A colleague asks whether replies to a product announcement are supportive. You find “Great, another accessory replacement” and cannot tell whether it is praise or sarcasm. Establish what each comment addresses before calculating sentiment proportions.

Read the promise in the original post

Retrieve the source by tweet_id and check whether replies concern features, price or timing. Define a narrow question, such as concerns about accessory compatibility. If the source is unavailable, leave that gap visible rather than inventing what a pronoun in a reply refers to.

Label ordinary and latest-comment samples

The workflow binds both comment and latest-comment lookups. Keep each entry point, lookup time and pagination coverage. Deduplicate by available comment identifiers before interpretation. Recent comments are not necessarily representative, and prominent ones do not automatically express a majority view.

Preserve more than attitude

Manually label a small set as experience, pre-purchase question, retelling, joke or unclear. Then record the specific concern and supporting context. “Will my old accessory work?” is a question, not necessarily negative feedback. If AI assists, provide the text and label definitions and allow uncertainty rather than requiring every entry to be positive or negative.

Produce questions the team can answer

A feedback table can include source, concern, attitude interpretation, reason and information to clarify. Say that some sampled replies ask about compatibility instead of claiming customers broadly reject the product. Skip sentiment percentages when the evidence does not support them. The deliverable is a traceable set of questions for the next explanation, not an automatic sentiment score or completed fact-check.

Requests used in this project

Request URL and parameters

GET https://api.trmesh.com/openapi/api/v1/twitter/web/fetch_post_comments

Parameter Type Required Meaning
tweet_id string Yes Tweet ID
cursor string No Cursor

Run the Python example

Install requests and set TRMESH_API_TOKEN in your environment. Replace the sample identifiers with the public object you intend to inspect. Example IDs may no longer be available.

import os
import requests

# Read your own developer key from the server environment.
url = "https://api.trmesh.com/openapi/api/v1/twitter/web/fetch_post_comments"
params = {
    "tweet_id": "1835124037934367098"
}
response = requests.get(
    url, params=params, timeout=30,
    headers={"Authorization": f"Bearer {os.environ['TRMESH_API_TOKEN']}"},
)
response.raise_for_status()
payload = response.json()
print(payload)  # Inspect the actual response before mapping fields

Endpoint documentation · Free profile tool

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