Methodology
Methodology
The code is available to browse on the GitHub repository but below includes very brief information about the main features of the project.
I used Python unless otherwise specified.
Google Trends
I used Google’s pytrends to collect the data using category 185 “Fashion & Style” from official category list.
Date from the beginning of 2019 until July 2022.
File is fashun_week_trends.py
from pytrends.request import TrendReq as UTrendReq
...
pytrends.build_payload(values, cat=185, geo="GB-ENG", timeframe=f'2019-01-01 {date.today()}',)
Twitter API
I used Twitter’s API package tweepy only obtaining single tweets - excluding replies, retweets, and links.
File is chirp_tweets.py
tweet_search = "lang:en -filter:links -filter:replies -filter:retweets"
If you are interested in the text cleaning, see bath.py.
I used VADER for sentiment analysis, singing.py as it is a rule-based method trained w/ tweets.
RShiny
With R I used the shiny library to create a basic set of plots and interactive elements for users to explore.
This app is developed based on the time-trends, sentiment, and correlation methods for users to make their own investigations.
See the app here: RShiny.
End of page.