ajout du README

README

@@ -0,0 +1,13 @@
+Installation des fichiers supplémentaires pour NLTK depuis python
+
+import nltk
+nltk.download('punkt')
+nltk.download('wordnet')
+nltk.download('stopwords')
+nltk.download('averaged_perceptron_tagger')
+nltk.download('universal_tagset')
+nltk.download('sentiwordnet')
+
+Installation de Stanford CoreNLP
+
+Télécharger et décompresser https://nlp.stanford.edu/software/stanford-parser-full-2018-10-17.zip dans le dossier de travail

negation_conversion.py

@@ -15,10 +15,13 @@ sentences = ["This is not a test.",
              "We do not like washing dishes which lead to the decision of buying a dishwasher."
              ]
 
+# Source du fichier à télécharger pour Stanford CoreNLP
+# https://nlp.stanford.edu/software/stanford-parser-full-2018-10-17.zip
+
 from nltk.parse.corenlp import CoreNLPServer
 from nltk.parse.corenlp import CoreNLPParser
 set_negatives = set(['no','not','never'])
-# https://nlp.stanford.edu/software/stanford-parser-full-2018-10-17.zip
+
 
 def is_negative_tree(tree):
     lower_leaves = [x.lower() for x in tree.leaves()]
@@ -103,11 +106,11 @@ def convert_negated_words(sentence):
 
 
 if __name__ == '__main__':
-    #server = CoreNLPServer("/home/francois/stanford-corenlp-full-2018-10-05/stanford-corenlp-3.9.2.jar",
-    #                       "/home/francois/stanford-corenlp-full-2018-10-05/stanford-english-corenlp-2018-10-05-models.jar")
-    #server.start()
-    #parser = CoreNLPParser()
-    output_file = open("/home/francois/nlp_a2019_tp2/nlp_a2019_tp2/output_negative.txt","w")
+    server = CoreNLPServer("./stanford-corenlp-full-2018-10-05/stanford-corenlp-3.9.2.jar",
+                           "./stanford-corenlp-full-2018-10-05/stanford-english-corenlp-2018-10-05-models.jar")
+    server.start()
+    parser = CoreNLPParser()
+    output_file = open("output_negative.txt","w")
     for sent in sentences:
         print("\nS:", sent)
         output_file.write("S: "+sent)
@@ -116,4 +119,4 @@ if __name__ == '__main__':
         print("N:", converted)
         output_file.write("\nN: "+converted+"\n\n")
     output_file.close()
-    #server.stop()
+    server.stop()

output_negative.txt

@@ -0,0 +1,45 @@
+S: This is not a test.
+N: This is not NOT_a NOT_test .
+
+S: There is no flowery dialog, and time is not wasted.
+N: There is no NOT_flowery NOT_dialog , and time is not NOT_wasted .
+
+S: She did not promise to help him.
+N: She did not NOT_promise NOT_to NOT_help NOT_him .
+
+S: The King of France is not bald.
+N: The King of France is not NOT_bald .
+
+S: It is not so much a work of entertainment as it is unique study.
+N: It is not NOT_so much a work of entertainment as it is unique study .
+
+S: Mary did not complete the program but Nancy wrote the report.
+N: Mary did not NOT_complete NOT_the NOT_program but Nancy wrote the report .
+
+S: Not an accomplished dancer, he moved rather clumsily.
+N: Not NOT_an NOT_accomplished NOT_dancer , he moved rather clumsily .
+
+S: Not all participants liked this game.
+N: Not NOT_all NOT_participants liked this game .
+
+S: I do not think he is coming.
+N: I do not NOT_think NOT_he NOT_is NOT_coming .
+
+S: Mary did not give the solution to Paul.
+N: Mary did not NOT_give NOT_the NOT_solution NOT_to NOT_Paul .
+
+S: She claimed that Donald had not offered bribes to any official.
+N: She claimed that Donald had not NOT_offered NOT_bribes NOT_to NOT_any NOT_official .
+
+S: Not for the first time, he was surprised by this player.
+N: Not NOT_for NOT_the NOT_first NOT_time , he was surprised by this player .
+
+S: I would never do it even if I can.
+N: I would never NOT_do NOT_it even if I can .
+
+S: A decision is not expected until June.
+N: A decision is not NOT_expected NOT_until NOT_June .
+
+S: We do not like washing dishes which lead to the decision of buying a dishwasher.
+N: We do not NOT_like NOT_washing NOT_dishes which lead to the decision of buying a dishwasher .
+

sentiment_analysis.py

@@ -5,15 +5,6 @@ from sklearn.metrics import accuracy_score, recall_score, precision_score
 from scipy.sparse import csr_matrix, hstack
 import pandas as pd
 
-# installation
-# import nltk
-# nltk.download('punkt')
-# nltk.download('wordnet')
-# nltk.download('stopwords')
-# nltk.download('averaged_perceptron_tagger')
-# nltk.download('universal_tagset')
-# nltk.download('sentiwordnet')
-
 
 train_pos_reviews_fn = "./data/train-positive-t1.txt"
 train_neg_reviews_fn = "./data/train-negative-t1.txt"