Topic Labeling: Guide on best way to approach

Topic Labeling is a task in Natural Language Processing (NLP) that refers to automatically assigning descriptive labels or categories to text documents or sentences based on their content. The goal is to identify and extract the underlying themes or subjects present within the text.

You can come up with many ideas as you can go with Unsupervised Learning techniques like topic modeling (e.g., Latent Dirichlet Allocation (LDA) or Negative Matrix Factorization (NMF)), clustering algorithms (e.g. K-means or hierarchical clustering ), embedding-based methods (e.g., Word2Vec, GloVe), Supervised Learning (e.g., Support Vector Machines (SVM), Naive Bayes, or neural networks). For each technique mentioned, there are some common difficulties you may face in the process of building the model. In this blog post, we’ll see some approaches that can enhance the performance of the model.

The aim behind making a topic labeling model is to categorize the content into different categories or themes which enable businesses to gain meaningful insight from the data and make appropriate decisions.  This model can be a foundational step for making more advanced analytics and decision-making. Let’s see different steps involved in topic labeling one by one.

Data Gathering

The model’s performance and accuracy are directly related to the quality, variation, and representativeness of the data used during the training process. The data serves as the foundation for the model to learn how to identify and label distinct topics. So, it is critical to include a diverse set of examples that represent the breadth of probable topics that the model may encounter. A dataset should ensure that the model can generalize effectively across domains, identifying patterns and themes.

Furthermore, the quality of the labeled data has a direct impact on the model’s capacity to produce correct predictions as it learns to correlate specific traits with specific themes. Some of my favorites are free and paid sources:

Sources

Here are some of the sources that you can leverage for data gathering. Each source is found reliable and helps ensure smoother data gathering.

1. NewsData.io: It is one of my favorite sources of getting news data with a very minimal charge. Newsdata.io is a News API that allows users to access updates from around the world. It compiles news from over 82018 news outlets that provide information in 89 languages and about 206 different nations. Over 100 million news articles from 2018 to the present are currently available on it.
2. TrackMyHashtag(TMH): It provides historical X data (previously Twitter) of any user, hashtag, or keywords in a suitable file format like CSV, XLSX, and JSON. TMH internal software analyzes the raw X data given by customers and provides lots of useful hashtag analytics free on a beautiful online dashboard. These analytics can be very useful in understanding the data and complexity of the task. Other than analytics TMH also provides sentiment of the post.
3. Kaggle: A wide variety of community-contributed datasets on subjects like text categorization, sentiment analysis, image classification, and many more.
4. UCI Machine Learning Repository: Frequently utilized by those working with machine learning, the UCI ML Repository is an archive of datasets, domain theories, and data generators. It covers a wide range of areas and contains text datasets for a variety of applications.
5. Reddit datasets: Some subreddits, such as r/datasets, allow users to share and request datasets. You can locate text data that has been provided by the community.
6. Other API: Various other platforms provide their API to extract data from their website but they are costly for example X, Yelp, Facebook, Instagram, etc.

Data Preprocessing

Now, that you have the data, the first and foremost thing to do in ML is to do preprocessing. In tasks like topic labeling, data preprocessing is especially important and is a critical stage in the machine learning pipeline. The quality of labeled data has a direct impact on ML model performance, and adequate data preparation is required to guarantee that the input data is appropriate for the training algorithms. While doing data processing you can do things like text cleaning, stopword removal, handling missing data, tokenization, etc.

Before moving to the next section, remember the rule, you have to spend your 70-80% of time in data processing only. There are no set rules, just remember if the model not working properly, in the majority of cases there is no issue with your training algorithm but with your data. So be ready to preprocess the data again after training the model, don’t get upset. If you are working with the news data and you want the news should be authentic so that your model provides a more realistic label, you should go with NewsData.io. It is cost-effective, reliable, and gives perfectly processed data.

Model Selection

In topic labeling, two approaches dominate the landscape: unsupervised and supervised techniques. These methods address many different situations and data availability, resulting in adaptable solutions for a broad spectrum of applications.

Unsupervised Techniques

Unsupervised learning is widely employed in a variety of disciplines, including data exploration, pattern recognition, and feature learning. It’s especially useful when working with huge and complex datasets where manually identifying data would be impractical or costly. However, measuring the effectiveness of models that use unsupervised learning might be more difficult than supervised learning because there are no clear measures based on labeled data.

Instead, evaluation frequently entails judging the quality of found patterns as well as their applicability to certain tasks. You can read more about unsupervised learning here. For the topic labeling task, you can use the following unsupervised algorithms:

1. Latent Dirichlet Allocation (LDA):

LDA has been used in a variety of disciplines, including document classification, recommendation systems, and information retrieval. It enables the uncovering of hidden subject matter in a vast collection of documents, making content analysis and comprehension easier.

Remember that, while LDA is a strong tool, it has limitations and assumptions. For example, it presumes that documents are collections of topics and that each topic is a collection of words. This may oversimplify the complex nature of real-world texts, and the quality of outcomes may be dependent on suitable parameter adjustment and input data preprocessing.

2. Non-negative Matrix Factorization (NMF):

In contrast to typical matrix factorization methods, NMF requires that the factorized matrices contain only non-negative elements, making the results more interpretable in a variety of scenarios.

