A Beginner’s Guide to Topic Modeling NLP

Discover how Topic Modeling with NLP can unravel hidden information in large textual datasets. | ProjectPro

A Beginner’s Guide to Topic Modeling NLP
 |  BY Simran

Companies are always on the lookout for enhancing customer experience by incorporating their feedback regularly. But, many customers often leave their feedback on social media and that is usually overlooked by the companies. What if we told you with topic modeling in NLP, you can easily access that feedback and leverage? Yes, it’s true! With topic modeling NLP, you can identify the sentiments of different users and then leverage it for enhancing your products/services. 


NLP Project on LDA Topic Modelling Python using RACE Dataset

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One of the most intriguing and crucial domains of Artificial Intelligence is Natural Language Processing (NLP). NLP is a scientific technique for training a system to comprehend human language, enabling interaction via language (including text and speech). Natural Language Processing (NLP) is a field of machine learning related to interactions between human language and computers.NLP is used in text processing mainly, and many kinds of tasks are made easier using NLP. For example, Chatbots, Virtual Assistants, Autocorrection, Speech Recognition, Language translators, Social media monitoring, Hiring and recruitment, Email filtering, Sentiment Analysis, etc.  One of the key challenges in NLP is to uncover the hidden patterns and structures in large volumes of textual data. This is where topic modelling in NLP comes into picture as it helps automatically discover the underlyins topics or themes in a corpus of text. Let’s enter the fascinating world of Topic Modelling NLP to explore some applications and key techniques used.

Natural Language Processing

What is Topic Modelling in NLP ?

It is the scientific technique of recognizing the related words from the topics present in the document or the corpus of data. It makes the task of extracting words from a document more easily. In topic modeling, the algorithms refer to a collection of statistical and Deep Learning methods for identifying latent semantic structures in collections of documents.

In this blog, we will cover various topic modelling methods, ranging from traditional algorithms to more contemporary methods based on deep learning and machine learning algorithms. The blog would also give a delineated introduction to these text analysis techniques and contrast their benefits and drawbacks in real-world scenarios. Additionally, it consists of Python code examples for understanding the implementation of machine learning models and getting a concise understanding of topic modeling techniques.

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Applications of Topic Modeling in NLP

Topic modeling has a wide range of applications, including:

  • Information Retrieval: You would have heard of the term 'Information Retrieval' in computer science, particularly in the context of search engines. It is incorporated into various text-processing rule-based systems to extract topics from text input and retrieve relevant information.

  • Document Clustering: Topic modeling can be used to group similar documents together based on the topics they contain. It is useful in a range of applications such as news aggregation, online discussion forums, and social media analysis.

  • Content Recommendation: Topic modeling can be used to identify the topics that a user is interested in and recommend content that matches those topics. This is useful in a various applications, such as content personalization on websites, e-commerce product recommendations, and news article recommendations.

  • Sentiment Analysis: Topic modeling can be used to identify the sentiment of a document or a section of text. By identifying the topics discussed in the text and the sentiment associated with each topic, we can better understand the overall sentiment of the document.

  • Trend Analysis: Topic modeling can be used to identify the topics that are currently trending in a given domain or industry. This can be useful in various applications such as market research and news analysis.

  • Keyword Extraction: Topic modeling can be used to identify the most important keywords in a document or a section of text. This is useful for tasks such as search engine optimization (SEO), information retrieval, and content analysis.

Besides these popular applications, topic modeling is also widely used in software engineering and bioinformatics domains for extensive research and analysis of documents. Let us now dive into different methods used for topic modeling.

Topic Modeling Methods in NLP

Various topic modeling algorithms perform topic modeling using natural language processing after the data preprocessing has been completed.

In this sample NLP project, we have used the Latent Dirichlet allocation (LDA) model in Python on the RACE dataset, which has odd 25000 documents where words are of different natures, such as nouns, Adjectives, Verbs, Prepositions, and many more. Even the length of documents varies vastly from having a minimum number of words in the range of around 40 to a maximum number of words in the range of around 500.

Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (pLSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF) are traditional and well-known approaches to topic modeling. They represent documents as vocabularies and assume that each document is a mixture of different themes. They all start by converting a text corpus into a Document Term Matrix (DTM). 

DTM is a table where each row is a document, and each column represents a unique word. The entries in the matrix represent the frequency of each term in each document. Each cell contains count i and the number of times word j occurs in document i. A popular alternative to word count is the TF-IDF score which considers word frequencies in a bag of words.We consider term frequency (TF) and inverse document frequency (IDF) to penalize the weight of terms that frequently occur in the corpus and increase the weight of rare or irrelevant terms.

The basic principle behind searching for latent topics is decomposing the DTM into document topics and a topic concept matrix. The various topic modeling methods differ in how they define and achieve this goal, let us explore a few popular approaches in detail.

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Latent Semantic Analysis or Latent Semantic Indexing (LSA)

Latent Semantic Analysis (LSA) is one of the basic topic modeling techniques. The core idea is to take the matrix of things we have - "Documents and Concepts" - and break it down into separate Documents - Topic Matrix and Topic - Concept Matrix.

The first step is to generate a Document Term Matrix (DTM). If you have m documents and n words in your vocabulary, you can create an m × n matrix A. Here each row represents a document, and each column represents a word. In the simplest version of LSA, each entry is a rough count of the number of times the jth word occurs in the ith document.

In practice, however, raw counts don't work well because they don't take into account the meaning of every word in the document. Therefore, LSA models typically replace the raw counts of the DTM with TF-IDF scores. TF-IDF or term frequency-inverse document frequency assigns a weight to term j in document i as follows:

Weight term for Topic Modeling with LSA

The more frequently a word appears in the document, the more intuitive weight it has, and the less frequently it occurs in the corpus, the more weight it has. 

Although LSA is fast and efficient to use, it has some significant drawbacks like a lack of interpretable embeddings (we don't know what the subject is, and the components can be arbitrarily positive/negative), a lot of documentation is required, and vocabulary to get accurate results.

LSA finds representations of documents and words in low dimensions. The dot product of row vectors is document similarity, and the dot product of column vectors is word similarity.

Apply truncated singular value decomposition (SVD) to reduce the dimension of X. Then U ∈ ℝ^(m ⨉ t) is the document topic matrix, and V ∈ ℝ^(n ⨉ t) is the term topic matrix. In both U and V, a column corresponds to one of the t topics. In U, rows represent document vectors expressed in terms of topics. In V, rows represent concept vectors expressed in terms of topics.

Below snippets of code depict the process of how latent semantic analysis is done, starting with importing necessary dependencies in Python, extracting features from many documents, and then segregating the words into a particular topic. 

The code was implemented on a train data of News Dataset with many documents consisting of news headlines of various topics and the same topic. This sample NLP project uses the RACE dataset similar to the training data with questions and answers.

The two main sub-libraries that perform topic modeling in the latent semantic analysis (LSA) algorithm are TfidfVectorizer and TruncatedSVD.

Code for Topic Modeling using LSA- Input Documents

Code for Topic Modeling using LSA- Implementing LSA

Code for Topic Modeling using LSA- Output Document Term Matrix

As can be inferred from the code snippets, the terms have been classified, and topic modeling has been done to get document vectors and term vectors. Word correlations and the respective word frequency play a significant role in identifying topics for a group of words with the same meaning.

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Latent Dirichlet Allocation (LDA)

LDA stands for Latent Dirichlet Allocation. LDA is a Bayesian version of pLSA (which we will discuss later). In particular, it can be generalized better by using Dirichlet priority for the distribution of document topics and word topics.

From the Dirichlet distribution Dir(α), we draw a random sample representing the distribution of topics or a mixture of topics in a given document. This topic distribution is θ. From θ, we choose a particular target Z based on its distribution. We then choose a random sample representing the word distribution of topic Z from another Dirichlet distribution Dir(𝛽). This word distribution is φ. Next, we choose a word w from φ.

