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