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Get Feature Vector
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def getFeatureVector(tweet): | |
featureVector = [] | |
#split tweet into words | |
words = tweet.split() | |
for w in words: | |
#replace two or more with two occurrences | |
w = replaceTwoOrMore(w) | |
#strip punctuation | |
w = w.strip('\'"?,.') | |
#check if the word stats with an alphabet | |
val = re.search(r"^[a-zA-Z][a-zA-Z0-9]*$", w) | |
#ignore if it is a stop word | |
if(w in stopWords or val is None): | |
continue | |
else: | |
featureVector.append(w.lower()) | |
return featureVector | |
###load airline sentiment training data | |
airlinetrain = pd.read_csv("Airline-Sentiment-2-w-AA.csv", encoding ="ISO-8859-1") | |
tweets = [] | |
featureList = [] | |
for i in range(len(airlinetrain)): | |
sentiment = airlinetrain['airline_sentiment'][i] | |
tweet = airlinetrain['text'][i] | |
processedTweet = processTweet2(tweet) | |
featureVector = getFeatureVector(processedTweet) | |
featureList.extend(featureVector) | |
tweets.append((featureVector, sentiment)) | |
def extract_features(tweet): | |
tweet_words = set(tweet) | |
features = {} | |
for word in featureList: | |
features['contains(%s)' % word] = (word in tweet_words) | |
return features | |
#end | |
### Remove featureList duplicates | |
featureList = list(set(featureList)) | |
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