Analyzing Text with Machine Learning

Build a Bag-of-Words Model We will go through the definition of "bag-of-words" in this lesson. You will be able to define bag-of-words, comprehend how to create machine learning features from words, and provideexamples of stopwords by the time you finish reading this article. The bag-of-wordsparadigm represents a body of text simply as a loose collection of words. It reduces any textto a list of words in any sequence. This straightforward technique is quite helpful to detect atopic or sentiment in text, such as whether a product review has a favorable or negativesentiment, or what a body of text talks about, even though it ignores the sentence structurelinked with the words. Bag-of-words model and feature matrix We can use the words in a feature matrix where each word is a column, and each text body or review in our movie example is a row that has boolean data values. A cell in the reviewrow gets assigned true if the word appears in the review, and false if it doesn't. Just bylooking at these rows in this matrix, with a limited set of words, we can identify that the topicof these reviews is movies, and probably review 1 and review 3 are positive, and review 2 isnegative. The feature matrix can be used as input to a machine learning model to classify the text. For example, we could use it to determine if a movie review is positive or negative. By usingthe words as features, the model can learn which words are most likely to be associated withpositive or negative reviews. Stopwords and Punctuation Before we move on to our notebook, we would like to mention that it is often practice to filter out stopwords and maybe even punctuations from the bag-of-words before furtheranalysis. Stopwords are words like "the", "that", and "is", which occur a lot but don't have abig significance in identifying the context of the text being processed. Similarly, punctuationslike exclamation marks or commas are useless for classification purposes. Using Stopwords We can use the stopwords in English and punctuation characters to clean the text before creating a bag-of-words model. NLTK (Natural Language Toolkit) is a popular Python libraryfor natural language processing. NLTK actually has a corpus of stopwords for eachlanguage, or in this case, English. Creating a Bag-of-Words Model

To create a bag-of-words model, we can use NLTK to tokenize the text into words and remove any stopwords and punctuation marks. The remaining words can then be used tocreate the feature matrix. Python Function for Bag-of-Words We can convert what we did here into a Python function to generalize what we did, this building of this dictionary here. We define a Python function in this code cell, and then we'lluse it. The build bag of words features Python function accepts a list of words and returns adictionary for it. Conclusion The bag-of-words model is an effective method for examining text data, to sum it. We can discern subjects and sentiment in text by visualizing it as a word cloud. A machine learningmodel that classifies text can be used as input with the feature matrix. To get a moreaccurate representation of the text, stopwords and punctuation should be eliminated beforebuilding a bag-of-words model. Use the well-known Python library NLTK to tokenize text,eliminate stopwords, and build bag-of-words models.