Teaching Assistant Experiences
Text Data Analysis (Graduate lecture; Instructor: Dr. Zhicong Chen)
- Feedbacks from my students:
- “I have to give Yixiao a perfect score on the evaluation! She is truly responsible!”
- “Yixiao explains coding in a way that’s easy to understand, and she clearly explains the principles and objectives of each step. That’s exactly what I was looking for!”
- “Yixiao totally cleared up all my confusion in class – she’s really sharp!”
Planning and Creative Strategy (Graduate seminar; Instructor: Dr. Zhihao Ma)
- Feedbacks from my students:
- “Yixiao lectures very clearly. We’re lucky to have her!”
- “Yixiao always clearly and precisely points out the issues in our project, and I find her presence reassuring.”
- “I think Yixiao is really talented and takes good command of coding!”
Advertising Strategy & Creation (Undergraduate lecture; Instructor: Dr. Zhihao Ma)
- Feedbacks from my students:
- “Yixiao is excellent! Thanks to her for sharing the word frequency analysis code and research methods, and I feel that her academics are very strong!”
- “Yixiao is really nice! She answers any questions in a timely and detailed manner, and will remind us of details and make encouragement when assigning tasks. She is perfect!”
- “Yixiao is beyond admiration.”
Teaching Resources
As a teaching assistant, I developed numerous codes for my students, explaining their functionality and demonstrating how to run them in class. Below are some representative utilities I offer:
Natural Language Processing (NLP)
-
Narrative Structure: Based on the insights provided by Toubia et al. (2021), I developed a code to measure the speed, volume, and circuitousness of narratives. -> Narrative_Structure.ipynb
-
Word Embedding Model: I explained the principles and functions of the Word Embedding model, demonstrated how to train or call the model, and used PCA to reduce the semantic space to two dimensions for visualizing the relative positions of word vectors. -> Word_Embedding.ipynb or Word_Embedding_Update.ipynb
-
LDA Model: I developed a classic LDA topic analysis algorithm and created an interactive visualization to present the results. -> LDA.ipynb
-
Bag of Words and TF-IDF: Explaining the principles and functions of the Bag-of-Words model and the TF-IDF method, I demonstrated how to apply them when processing text data. Additionally, I also introduced two distinct approaches for preprocessing English and Chinese corpora. -> BoW_Tfidf_eng.ipynb for English corpora and BoW_Tfidf_chn.ipynb for Chinese corpora
Social Media Data Sampling and Crawling
- Random Digit Search (RDS): Following the approach of Zhu et al. (2011), an estimation is made for the actual user population of NetEase Cloud Music. -> music163_RDS.ipynb
Other Resources to Share
Outside of my work as a teaching assistant, I will periodically share methodological insights I have acquired during my research. My aim is to actively engage with other individuals interested in computational social science and quantitative research. Below are some items that may be useful:
- Using R language to conduct Bootstrapping method
- Performing ANOVA analysis to calculate effect size on panel data across multiple time points using R language