Text Classification in Data Mining Simplified 101

on Data Integration, Data Mining, Data Science, Data Visualization, Data Warehouse, ETL, ETL Tutorials, Machine Learning • May 6th, 2022 • Write for Hevo

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Rapid Advancements in Computerized or Digital Information have resulted in a massive amount of information and data. Text databases, which are large collections of documents from various sources, contain a significant portion of the available information. Because of the increasing amount of information available in electronic form, text databases are rapidly expanding. More than 80% of current information is in the form of unstructured or semi-structured data.

Traditional Information retrieval techniques are becoming insufficient for the ever-increasing amount of text data. As a result, Text Classification in Data Mining has grown in popularity. The discovery of appropriate patterns and the analysis of text documents from massive amounts of data is a major issue in real-world application areas.

It used to be a difficult and costly process because it required time and resources to manually sort the data. Text Classifiers with NLP have proven to be a great alternative for quickly, cost-effectively, and scalable text data structure. Text Classification systems are being used by an increasing number of organizations to effectively manage the ever-increasing inflow of unstructured data.

The goal of Text Classification in Data Mining is to improve information discoverability and make all discovered knowledge available or actionable to support strategic decision-making. Let’s dive in and experience it live!

Table of Contents

What is Text Classification?

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Text Classification Algorithms are at the heart of many software systems that process large amounts of text data. Text Classification is used by email software to determine whether incoming mail is sent to the inbox or filtered into the spam folder. Text classification is used in discussion forums to determine whether comments should be flagged as inappropriate.

These are two examples of Topic Classification, in which a text document is classified into one of a predefined set of topics. Many topic classification problems rely heavily on textual keywords for categorization.

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Sentiment Analysis is another common type of text classification, with the goal of determining the polarity of text content: the type of opinion it expresses. This can be expressed as a binary like/dislike rating or as a more granular set of options, such as a star rating from 1 to 5.

Sentiment Analysis can be used to determine whether or not people liked the Black Panther movie by analyzing Twitter posts or extrapolating the general public’s opinion of a new brand of Nike shoes based on Walmart reviews.

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What is Data Mining?

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Data Mining is the process of analyzing data in order to uncover patterns, correlations, and anomalies in large datasets. These datasets contain information from employee databases, financial information, vendor lists, client databases, network traffic, and customer accounts, among other things. Statistics, Machine Learning (ML), and Artificial Intelligence can be used to explore large datasets manually or automatically (AI).

The Data Mining process begins with determining the business goal that will be achieved using the data. Data is then collected from various sources and loaded into Data Warehouses, which act as a repository for analytical data. Data is also cleansed, which includes the addition of missing data and the removal of duplicate data. Sophisticated tools and mathematical models are used to find patterns in data.

Key Features of Data Mining

These are the characteristics of Data Mining:

  • Probable Outcome Prediction
  • Focuses on Large Datasets and Databases
  • Automatic Pattern Predictions are made based on Behavior Analysis
  • To compute a feature from other features, any SQL expression can be used

How does Text Classification in Data Mining Work?

The process of categorizing text into organized groups is known as text classification, also known as text tagging or text categorization. Text Classification in Data Mining can automatically analyze text and assign a set of pre-defined tags or categories based on its content using Natural Language Processing (NLP).

Text Classification in Data Mining is becoming an increasingly important part of the business because it enables easy data insights and the automation of business processes.

The following are some of the most common examples and use cases in Text Classification in Data Mining for Automatic Text Classification:

  • Sentiment Analysis for determining whether a given text is speaking positively or negatively about a particular subject (e.g. for brand monitoring purposes).
  • The task of determining the theme or topic of a piece of text is known as topic detection (e.g. knowing if a product review is about Ease of Use, Customer Support, or Pricing when analyzing customer feedback).
  • Language detection refers to the process of determining the language of a given text (e.g. knowing if an incoming support ticket is written in English or Spanish for automatically routing tickets to the appropriate team).

Here is the Text Classification in Data Mining workflow:

Step 1: Collect Information

The most important step in solving any Supervised Machine Learning problem is gathering data. Your Text Classifier is only as good as the dataset it is trained on.

