Text mining is a popular topic for exploring what text you have in documents etc. Text mining and NLP can help you discover different patterns in the text like uncovering certain words or phases which are commonly used, to identifying certain patterns and linkages between different texts/documents. Combining this work on Text mining you can use Word Clouds, time-series analysis, etc to discover other aspects and patterns in the text. Check out my previous blog posts (post 1, post 2) on performing Text Mining on documents (manifestos from some of the political parties from the last two national government elections in Ireland). These two posts gives you a simple indication of what is possible.
We can build upon these Text Mining examples to include other machine learning algorithms like those for Classification. With Classification we want to predict or label a record or document to have a particular value. With Classification this could involve labeling a document as being positive or negative (movie or book reviews), or determining if a document is for a particular domain such as Technology, Sports, Entertainment, etc
With Classification problems we typically have a case record containing many different feature/attributes. You will see many different examples of this. When we add in Text Mining we are adding new/additional features/attributes to the case record. These new features/attributes contain some characteristics of the Word (or Term) frequencies in the documents. This is a form of feature engineering, where we create new features/attributes based on our dataset.
Let’s work through an example of using Text Mining and Classification Algorithm to build a model for determining/labeling/classifying documents.
The Dataset: For this example I’ll use Move Review dataset from Cornell University. Download and unzip the file. This will create a set of directories with the reviews (as individual documents) listed under the ‘pos’ or ‘neg’ directory. This dataset contains approximately 2000 documents. Other datasets you could use include the Amazon Reviews or the Disaster Tweets.
The following is the Python code to perform NLP to prepare the data, build a classification model and test this model against a holdout dataset. First thing is to load the libraries NLP and some other basics.
import numpy as np import re import nltk from sklearn.datasets import load_files from nltk.corpus import stopwords
Load the dataset.
#This dataset will allow use to perform a type of Sentiment Analysis Classification source_file_dir = r"/Users/brendan.tierney/Dropbox/4-Datasets/review_polarity/txt_sentoken" #The load_files function automatically divides the dataset into data and target sets. #load_files will treat each folder inside the "txt_sentoken" folder as one category # and all the documents inside that folder will be assigned its corresponding category. movie_data = load_files(source_file_dir) X, y = movie_data.data, movie_data.target #load_files function loads the data from both "neg" and "pos" folders into the X variable, # while the target categories are stored in y
We can now use the typical NLP tasks on this data. This will clean the data and prepare it.
documents =  documents =  from nltk.stem import WordNetLemmatizer stemmer = WordNetLemmatizer() for sen in range(0, len(X)): # Remove all the special characters, numbers, punctuation document = re.sub(r'\W', ' ', str(X[sen])) # remove all single characters document = re.sub(r'\s+[a-zA-Z]\s+', ' ', document) # Remove single characters from the start of document with a space document = re.sub(r'\^[a-zA-Z]\s+', ' ', document) # Substituting multiple spaces with single space document = re.sub(r'\s+', ' ', document, flags=re.I) # Removing prefixed 'b' document = re.sub(r'^b\s+', '', document) # Converting to Lowercase document = document.lower() # Lemmatization document = document.split() document = [stemmer.lemmatize(word) for word in document] document = ' '.join(document) documents.append(document)
You can see we have removed all special characters, numbers, punctuation, single characters, spacing, special prefixes, converted all words to lower case and finally extracted the stemmed word.
Next we need to take these words and convert them into numbers, as the algorithms like to work with numbers rather then text. One particular approach is Bag of Words.
The first thing we need to decide on is the maximum number of words/features to include or use for later stages. As you can image when looking across lots and lots of documents you will have a very large number of words. Some of these are repeated words. What we are interested in are frequently occurring words, which means we can ignore low frequently occurring works. To do this we can set
max_feature to a defined value. In our example we will set it to 1500, but in your problems/use cases you might need to experiment to determine what might be a better values.
Two other parameters we need to set include
min_df sets the minimum number of documents to contain the word/feature.
max_df specifies the percentage of documents where the words occur, for example if this is set to 0.7 this means the words should occur in a maximum of 70% of the documents.
from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer(max_features=1500, min_df=5, max_df=0.7,stop_words=stopwords.words('english')) X = vectorizer.fit_transform(documents).toarray()
The CountVectorizer in the above code also remove Stop Words for the English language. These words are generally basic words that do not convey any meaning. You can easily add to this list and adjust it to suit your needs and to reflect word usage and meaning for your particular domain.
The bag of words approach works fine for converting text to numbers. However, it has one drawback. It assigns a score to a word based on its occurrence in a particular document. It doesn’t take into account the fact that the word might also be having a high frequency of occurrence in other documentsas well. TFIDF resolves this issue by multiplying the term frequency of a word by the inverse document frequency. The TF stands for “Term Frequency” while IDF stands for “Inverse Document Frequency”.
And the Inverse Document Frequency is calculated as:
IDF(word) = Log((Total number of documents)/(Number of documents containing the word))
The term frequency is calculated as:
Term frequency = (Number of Occurrences of a word)/(Total words in the document)
The TFIDF value for a word in a particular document is higher if the frequency of occurrence of thatword is higher in that specific document but lower in all the other documents.
To convert values obtained using the bag of words model into TFIDF values, run the following:
from sklearn.feature_extraction.text import TfidfTransformer
tfidfconverter = TfidfTransformer()
X = tfidfconverter.fit_transform(X).toarray()
That’s the dataset prepared, the final step is to create the Training and Test datasets.
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) #Train DS = 70% #Test DS = 30%
There are several machine learning algorithms you can use. These are the typical classification algorithms. But for simplicity I’m going to use RandomForest algorithm in the following code. After giving this a go, try to do it for the other algorithms and compare the results.
#Import Random Forest Model #Use RandomForest algorithm to create a model #n_estimators = number of trees in the Forest from sklearn.ensemble import RandomForestClassifier classifier = RandomForestClassifier(n_estimators=1000, random_state=0) classifier.fit(X_train, y_train)
Now we can test the model on the hold-out or Test dataset
#Now label/classify the Test DS y_pred = classifier.predict(X_test) #Evaluate the model from sklearn.metrics import classification_report, confusion_matrix, accuracy_score print("Accuracy:", accuracy_score(y_test, y_pred)) print(confusion_matrix(y_test,y_pred)) print(classification_report(y_test,y_pred))
This model gives the following results, with an over all accuracy of 85% (you might get a slightly different figure). This is a good outcome and a good predictive model. But is it the best one? We simply don’t know at this point. Using the ‘No Free Lunch Theorem’ we would would have to see what results we would get from the other algorithms.
Although this example only contains the words from the documents, we can see how we could include this with other features/attributes when forming a case record. For example, our case records represented Insurance Claims, the features would include details of the customer, their insurance policy, the amount claimed, etc and in addition could include incident reports, claims assessor reports etc. This would be documents which we can include in the building a predictive model to determine of an insurance claim is fraudulent or not.