| .. | ||
| Emoji_Distance.ipynb | ||
| Emoticon_Emoji_Assignment.ipynb | ||
| fastTextVectors.kv | ||
| naive_approach.ipynb | ||
| naive_approach.py | ||
| naiveApproachTest.ipynb | ||
| README.md | ||
| word2vec.model | ||
naive_approach
This directory contains the functions necessary to run the Naive Approach.
Prerequisites: # the file "emoji_descriptions_preprocessed.csv" has to be located in the specified folder ("../Tools") # pandas has to be installed
For testing, import naive_approach.py and execute the following commands:
-
prepareData(stem, lower)
preprocesses the emoji descriptions and returns a dictionary with the indexed emojis
parameters:
# stem: Apply stemming (default=True) # lower: Apply lowercasing (default=True) -
predict(sentence, lookup, emojis_to_consider, criteria, lang, embeddings, n=10, t=0.9)
evaluates an input sentence and returns a list of predicted emojis
parameters:
# sentence: Input sentence (required parameter) # lookup: dictionary with emoji data (return value of prepareData, required parameter) # emojis_to_consider: set of emojis to include in prediction, or "all" (default="all") # criteria: criteria to evaluate the values of the description - message matching. # options: "sum", "mean", "max_val", "threshold" (default: "threshold") # lang: language to use (default: "eng") # embeddings: word embeddings # options: "wordnet", "word2Vec", "fastText", default: "wordnet" # n: number of top ranked emojis to return (default=10) # t: threshold for the "threshold" criteria (default=0.9)