def predict(test_list: list, special={}):
"""Takes in a test_list of sentences to classify and returns a list of labels
Parameters
----------
test_list:[str]
list of strings to be labeled
DEFAULTS:
special:dict
special lexicon dictionary we would like to use in scoring words
Returns
-------
labels:list
list of labels for each item in test_list e.g. [1,0,0,-1,...]
"""
contractions = rneg.getContractions() # create contractions
emoti_dict = emoticons.load_emoticon_sentiment() # create emoti_dict
label_list = [] # list to return
for item in test_list:
label, _ = swn_pipeline(item, contractions=contractions, emoti_dict=emoti_dict, special=special)
label_list.append(label)
return label_list
def senti_train(
train_list: list,
special_dict={},
iterations=5,
stop_iter_threshold=0.0,
diff_thresh=0,
word_err_threshold=0.2,
output_filename="outputNew.txt",
learn_nouns=False,
count_nouns=True,
):
"""trains on train_list to create special lexiographic dictionary
Each iteration, will create a bag_of_words to count all the words that appear
in the tweets incorrectly labeled. It will then reverse the positive and
negative scores for all the words that cause an error above word_err_threshold
Do this for iterations times or until error rate is below threshold
Parameters
----------
train_list: list[list]
in form [[label1, tweet1], [label2, tweet2],...]
Label must be either 1, 0 or -1 for positive, neutral, negative respectively
DEFAULTS:
special: dict = {}
dictionary of specialized lexicon to score words on top of SentiWordNet
iterations: int = 5
how many times we want to iterate through the list to train
stop_iter_threshold: int = 0.0
err_threshold below which to stop training
diff_thresh:int = 0
how far we want the positive and negative score to differ to be considered
not neutral
word_err_threshold: int=0.2
how many errors word would have to cause in order to have their sentiscores changed
output_filename: str
file to output debugging results
learn_nouns: boolean=False
considers whether or not to add nouns to the outputted specialized dictionary
count_nouns=true: determines whether or not to include nouns in the scoring
Returns
-------
tuple(dict, list)
dict is the newly created specialized sentiment lexicon
list is a is a list of error rates from each iteration
"""
if type(iterations) != int:
raise TypeError("Expected 'iterations' to be of type int but was of type {}".format(type(iterations)))
if iterations < 0:
raise ValueError("Parameter 'iterations' must be >= 0")
if stop_iter_threshold < 0:
raise ValueError("Paremeter 'threshold' must be >= 0")
if word_err_threshold < 0:
raise ValueError("Paremeter 'sp_threshold' must be >= 0")
if diff_thresh < 0:
raise ValueError("Paremeter 'diff_thresh' must be >= 0")
# create all necessary variables, files and dictionaries for processing
output = open(output_filename, "w", encoding="utf8")
output.write(
"Training on {} items with a specialized dictionary of {} items\n".format(len(train_list), len(special_dict))
)
output.write(
"Iterations={}; Stopping Error Threshold={}; word error to be in dictionary threshold = {}\n".format(
iterations, stop_iter_threshold, word_err_threshold
)
)
output.write(
"Words must be {} apart to not be neutral. Learning nouns set to {} and counting nounse set to = {}\n".format(
diff_thresh, learn_nouns, count_nouns
)
)
output.flush()
contractions = rneg.getContractions()
emoti_dict = emoticons.load_emoticon_sentiment()
it = 0 # current iteration
err_rate = 1 # current_error rate (initialized to 1 because assume everything wrong)
err_list = [] # list of error_rates at each iteration
special = special_dict # specialized dictionary different from sentiwordnet labels learned
flip_dict = {} # keeps track of words that keep flipping back and forth (keeps the one with lower error)
while it < iterations and err_rate > stop_iter_threshold:
err_count = 0 # counts how many errors were in the iteration
