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DTSentiment.py
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### NBSentiment
### Author: Amy Olex
### 11/13/17
### This program takes in two csv files containing text to be classified as positive or negative sentiment.
### The first file contains data for training and testing, the second contains data to be classified.
###
import string
import numpy
from nltk.classify import DecisionTreeClassifier
import nltk.classify.util
import nltk
import math
import csv
import argparse
import random
from collections import Counter
## Formats a string for input in the NB Classifier by converting all to lowercase and removing al punctuation.
# @author Amy Olex
# @param sent The string to be formatted.
# @param stopwords A list of stopwords to be removed. Default is None.
# @return A dictionary of each word as the key and True as the value.
from sklearn.model_selection import StratifiedKFold
def format_sentence(sent, stopwords=None):
# convert to lowercase
sent = sent.translate(str.maketrans("", "", string.punctuation)).lower()
#remove stopwords
if stopwords is not None:
com_list = sent.split()
filtered_words = []
for word in com_list:
if word not in stopwords:
filtered_words.append(word)
sent = ' '.join(filtered_words)
return({word: True for word in nltk.word_tokenize(sent)})
#####
## End Function
#####
if __name__ == "__main__":
## Parse input arguments
parser = argparse.ArgumentParser(description='Train a NB Sentiment Classifier')
parser.add_argument('-i', metavar='inputfile', type=str, help='path to the input csv file for training and testing.', required=True)
parser.add_argument('-c', metavar='toclassify', type=str, help='path to file with entries to classify.', required=False, default=None)
parser.add_argument('-s', metavar='stopwords', type=str, help='path to stopwords file', required=True)
parser.add_argument('-p', metavar='posratings', type=str, help='a list of positive ratings as strings', required=False, default=['4','5'])
parser.add_argument('-n', metavar='negratings', type=str, help='a list of negative ratings as strings', required=False, default=['1','2'])
parser.add_argument('-z', metavar='iterations', type=str, help='the number of times to repeat the classifier training', required=False, default=2)
parser.add_argument('-d', metavar='domain', type=str, help='a file with text from a different domain.', required=False, default = None)
args = parser.parse_args()
## Import csv file
my_list = []
with open(args.i) as commentfile:
reader = csv.DictReader(commentfile)
for row in reader:
my_list.append({'comment': row['comment'], 'rating': row['rating']})
## Parse and convert positive and negative examples.
pos_list=[]
neg_list=[]
for c in my_list:
tmp_com = c['comment']
tmp_rating = c['rating']
#remove stop words
with open(args.s) as raw:
stopwords = raw.read().translate(str.maketrans("", "", string.punctuation)).splitlines()
if tmp_rating in args.n:
neg_list.append((format_sentence(tmp_com, stopwords), 0))
if tmp_rating in args.p:
pos_list.append((format_sentence(tmp_com, stopwords), 1))
seed = 123
numpy.random.seed(seed)
print("Total Negative Instances:" + str(len(neg_list)) + "\nTotal Positive Instances:" + str(len(pos_list)))
negcutoff = math.floor(len(neg_list) * 1)
poscutoff = math.floor(len(pos_list) * 1)
neg_idx_train = sorted(random.sample(range(len(neg_list)), negcutoff))
neg_train = [neg_list[i] for i in neg_idx_train]
pos_idx_train = sorted(random.sample(range(len(pos_list)), poscutoff))
pos_train = [pos_list[i] for i in pos_idx_train]
dataset = neg_train + pos_train
X = [x[0] for x in dataset]
Y = [x[1] for x in dataset]
kfold = StratifiedKFold(n_splits=int(args.z), shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.split(X, Y):
# print(dataset[train[0]])
train_data = []
for i in range(len(train)):
train_data.append(dataset[train[i]])
test_data = []
for i in range(len(test)):
test_data.append(dataset[test[i]])
model = DecisionTreeClassifier.train(train_data)
scores = nltk.classify.util.accuracy(model, test_data)
print("{}%".format(scores * 100))
cvscores.append(scores * 100)
# plot_model(model, to_file='model.png')
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores), numpy.std(cvscores)))
### create training and test sets
## set the cutoffs
# negcutoff = math.floor(len(neg_list)*3/4)
# poscutoff = math.floor(len(pos_list)*3/4)
#
# top10list = []
# avgAccuracy = 0
# for z in range(int(args.z)):
# #train = neg_list[:negcutoff] + pos_list[:poscutoff]
# #test = neg_list[negcutoff:] + pos_list[poscutoff:]
# neg_idx_train = sorted(random.sample(range(len(neg_list)), negcutoff))
# neg_train = [neg_list[i] for i in neg_idx_train]
#
# neg_idx_test = set(range(len(neg_list))) - set(neg_idx_train)
# neg_test = [neg_list[i] for i in neg_idx_test]
#
#
# pos_idx_train = sorted(random.sample(range(len(pos_list)), poscutoff))
# pos_train = [pos_list[i] for i in pos_idx_train]
#
# pos_idx_test = set(range(len(pos_list))) - set(pos_idx_train)
# pos_test = [pos_list[i] for i in pos_idx_test]
#
# train = neg_train + pos_train
# test = neg_test + pos_test
# print('Training on %d instances, testing on %d instances' % (len(train), len(test)))
#
# classifier = DecisionTreeClassifier.train(train)
# accuracy = nltk.classify.util.accuracy(classifier, test)
# avgAccuracy = avgAccuracy + accuracy
# print('Classifier accuracy:', accuracy)
#
if args.c is not None:
model = DecisionTreeClassifier.train(dataset)
with open(args.c) as file:
toclass = file.readlines()
for sent in toclass:
print(str(model.classify(format_sentence(sent)))+" :: "+sent)
#
# ### Count the occurences of each word that appeared in the top 10 over the 20 runs.
# print("Average Accuracy: "+ str(avgAccuracy/int(args.z)))
# my_counts = Counter(top10list)
# print(my_counts)
if args.d is not None:
domain_list = []
with open(args.d) as domainfile:
reader = csv.DictReader(domainfile)
for row in reader:
domain_list.append({'comment': row['comment'], 'rating': row['rating']})
print(str(len(domain_list)))
d_list = []
for c in range(len(domain_list)):
tmp_c = domain_list[c]['comment']
tmp_r = domain_list[c]['rating']
if tmp_r in args.n:
d_list.append((format_sentence(tmp_c, stopwords), 0))
if tmp_r in args.p:
d_list.append((format_sentence(tmp_c, stopwords), 1))
#classifier2 = NaiveBayesClassifier.train(domain_list)
model = DecisionTreeClassifier.train(dataset)
domain_accuracy = nltk.classify.util.accuracy(model, d_list)
print('Classifier domain shift accuracy:', domain_accuracy)