Text classification is most probably, the most encountered Natural Language Processing task. It can be described as assigning texts to an appropriate bucket. To train a text classifier, we need some annotated data. This training data can be obtained through several methods.
Suppose you want to build a spam classifier. You would export the contents of your mailbox. For the sake of simplicity, we will use a news corpus already available in scikit-learn. Training a model usually requires some trail and error. Text classification is the most common use case for this classifier. TfidfVectorizer has the advantage of emphasizing the most important words for a given document.
Pretty good result for a first try. The first thing that comes to mind is to ignore insignificant words. Good boost. We can now try to play with the alpha parameter of the Naive-Bayes classifier. Great progress. It basically means you take the available words in a text and keep count of […]. Make sure you brush up on the text classification […]. Not sure what you are trying to accomplish. You can use chi2 to do feature selection. Feature selection means you discard the features in the case of text classification, words that contribute the least to the performance of the classifier.
Text Classification with Scikit-Learn
This way you can have a lighter model and sometimes it helps performance wise by clearing the noise. Is it possible check, which word from news. There are ways of computing probabilities. I am currently exploring Spacy for NER and need to extract relevant information from job descriptions posted on Linkedin.
Can you help me with some leads or process? Will be really great if you can cover something like a resume to job description matching in one of your posts. Hello, is it possible to give you a list of suggested categories ordered by the accuracy? It might answer your questions. Your email address will not be published. Notify me of follow-up comments by email.
Notify me of new posts by email. Menu Sidebar. Accuracy: 0. It basically means you take the available words in a text and keep count of […] Reply. Make sure you brush up on the text classification […] Reply.
Hi Yousif, Not sure what you are trying to accomplish. Thank you very much for your reply but I want to know how I can use chi2? Hi Yousif, You can use chi2 to do feature selection.There are lots of applications of text classification in the commercial world. For example, news stories are typically organized by topics; content or products are often tagged by categories; users can be classified into cohorts based on how they talk about a product or brand online ….
However, the vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering spam vs.
In most cases, our real world problem are much more complicated than that. Therefore, this is what we are going to do today: Classifying Consumer Finance Complaints into 12 pre-defined classes. The data can be downloaded from data. We use Python and Jupyter Notebook to develop our system, relying on Scikit-Learn for the machine learning components.
If you would like to see an implementation in PySparkread the next article. The problem is supervised text classification problem, and our goal is to investigate which supervised machine learning methods are best suited to solve it. Given a new complaint comes in, we want to assign it to one of 12 categories. The classifier makes the assumption that each new complaint is assigned to one and only one category.
Multi Label Text Classification with Scikit-Learn
This is multi-class text classification problem. Before diving into training machine learning models, we should look at some examples first and the number of complaints in each class:. We also create a couple of dictionaries for future use.
After cleaning up, this is the first five rows of the data we will be working on:. We see that the number of complaints per product is imbalanced. When we encounter such problems, we are bound to have difficulties solving them with standard algorithms.
Conventional algorithms are often biased towards the majority class, not taking the data distribution into consideration. In the worst case, minority classes are treated as outliers and ignored. For some cases, such as fraud detection or cancer prediction, we would need to carefully configure our model or artificially balance the dataset, for example by undersampling or oversampling each class.
However, in our case of learning imbalanced data, the majority classes might be of our great interest. It is desirable to have a classifier that gives high prediction accuracy over the majority class, while maintaining reasonable accuracy for the minority classes.
Therefore, we will leave it as it is. The classifiers and learning algorithms can not directly process the text documents in their original form, as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length. Therefore, during the preprocessing step, the texts are converted to a more manageable representation. One common approach for extracting features from text is to use the bag of words model: a model where for each document, a complaint narrative in our case, the presence and often the frequency of words is taken into consideration, but the order in which they occur is ignored.
Specifically, for each term in our dataset, we will calculate a measure called Term Frequency, Inverse Document Frequency, abbreviated to tf-idf. We will use sklearn. TfidfVectorizer to calculate a tf-idf vector for each of consumer complaint narratives:. Now, each of consumer complaint narratives is represented by features, representing the tf-idf score for different unigrams and bigrams. We can use sklearn. Most correlated unigrams:. Most correlated bigrams:.
After all the above data transformation, now that we have all the features and labels, it is time to train the classifiers.
Multi-Class Text Classification with Scikit-Learn
There are a number of algorithms we can use for this type of problem. We are now ready to experiment with different machine learning models, evaluate their accuracy and find the source of any potential issues.Its purpose is to aggregate a number of data transformation steps, and a model operating on the result of these transformations, into a single object that can then be used in place of a simple estimator. This allows for the one-off definition of complex pipelines that can be re-used, for example, in cross-validation functions, grid-searches, learning curves and so on.