err_bow = {} # dict of words, counts and scores word:{count, pos, neg, pos_count, neg_count, neutral_count}
# look for words in which there is an error
for item in train_list:
# pow_word_list has tuples in form (word, positive score, negative score, part_of_speech)
prediction, pow_word_tup = swn_pipeline(
item[1],
contractions=contractions,
label_diff_thresh=diff_thresh,
special=special,
emoti_dict=emoti_dict,
count_nouns=count_nouns,
)
# if incorrect increase error and add to err_bow
if prediction != item[0]:
err_count += 1
for word, pos_score, neg_score, pos in pow_word_tup: # count all words in erroneous tweet
# count number of errors
if word in err_bow:
err_bow[word]["count"] += 1
else:
err_bow[word] = {
"count": 1,
"pos_score": pos_score,
"neg_score": neg_score,
"pos_count": 0,
"neg_count": 0,
"neutral_count": 0,
"part_of_speech": pos,
}
# add count for label in word
if item[0] == "-1": # if label was suppose to be negative
err_bow[word]["neg_count"] += 1
elif item[0] == "1": # suppose to be positive
err_bow[word]["pos_count"] += 1
else: # if was suppose to be neutral
err_bow[word]["neutral_count"] += 1
if count_nouns:
output.write(
"word: {}; p-o-s: {}; pos_score: {}; neg_score: {}\n".format(
word, pos, pos_score, neg_score
)
)
# uncomment bottom to see what incorrect labeling has been done
output.write("label: {} ==> {} for tweet ({})\n".format(prediction, item[0], item[1]))
output.write("__________________________________________________________\n")
sys.stdout.flush()
# adjust scores of words
for (
word,
info_dict,
) in (
err_bow.items()
): # info_list in form [count, pos_score, neg_score, pos_count, neg_count, neutral_count, part_of_speech]
# adds a higher score to to the correct label score
if info_dict["count"] > word_err_threshold * err_count and (
learn_nouns or info_dict["part_of_speech"] != "n"
): # word caused a lot of errors
# if suppose to be neutral
if (
info_dict["neutral_count"] > info_dict["pos_count"]
and info_dict["neutral_count"] > info_dict["neg_count"]
or info_dict["pos_count"] == info_dict["neg_count"]
):
new_pos = 0.0
new_neg = 0.0
# otherwise, if word was not in sentidict add word with initialized score
elif info_dict["pos_score"] == None: # word was not in sentidict
if info_dict["pos_count"] > info_dict["neg_count"]: # suppose to be positive
new_pos = 0.5
new_neg = 0.0
else: # suppose to be negative
new_pos = 0.0
new_neg = 0.5
# if word should have been positive/negative, but scores are opposite
# invert them
elif (
(
info_dict["pos_count"] > info_dict["neg_count"]
and info_dict["pos_score"] < info_dict["neg_score"]
)
or info_dict["pos_count"] < info_dict["neg_count"]
and info_dict["pos_score"] > info_dict["neg_score"]
):
# if had been flipped originally invert only if this cuased a bigger error
if word not in flip_dict or word in flip_dict and flip_dict[word] < info_dict["count"]:
new_pos = info_dict["neg_score"]
new_neg = info_dict["pos_score"]
flip_dict[word] = info_dict["count"]
# if word scores were correct in comparison but not large enough difference
else:
if info_dict["pos_count"] > info_dict["neg_count"]:
new_pos = info_dict["pos_score"] + gv.score_increment
new_pos = 1.0 if new_pos > 1.0 else new_pos
new_neg = info_dict["neg_score"]
else:
new_neg = info_dict["neg_score"] + gv.score_increment
new_neg = 1.0 if new_neg > 1.0 else new_neg
new_pos = info_dict["pos_score"]
special[word] = (new_pos, new_neg)
err_rate = err_count / len(train_list)
it += 1
err_list.append(err_rate)
print("current special dictionary:", special)
print("it: {}, err_rate: {}".format(it - 1, err_rate))
print("###########################################################")
output.write("current special dictionary: {}\n".format(special))
output.write("it: {}, err_rate: {}\n".format(it - 1, err_rate))
output.write("###########################################################\n")
output.flush()
output.close()
return special, err_list