Twitter is an excellent source of data for companies and organizations. Millions of conversations, intentions, claims, sales opportunities, product improvements, etc., are on Twitter.
To obtain the data required, there are two options. The first is to use the Twitter API, but this requires programming skills.
The other option is to “scrape” the tweets that are required. Scraping is a computational tool that simulates being a human and navigates Twitter to get the data we need.
The easiest and no-code option is to use a Twitter scraping provider, and there are several here. Here we suggest 3 apps we have used successfully and the link to see how to do it.
- PhantomBuster.com: https://phantombuster.com/blog/guides/how-to-scrape-twitter-2fe3zCX8siWf8TLPN3DRIN
- Octoparse.com: https://www.octoparse.com/tutorial-7/scrape-tweets-from-twitter
Analyzing tweets on Deep Talk
You will get an archive .CSV once you have scraped the tweets with one of the above scraping providers. The .CSV file contains several columns; some of the most common ones are:
Once you have the .CSV file, it is as simple as uploading the file to Deep Talk Platform by creating a “General text Data” model and following the steps. It is important that you tell us which column contains the tweets you want to analyze and which contains the date on which the tweet was created.
It’s that easy!
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