Each webpage in the provided dataset is represented by its html content as well as additional meta-data, the latter of which I will ignore here for simplicity. The idea is simple. Each word in a document is represented by a number that is proportional to its frequency in the document, and inversely proportional to the number of documents in which it occurs.
Scikit-learn provides a TfidfVectorizer class, which implements this transformation, along with a few other text-processing options, such as removing the most common words in the given language stop words. In few cases, however, is the vectorization of text into numerical values as simple as applying tf-idf to the raw data.
Often, the relevant text to be converted needs to be extracted first. Also, the tf-idf transformation will usually result in matrices too large to be used with certain machine learning algorithms. Hence dimensionality reduction techniques are often applied too. Manually implementing these steps everytime text needs to be transformed quickly becomes repetitive and tedious.
It needs to be done for the training as well as test set. Pipelines help reduce this repetition. Here, we first create an instance of the tf-idf vectorizer for its parameters see documentation. We then create a list of tuples, each of which represents a data transformation step and its name the latter of which is required, e. The first two are custom transformers and the last one our vectorizer. The corresponding values are concatenated into a single string per row in the dataset. The result is a new transformed dataset with a single column containing the extracted text, which can then be processed by the vectorizer.
JsonFields itself encapsulates another custom transformer Selectused here to keep the specification of pipelines concise. It could also have been used as a prior step in the definition of the pipeline. You may have noticed the use of the function unsquash and the Transformer Squash in the first definition of the pipeline. This is an unfortunate but apparently required part of dealing with numpy arrays in scikit-learn.
The problem is this. One may want, as part of the transform pipeline, to concatenate features from different sources into a single feature matrix. However, both only operate on feature columns of dimensionality n,1. As a result, when working with multiple feature sources, one of them being vectorized text, it is necessary to convert back and forth between the two ways of representing a feature column. The Squash and Unsquash class used above simply wraps this functionality for use in pipelines.
For these and some other Transformers you may find useful check here.Please cite us if you use the software. Click here to download the full example code or to run this example in your browser via Binder.
This is an example showing how scikit-learn can be used to classify documents by topics using a bag-of-words approach. This example uses a scipy.
The dataset used in this example is the 20 newsgroups dataset. It will be automatically downloaded, then cached. We train and test the datasets with 15 different classification models and get performance results for each model. The bar plot indicates the accuracy, training time normalized and test time normalized of each classifier. Total running time of the script: 0 minutes 5. Gallery generated by Sphinx-Gallery.
Toggle Menu. Prev Up Next. Classification of text documents using sparse features Load data from the training set Benchmark classifiers Add plots. Note Click here to download the full example code or to run this example in your browser via Binder. Loading 20 newsgroups dataset for categories: ['alt. The more regularization, the more sparsity.Please cite us if you use the software. The goal of this guide is to explore some of the main scikit-learn tools on a single practical task: analyzing a collection of text documents newsgroups posts on twenty different topics.
To get started with this tutorial, you must first install scikit-learn and all of its required dependencies. Please refer to the installation instructions page for more information and for system-specific instructions. The source can also be found on Github. Machine learning algorithms need data. Here is the official description, quoted from the website :. The 20 Newsgroups data set is a collection of approximately 20, newsgroup documents, partitioned nearly evenly across 20 different newsgroups.
The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. In the following we will use the built-in dataset loader for 20 newsgroups from scikit-learn.
Alternatively, it is possible to download the dataset manually from the website and use the sklearn. In order to get faster execution times for this first example we will work on a partial dataset with only 4 categories out of the 20 available in the dataset:. The files themselves are loaded in memory in the data attribute. For reference the filenames are also available:. Supervised learning algorithms will require a category label for each document in the training set.
In this case the category is the name of the newsgroup which also happens to be the name of the folder holding the individual documents.
The category integer id of each sample is stored in the target attribute:. In order to perform machine learning on text documents, we first need to turn the text content into numerical feature vectors. Assign a fixed integer id to each word occurring in any document of the training set for instance by building a dictionary from words to integer indices. For each document icount the number of occurrences of each word w and store it in X[i, j] as the value of feature j where j is the index of word w in the dictionary.
Fortunately, most values in X will be zeros since for a given document less than a few thousand distinct words will be used. For this reason we say that bags of words are typically high-dimensional sparse datasets. We can save a lot of memory by only storing the non-zero parts of the feature vectors in memory. Text preprocessing, tokenizing and filtering of stopwords are all included in CountVectorizerwhich builds a dictionary of features and transforms documents to feature vectors:.
