Sometimes we waste a lot of time to decide which movie to watch especially when we are with friends or family.
At the beginning you enter the name of 3 movies you like, the workflow searches each movieās name in Metacritic website and Finds name of the critics who gave this film a score above 90.
It then opens the page related to each of the critics and extracts the name of the movies that these critics have given a score of 100.
Finally it counts the number of repetitions of each movie in the list and gives you top 5 with name of director, genre, runtime, year of production and link to review of that movie on Roger Ebertās website.
It may have some bugs and be a little slow.
First of all please go to Metacritic website and search your movies and copy the name and production year from there (this is because Some movies, such as Downfall, have different production year on Metacritic website and some movies like Once Upon A Timeā¦In Hollywood, have different writing modes)
If your internet speed is slow, it may take a while to see the result (15 - 45 min in three-movie version). To change the accuracy and execution time you can adjust the nodes specified in the red boxes or if you are in a hurry you can cancel the execution of the node inside the blue box and see the result in previous node first view.
GL,
Mehrdad
P.S. Thanks @qqilihq for Selenium nodes and free trial.
O-M-G! What a wonderful idea! Before I dive deeper why I love this workflow, hereās the list that KNIME came up with for me (based on āone movie modeā choosing āThe Godfatherā as movie)
What I find quite surprising is that there quite a lot of movies I didnāt even know. Itās also a good mixture of classics and newer movies (thereās Avatar but thereās also On the Waterfront from the mid 50s)
I thought it would probably a lot of the usual suspects and maybe that would have been the case if I ran the ā3 movies modeā.
Amazing job @mehrdad_bgh for this workflow (and of course to @qqilihq for the SE Nodes).
I immediately thought of some other use cases that impliment a very similar logic and could strongly benefit from this share:
An Amazon booklist: could also be limited to non-fiction books
A list of people (LinkedIn profiles, Twitter accounts) to connect with
A list of hotels you liked and others as well
Basically everything that gets a review or something like a like could be interesting here.
Once again: fantastic job! Now off to the TV screen (my Prime Video account needs to do some purchases )
Hi @kowisoft , Thank you for your kind reply. Iām happy you like it! actually I had started similar workflow for books but I didnāt have enough free time to finish it. LinkedIn idea is great, I really need more connections . Soon Iāll create a topic and ask you guys there what features do we need for our LinkedIn add connections bot.
Br, Mehrdad
Well this is already pretty easily done. I have created a LinkedIn group scraper (so to say) that scrapes members from focus groups and exports them to a table (see this post here)
The things left to do there is use that table, loop through each line (maybe also with a randomized delay) and connect to the people you have previously filtered out as āinterestingā.
If you want, I can also share what I created using the SE Nodes.