NMF can be used to decompose a document-term matrix given that rows represent documents, columns represent terms, and values represent the frequency of terms in documents. The first lower-dimensional matrix represents the documents in terms of topics, while the second lower-dimensional matrix represents the terms in terms of topics.

NMF limitations in NLP include sensitivity to initialization, fixed rank requiring predetermined topics, sparsity leading to potential overfitting, and a lack of consideration for word order and orthogonality.

3. Word Embeddings (Word2Vec, GloVe):

Word embeddings are vector representations of words that capture semantic relationships between words based on their context in a given corpus. Both Word2Vec and GloVe embeddings have been pre-trained on large corpora and can be used as features for downstream NLP tasks or fine-tuned for specific applications.

In practice, researchers and practitioners often choose between Word2Vec and GloVe based on their specific needs, the characteristics of their data, and computational considerations.Word2Vec and GloVe, while powerful, have limitations for topic labeling.

Challenges include context insensitivity, struggles with rare terms, difficulty distinguishing meanings, and static representations, limited-phrase understanding, corpus dependency, and neglect of word order nuances.

For making the model using an unsupervised learning technique, you should go with the data with less sequence length. You can check for any Kaggle dataset, but if you want to work on real-time datasets you can check TMH. With a little cost, you can data, analytics, and many more.

Supervised Techniques

Supervised learning is a machine learning paradigm where a model is trained on a labeled dataset, meaning that the input data is paired with corresponding output labels. The goal is for the model to learn a mapping between the input data and the desired output so that it can make predictions on new, unseen data.

For tasks like topic labeling Deep Learning technique suits the best, no other techniques would be able to give the desired result. We all know that, recently, the dynamics in ML, specifically in NLP have transformed so much. This is all because of the one architecture i.e., transformer. So, if you have a labeled dataset, I think you can directly go with a pre-trained transformer-based model like the Bidirectional Encoder Representations from Transformers (BERT) and many other transformer-based models, you can check it out from Hugging Face.

Neural Networks: Deep learning techniques, such as neural networks, have become increasingly popular for NLP tasks, including topic classification. Models like recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformers (e.g., BERT) have shown state-of-the-art performance on a wide range of NLP tasks.

Annotation of the Data

Annotating unlabelled text data involves the process of adding labels or tags to the text to create a labeled dataset for training machine learning models. You can do annotation either by yourself or by using the Large Language Model (LLM) and take the human in a loop such that he/she can correct the mistakes by the model. For manual annotation choose an annotation tool that suits your needs. There are various tools available, ranging from simple spreadsheet software to more specialized annotation platforms like Prodigy, Labelbox, Label Studio, or DagsHub.

If you want to go for annotation using LLMs you can use OpenAI API to use models like GPT-3, GPT-4, etc, or use free available models like LLAMS, Mistral, etc. The issue with these model is you have to test different prompt such that the model understand what you want to know. You can explore a library called guidance, it can help you in writing more advanced prompts and gives you extra control over models. To improve the quality of annotation you can also use the concept of active learning, where you only annotate the sentences that your model finds more uncertain (but for this you have to train the model in balanced dataset first). Remember this is the most important part since the performance of your model directly depends on this, so keeping human feedback is necessary.

Improving the Model: Quick Tips

Improving the performance of a topic labeling model involves refining the model architecture, fine-tuning parameters, and optimizing the preprocessing steps. Here are some strategies to enhance the performance of models commonly used for topic labeling, including Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), word embeddings, and transformer-based models like BERT:

1. LDA:

• Optimize the number of topics.
• Fine-tune hyperparameters (alpha, beta).
• Preprocess text data by removing stop words and handling rare words.

2. NMF:

• Experiment with several topics.
• Adjust regularization parameters.
• Conduct effective text preprocessing.

3. Word Embeddings:

• Experiment with different embedding techniques.
• Adjust embedding dimensionality.
• Fine-tune context window size.

4. Transformer-Based Models (e.g., BERT):

• Fine-tune pre-trained models.
• Adjust learning rates and batch sizes.
• Consider task-specific architecture modifications.
• Optimize tokenization and padding strategies.

General Tips:

• Evaluate performance using metrics like precision, recall, F1 score, or topic coherence.
• Explore ensemble models for improved robustness.
• Use data augmentation techniques.
• Apply regularization to prevent overfitting.
• Incorporate domain-specific knowledge.
• By iteratively implementing these strategies, you can enhance the performance of topic labeling models across a range of techniques.

Conclusion

Effectively approaching topic labeling involves meticulous steps from data gathering to model improvement. The choice between unsupervised and supervised techniques, such as LDA, NMF, word embeddings, and transformer-based models, depends on data characteristics and task requirements. Data quality is paramount, emphasizing diverse sources. To get reliable data, and to skip those frustrating small-small preprocessing steps, you should go with NewsData.io.

For supervised methods, while using transformer architecture like BERT, it need generous amount of labeled datasets. Labelling, whether manual or assisted by LLM, requires precision. Model refinement involves parameter tuning, preprocessing optimization, and task-specific adjustments. Continuous evaluation, exploration of ensemble models, and incorporation of domain knowledge contribute to the iterative enhancement of topic labeling models.