a related field, such as Computer Science, Data Science, or Linguistics
- Strong proficiency in programming languages such as Python or R
- Familiarity with NLP techniques, tools, and software such as NLTK, spaCy, or Gensim
- Must have experience working with large datasets and databases
- Must have strong analytical and problem-solving skills
- Must have excellent communication and interpersonal skills
- Ability to work independently and as part of a team
- Must have strong attention to detail and organizational skills
HOW AND WHERE YOU WILL WORK
All Babson College faculty and staff authorized to work on campus must be fully vaccinated against COVID-19, including any appropriate boosters.
Proof of vaccination upon hire is required or within 21 days of becoming eligible for a booster. Contact Human Resources for medical accommodation or religious exemption requests.
ADDITIONAL SKILLS YOU MAY HAVE
- Experience with social media data collection and analysis
- Familiarity with machine learning techniques and algorithms
- Knowledge of statistical analysis methods
PDN-99210bc2-684e-428d-a740-0b24d31e9a62
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