If you don’t have a specific problem in mind and are simply interested in learning about Text Classification or Text Classification in Data Mining in general, there are a plethora of open-source datasets available. If, on the other hand, you are attempting to solve a specific problem, you will need to gather the necessary data.

Text Classification in Data Mining is not a buzz, Many organizations, such as Twitter and the New York Times, provide public APIs for accessing their data. You might be able to use these to solve the problem you’re trying to solve.

Here are some things to keep in mind when you gather data for Text Classification in Data Mining:

  • Before you use a Public API, make sure you understand its limitations. Some APIs, for example, limit the number of queries you can make per second.
  • The more training examples (referred to as samples throughout this guide), the better. This will help your model generalize more effectively.
  • Make certain that the number of samples for each class or topic is not excessively imbalanced. That is, each class should have a comparable number of samples.
  • Make certain that your samples adequately cover the space of possible inputs, rather than just the common cases.

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Step 2: Investigate Your Data

Building and Training a model is only one step in the process. Understanding the characteristics of your data ahead of time will allow you to build a more accurate model. This could simply mean achieving greater accuracy. It could also imply requiring less data or fewer computational resources for training.

First, import the dataset into Python.

def load_imdb_sentiment_analysis_dataset(data_path, seed=123):
    """Loads the IMDb movie reviews sentiment analysis dataset.
    #Text Classification in Data Mining Step 2
    # Arguments
        data_path: string, path to the data directory.
        seed: int, seed for randomizer.

    # Returns
        A tuple of training and validation data.
        Number of training samples: 25000
        Number of test samples: 25000
        Number of categories: 2 (0 - negative, 1 - positive)

    # References
        Mass et al., http://www.aclweb.org/anthology/P11-1015

        Download and uncompress archive from:
        http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
    """
    imdb_data_path = os.path.join(data_path, 'aclImdb')

    # Load the training data
    train_texts = []
    train_labels = []
    for category in ['pos', 'neg']:
        train_path = os.path.join(imdb_data_path, 'train', category)
        for fname in sorted(os.listdir(train_path)):
            if fname.endswith('.txt'):
                with open(os.path.join(train_path, fname)) as f:
                    train_texts.append(f.read())
                train_labels.append(0 if category == 'neg' else 1)

    # Load the validation data.
    test_texts = []
    test_labels = []
    for category in ['pos', 'neg']:
        test_path = os.path.join(imdb_data_path, 'test', category)
        for fname in sorted(os.listdir(test_path)):
            if fname.endswith('.txt'):
                with open(os.path.join(test_path, fname)) as f:
                    test_texts.append(f.read())
                test_labels.append(0 if category == 'neg' else 1)

    # Shuffle the training data and labels for Text Classification in Data Mining.
    random.seed(seed)
    random.shuffle(train_texts)
    random.seed(seed)
    random.shuffle(train_labels)

    return ((train_texts, np.array(train_labels)),
            (test_texts, np.array(test_labels)))

Examine the Data

After loading the data, it’s a good idea to run some checks on it: select a few samples and manually check if they match your expectations. Print a few random samples, for example, to see if the sentiment label corresponds to the sentiment of the review.

Step 2.5: Select a Model

We have assembled our dataset and gained insights into the key characteristics of our data at this point. Following that, we should consider which classification model to employ based on the metrics gathered in Step 2. This includes questions like, “How do we present the text data to an algorithm that expects numeric input?” (this is known as data preprocessing and vectorization), “What type of model should we use?“, and “What configuration parameters should we use for our model?” and so on.

We now have access to a wide range of data preprocessing and model configuration options as a result of decades of research. The availability of a very large array of viable options to choose from, on the other hand, greatly increases the complexity and scope of the specific problem at hand.

Given that the best options may not be obvious, a naive solution would be to exhaust all possible options, pruning some through intuition. That, however, would be prohibitively expensive.

The model selection algorithm shown below is a summary of our research. Don’t worry if you don’t understand all of the terms used in them yet; the sections that follow will explain them thoroughly.

