Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 6 Successfully cheating using SentiWordNet While the linguistic information that we discussed earlier will most likely help us, there is something better we can do to harvest it: SentiWordNet (http:// sentiwordnet.isti.cnr.it). Simply put, it is a 13 MB file that assigns most of the English words a positive and negative value. In more complicated words, for every synonym set, it records both the positive and negative sentiment values. Some examples are as follows: POS ID PosScore NegScore SynsetTerms Description a 00311354 0.25 0.125 studious#1 Marked by care and effort; "made a studious attempt to fix the television set" a 00311663 0 0.5 careless#1 Marked by lack of attention or consideration or forethought or thoroughness; not careful n 03563710 0 0 implant#1 A prosthesis placed permanently in tissue v 00362128 0 0 kink#2 curve#5 curl#1 Form a curl, curve, or kink; "the cigar smoke curled up at the ceiling" With the information in the POS column, we will be able to distinguish between the noun "book" and the verb "book". PosScore and NegScore together will help us to determine the neutrality of the word, which is 1-PosScore-NegScore. SynsetTerms lists all words in the set that are synonyms. The ID and Description can be safely ignored for our purpose. The synset terms have a number appended, because some occur multiple times in different synsets. For example, "fantasize" conveys two quite different meanings, also leading to different scores: POS ID PosScore NegScore SynsetTerms Description v 01636859 0.375 0 fantasize#2 fantasise#2 v 01637368 0 0.125 fantasy#1 fantasize#1 fantasise#1 Portray in the mind; "he is fantasizing the ideal wife" Indulge in fantasies; "he is fantasizing when he says that he plans to start his own company" [ 141 ]
Classification II – Sentiment Analysis To find out which of the synsets to take, we would have to really understand the meaning of the tweets, which is beyond the scope of this chapter. The field of research that focuses on this challenge is called word sense disambiguation. For our task, we take the easy route and simply average the scores over all the synsets in which a term is found. For "fantasize", PosScore would be 0.1875 and NegScore would be 0.0625. The following function, load_sent_word_net(), does all that for us, and returns a dictionary where the keys are strings of the form "word type/word", for example "n/ implant", and the values are the positive and negative scores: import csv, collections def load_sent_word_net(): sent_scores = collections.defaultdict(list) <strong>with</strong> open(os.path.join(DATA_DIR, SentiWordNet_3.0.0_20130122.txt"), "r") as csvfile: reader = csv.reader(csvfile, delimiter='\t', quotechar='"') for line in reader: if line[0].starts<strong>with</strong>("#"): continue if len(line)==1: continue POS,ID,PosScore,NegScore,SynsetTerms,Gloss = line if len(POS)==0 or len(ID)==0: continue #print POS,PosScore,NegScore,SynsetTerms for term in SynsetTerms.split(" "): # drop number at the end of every term term = term.split("#")[0] term = term.replace("-", " ").replace("_", " ") key = "%s/%s"%(POS,term.split("#")[0]) sent_scores[key].append((float(PosScore), float(NegScore))) for key, value in sent_scores.iteritems(): sent_scores[key] = np.mean(value, axis=0) return sent_scores [ 142 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
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Topic Modeling Topic modeling was f
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Page 132 and 133: Classification II - Sentiment Analy
Page 134 and 135: Chapter 6 Getting to know the Bayes
Page 136 and 137: Using Naive Bayes to classify Given
Page 138 and 139: Chapter 6 This denotation "" leads
Page 140 and 141: Chapter 6 Similarly, we do this for
Page 142 and 143: Chapter 6 A quick look at the previ
Page 144 and 145: Chapter 6 To keep our experimentati
Page 146 and 147: Chapter 6 Y = np.zeros(Y.shape[0])
Page 148 and 149: Chapter 6 ° ° Experiment with whe
Page 150 and 151: Chapter 6 We have to be patient whe
Page 152 and 153: Chapter 6 First, we define a range
Page 154 and 155: Chapter 6 Determining the word type
Page 158 and 159: Chapter 6 Our first estimator Now w
Page 160 and 161: Chapter 6 for d in documents: allca
Page 162 and 163: Regression - Recommendations You ha
Page 164 and 165: Chapter 7 The preceding graph shows
Page 166 and 167: Chapter 7 Root mean squared error a
Page 168 and 169: Penalized regression The important
Page 170 and 171: Chapter 7 P greater than N scenario
Page 172 and 173: Chapter 7 So, we can see that the d
Page 174 and 175: Chapter 7 Fortunately, scikit-learn
Page 176 and 177: [ 161 ] Chapter 7 The loading of th
Page 178: Chapter 7 Summary In this chapter,
Page 181 and 182: Regression - Recommendations Improv
Page 185 and 186: Weights Regression - Recommendation
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Dimensionality Reduction Sketching
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Dimensionality Reduction However, t
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Dimensionality Reduction To underst
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Dimensionality Reduction In order t
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Dimensionality Reduction Asking the
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Dimensionality Reduction n_ feature
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Dimensionality Reduction Sketching
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Dimensionality Reduction Limitation
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Dimensionality Reduction Now, MDS t
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Dimensionality Reduction Of course,
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Big(ger) Data • Your algorithms c
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Big(ger) Data sleep(4) return 2*x @
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Big(ger) Data Looking under the hoo
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Big(ger) Data def write_result(ofna
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Big(ger) Data Amazon Web Services i
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Big(ger) Data In EC2 parlance, a ru
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Big(ger) Data In this system, pip i
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Big(ger) Data Keys, keys, and more
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Big(ger) Data We can use the same j
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Where to Learn More about Machine L
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• Machined Learnings at http://ww
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Index A AcceptedAnswerId attribute
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F false negative 41 false positive
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inary matrix of recommendations, us
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sklearn.naive_bayes package 127 skl
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NumPy Beginner's Guide - Second Edi
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