International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 06 | June 2022
p-ISSN: 2395-0072
www.irjet.net
Optimizing Wordle Helper using Datasets and Commonly used words. Sarosh Dandoti1 ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Wordle is a very simple game that went viral
were selected. The data should be accurate and should not contain spelling mistakes. These words range from simple words like ‘hello’ to unique words like ‘vraic’. This is an interesting part where one must find out which words are important and which ones are not. The Data must be converted into lowercase letters as computers consider ‘A’ and ‘a’ with unique ASCII values. Words that are very unique like mentioned above are sometimes not even in the Wordle Dictionary because, the dictionary is made up of commonly used words and since the player does not know the dictionary of words, it's difficult to find a word that fits appropriately as there may be multiple words that may fit into certain criteria. For resolving this we need to find the most commonly used words in the day-to-day language and the letter which are most occur in our 5 letter words dataset.
at the start of 2022 and yet the game is very difficult without sufficient knowledge of vocabulary. Such simple 5minute brain teasers like wordle, hangman look very simple at sight, but there is a deeper understanding of these games and figuring out if there’s a statistical and informed approach one can make to maximize the win states. The game looks very random at the start, there are methods and initial guesses which eliminate doubts and restrict your search. Key Words: Wordle, Python, Programming, Logic, Datasets, Brain-Teasers.
1. INTRODUCTION In Wordle, the player has 6 chances to guess a 5 letter word. These configurations can be changed as required hence changing the difficulty of the entire game. We have implemented an AI which suggests the best suitable word to maximize our win and get it at an earlier stage. The player gets more points if the correct word is guessed at a second chance than the last chance. You have to enter a letter word, and if the letters in that word lie in the goal word they turn yellow, if they turn green, it means that the position and word are correct, and if it turns grey it means that letter is not present in the goal word. This looks pretty simple when you play it yet one gets stuck due to the lack of vocabulary or simple the pressure of having fewer chances each time you play. To make this even more pressuring, the original wordle gives only 1 word per day. It also has a hard mode, which then forces you to use those letters which were green again on the position for the next guess, for example, if you hit the green letter ‘A’ on the first position, you have to guess all the next words starting with ‘A’. similarly, if you hit let's say, ‘W’ in the middle, you have to use ‘A’ and ‘W’ both and then think of a word. This makes the game very difficult but the solution is guaranteed in lesser steps as there will be very few words matching those criteria. There may be multiple same letters to confuse the player. For example, if the goal word is ‘sweet’ and if the player hits ‘e’ at one position he may never try ‘e’ again assuming that the letter does not occur again in the words and hence losses the game.
Here is a word cloud showing the most unique and least used words from the dataset.
These words even though are correct and identified by all the dictionaries in the world, are not used in day-to-day conversations and hence not recognized by the wordle dictionary. And here are just s few most commonly occurring words in the dataset.
2.1 Data To create our Wordle Helper we need lots of data containing 5 letter words. The dataset is an oxford dictionary words dataset from which only 5 letter words
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