Data Preparation and Model Building Algorithm

1. Calculate the number of samples/number of words per sample ratio.
2. If this ratio is less than 1500, tokenize the text as n-grams and use a
simple multi-layer perceptron (MLP) model to classify them (left branch in the
flowchart below):
  a. Split the samples into word n-grams; convert the n-grams into vectors.
  b. Score the importance of the vectors and then select the top 20K using the scores.
  c. Build an MLP model.
3. If the ratio is greater than 1500, tokenize the text as sequences and use a
   sepCNN model to classify them (right branch in the flowchart below):
  a. Split the samples into words; select the top 20K words based on their frequency.
  b. Convert the samples into word sequence vectors.
  c. If the original number of samples/number of words per sample ratio is less
     than 15K, using a fine-tuned pre-trained embedding with the sepCNN
     model will likely provide the best results.
4. Measure the model performance with different hyperparameter values to find
   the best model configuration for the dataset.

Step 3: Gather Your Data

Before we can feed our data to a model, it must be transformed into a format that the model can understand.

For Text Classification in Data Mining, First, the data samples that we have gathered may be in a particular order. We don’t want any information related to sampling order to influence the relationship between texts and labels. For example, if a dataset is sorted by class and then divided into training/validation sets, the training/validation sets will not be representative of the overall data distribution.

If your data has already been divided into training and validation sets, make sure to transform your validation data in the same way you did your training data. If you don’t already have separate training and validation sets, you can split the samples after shuffling; typically, 80% of the samples are used for training and 20% for validation.

Second, Machine Learning Algorithms are fed numerical inputs. This means we’ll have to turn the texts into numerical vectors. This procedure consists of two steps:

  • Tokenization: Break the texts down into words or smaller sub-texts to allow for better generalization of the relationship between the texts and the labels. This determines the dataset’s “vocabulary” (set of unique tokens present in the data).
  • Vectorization: It’s the process of defining a good numerical measure to characterize these texts.

Step 4: Create, Train, and Test Your Model

In this section, we will work on developing, training, and assessing our model. In Step 3, we decided whether to use an n-gram model or a sequence model based on our S/W ratio. It is now time to write and train our classification algorithm. TensorFlow and the tf.keras API will be used for this.

Building Machine Learning Models with Keras is as simple as putting together layers of data-processing building blocks, similar to how we would put together Lego bricks. These layers allow us to specify the order in which we want to perform transformations on our input. Because Learning Algorithm accepts single text input and produces a single classification, we can use the Sequential model API to build a Linear Stack of Layers.

We need to train the model now that we’ve built the model architecture. Training entails making a prediction based on the current state of the model, calculating how inaccurate the prediction is, and updating the network’s weights or parameters to minimize this error and improve the model’s prediction. This process is repeated until our model has converged and can no longer learn.

Step 5: Fine-tune the Hyperparameters

For defining and training the model, we had to select a number of hyperparameters. We relied on our instincts, examples, and best practice recommendations. However, our initial selection of hyperparameter values may not produce the best results. It merely provides us with a good starting point for training. Every problem is unique, and fine-tuning these hyperparameters will aid in refining our model to better represent the specifics of the problem at hand.

Let’s look at some of the hyperparameters we used and what tuning them entails:

  • The model’s number of layers
  • The number of units in each layer
  • Dropout rates
  • Learning rates

Step 6: Put Your Model to Work

When deploying your model, please keep the following points in mind:

  • Check that your production data is distributed in the same way as your training and evaluation data.
  • Re-evaluate on a regular basis by gathering more training data.
  • Retrain your model if your data distribution changes.

This completes your Text Classification in Data Mining! To know more click here.

Benefits of Text Classification in Data Mining

Here are some benefits of Text Mining Approaches in Data Mining:

  • Text Classification in Data Mining provides an accurate representation of the language and how meaningful words are used in context.
  • Text Classification in Data Mining can work at a higher level of abstraction, it makes it easier to write simpler rules.
  • Text Classification in Data Mining uses the fundamental features of semantic technology to understand the meaning of words in context. Because semantic technology allows words to be understood in their proper context, this provides superior precision and recall.
  • Documents that do not “fit” into a specific category are identified and automatically separated once the system is deployed, and the system administrator can fully understand why they were not classified.

Conclusion

Text Classification is a fundamental Machine Learning problem with numerous applications. We have divided the Text Classification in Data Mining Workflow into several steps in this guide. We have suggested a customized approach for each step based on the characteristics of your specific dataset.

Following the guide and the accompanying code, we hope you will understand and get a quick first-cut solution to your text classification problem.

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