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| # 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) | ||||
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