CountVectorizer supports counts of N-grams of words or consecutive characters. Once fitted, the vectorizer has built a dictionary of feature indices:. The index value of a word in the vocabulary is linked to its frequency in the whole training corpus. Occurrence count is a good start but there is an issue: longer documents will have higher average count values than shorter documents, even though they might talk about the same topics.
To avoid these potential discrepancies it suffices to divide the number of occurrences of each word in a document by the total number of words in the document: these new features are called tf for Term Frequencies. Another refinement on top of tf is to downscale weights for words that occur in many documents in the corpus and are therefore less informative than those that occur only in a smaller portion of the corpus.
Both tf and tf—idf can be computed as follows using TfidfTransformer :. In the above example-code, we firstly use the fit. These two steps can be combined to achieve the same end result faster by skipping redundant processing.
Now that we have our features, we can train a classifier to try to predict the category of a post. To try to predict the outcome on a new document we need to extract the features using almost the same feature extracting chain as before. The names vecttfidf and clf classifier are arbitrary. We will use them to perform grid search for suitable hyperparameters below.In a previous article I wrote about a recent request from a client to classify short pieces of text.
We started out with the simplest thing possible, which in that case was to use a 3rd party API. We show that with minimal processing and no parameter tuning at all we get the following accuracies:. However, each one of these classifiers can be improved significantly with additional parameter tuning.
All of these algorithms will perform differently with your data and the decision on if tuning and hosting your own models is worth the improvement is up to your specific needs.
Tuning and hosting will be the subject of a future articles. Lets take a quick look at how we can use the various classifiers from sklearn. For background on the data set see this article. We need to load the data without the headers, footers and quotes. We'll do basic clean up and remove posts that are less than 50 characters as those are likely to be too short for us to use.
We don't truncate long texts since these algorithms do not have that requirement. Now lets try a Naive Bayes classifier which gets an accuracy of 0. The Random Forest classifier with the default parameters only 10 trees gets 0. The crowd favorite Logistic Regression gets 0. And the simplest of all K Nearest Neighbors classifier with the default of 5 neighbors gets 0. We looked at performance of five common classifiers from sklearn using the least amount of programming and tuning possible.
The performance of two of them come close to the 3rd party API but all can be improved with further tuning. Each classifier will work differently on your particular data and with different hyper-parameters so testing with your own use case is critical. In a future article we'll look at how to go about tuning these classifiers to get even better results. Text Classification with Scikit-Learn In a previous article I wrote about a recent request from a client to classify short pieces of text.
The code Lets take a quick look at how we can use the various classifiers from sklearn. Want to get notified of new articles and projects?Assigning categories to documents, which can be a web page, library book, media articles, gallery etc. In this article, I would like to demonstrate how we can do text classification using python, scikit-learn and little bit of NLTK.
Disclaimer : I am new to machine learning and also to blogging First. So, if there are any mistakes, please do let me know. All feedback appreciated. The prerequisites to follow this example are python version 2.
You can just install anaconda and it will get everything for you. Also, little bit of python and ML basics including text classification is required. We will be using scikit-learn python libraries for our example.
About the data from the original website :. The 20 Newsgroups data set is a collection of approximately 20, newsgroup documents, partitioned nearly evenly across 20 different newsgroups. To the best of my knowledge, it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper, though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering.
This will open the notebook in browser and start a session for you. You can give a name to the notebook - Text Classification Demo 1. Loading the data set: this might take few minutes, so patience.
Note: Above, we are only loading the training data. We will load the test data separately later in the example. You can check the target names categories and some data files by following commands. Text files are actually series of words ordered. In order to run machine learning algorithms we need to convert the text files into numerical feature vectors.
We will be using bag of words model for our example. Briefly, we segment each text file into words for English splitting by spaceand count of times each word occurs in each document and finally assign each word an integer id.
Each unique word in our dictionary will correspond to a feature descriptive feature. More about it here. TF: Just counting the number of words in each document has 1 issue: it will give more weightage to longer documents than shorter documents. To avoid this, we can use frequency TF - Term Frequencies i. We can achieve both using below line of code:.
There are various algorithms which can be used for text classification.
You can easily build a NBclassifier in scikit using below 2 lines of code: note - there are many variants of NB, but discussion about them is out of scope. This will train the NB classifier on the training data we provided.
Building a pipeline: We can write less code and do all of the above, by building a pipeline as follows:. Also, congrats!!! Almost all the classifiers will have various parameters which can be tuned to obtain optimal performance. Here, we are creating a list of parameters for which we would like to do performance tuning. All the parameters name start with the classifier name remember the arbitrary name we gave.
This might take few minutes to run depending on the machine configuration.