The final path specified is the location the files are copied to (in this case, the working directory). On line 9, we list several files to copy and then specify where to copy them. COPY: Copy a file or files into the Docker container.If the working directory does not already exist, it is created by Docker. WORKDIR: Sets the working directory for the application.LABEL: Defines the metadata for our Dockerfile, including information about the version, description, and maintainers.In this context, we use Node Version 12.18.3. FROM: Defines the image we will use as the basis for our container.After this, pending a successful response from both the Twitter and Google NLP APIs, the data is structured and returned to the frontend, where it is then formatted and passed to various React components. When a user submits a valid tweet URL, the tweet ID is parsed from the URL and sent to the backend, where we then request tweet and user information from the Twitter V2 API, parse the response from Twitter to extract the tweet contents, and send that contents to the Google NLP API for sentiment analysis. To accomplish this task, we decided to use an express.js backend with one endpoint that accepts the Tweet ID as a parameter ( POST /analyze/:tweet_id). First, we needed to build a backend that retrieved data from the Twitter V2 API, sent it to the Google NLP API, and structured the responses for easy frontend consumption.
After landing on a final design (shown at the very bottom of the embedded Figma document), we structured the work into two buckets: frontend and backend.
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