diff --git a/Presentations/final/Emoji Prediction for Text Messages - Final Presentation.pdf b/Presentations/final/Emoji Prediction for Text Messages - Final Presentation.pdf new file mode 100644 index 0000000..e6e385d Binary files /dev/null and b/Presentations/final/Emoji Prediction for Text Messages - Final Presentation.pdf differ diff --git a/Project/naive_approach/README.md b/Project/naive_approach/README.md new file mode 100644 index 0000000..e24455e --- /dev/null +++ b/Project/naive_approach/README.md @@ -0,0 +1,29 @@ +# 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: + +1. 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) + +2. 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) \ No newline at end of file