From the selected mixture θ, we draw the target z based on the distribution. From the bottom, we see β, the parameter of the Dirichlet prior distribution for the word distribution by topic. From the Dirichlet distribution Dir(𝛽), we choose a sample representing the word distribution φ for topic z. Then draw the word w from φ.

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Finally, we are interested in estimating the probability of topic z given document d and parameters α and 𝛽, i.e., P(z|d, α, 𝛽). The problem is formulated as computing the posterior probability distribution of the hidden variables given in the documents.

Formula for Topic Modeling in NLP using LDA

If the document collection is large enough, LDA as a topic modeling algorithm will detect sets of such terms (i.e., topics) based on common occurrences of individual terms in a particular document, but the task of assigning meaningful labels to single topics is finished. Special knowledge is often required (e.g., gathering technical documentation). It is a more effective topic modelling NLP algorithm in terms of its machine learning performance than LSA and pLSA.

Latent Dirichlet allocation implementations for topic modeling are most commonly in the Gensim and sklearn packages (in Python). These packages are popularly leveraged for topic classification, keyword extraction from document cover, and other machine-learning algorithms.

The most important tuning parameter for the LDA model is n_components (the number of topics). Also, we look for learning_decay (which controls the learning rate).

Besides these, other possible search parameters are learning_offset (lower initial iterations, should be > 1) and max_iter given that you have enough computing resources. Grid Search creates multiple LDA models for every possible combination of param values ​​in the param_grid dict. As such, this process can consume a lot of time and resources.

Implementing LDA in Python for Topic Modeling

Non-Negative Matrix Factorization (NMF)

NMF performs topic modeling by decomposing the DTM into a topic document matrix U and a topic term matrix Váµ—. This is similar to SVD, with the additional restriction that U and Váµ— can only contain non-negative elements, thereby improving the clarity in mathematical factorization for topic models.

In this sample NLP project, TF-IDF Vectorizer is fitted and transformed on clean tokens, 13 topics are extracted, and the number is found using the Coherence Score. However, the decomposition becomes more difficult due to the non-negativity constraints and could lead to inappropriate topics. Like LDA, even NMF requires preprocessing, and finding an optimal number of topics is complex using NMF topic modeling tools.

Probabilistic latent semantic analysis - pLSA

PLSA evolved from the LSA but focuses more on subject relationships within documents. It's a statistical method for analyzing bimodal and co-occurrence data, applied to information retrieval and filtering, NLP, machine learning from text, and related fields.

When compared to LSA, pLSA shows better performance. However, probabilistic models at the document level must be provided, implying that the number of parameters grows linearly with the number of documents, leading to scalability and overfitting issues.

Probabilities cannot be assigned to new documents. Thus, LDA is a more effective topic model algorithm than pLSA.

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Pachinko Allocation Model (PAM)

It is an improved version of the latent Dirichlet Allocation model. It is well known that LDA identifies topics and emphasizes correlations between words in a text corpus as a correlated topic model. Pachinko's allocation model, on the other hand, is improvised by establishing correlations between the generated themes. PAM emphasizes correlations between topics rather than between words, so it has a powerful ability to pinpoint semantic relationships.

Top Libraries used for Topic Modelling in NLP

In this sample NLP project, we have incorporated the Gensim library with an interactive tool pyLDAvis for graphically visualizing topic distribution and word importance within topics using latent variables on a trained model. Topics distribution is analyzed using the t-SNE algorithm and iterative tool using pyLDAvis.

The regular expressions re, gensim, and spacy are used to process the text. pyLDAvis and matplotlib are used for visualization. NumPy and Pandas are used for manipulating and displaying data in tabular form.

BERTopic is a topic modeling library in Python that leverages BERT embeddings (transformers) and class-based TF-IDF to build dense clusters. Class-based TF-IDF provides the same class vector for all documents of a single class.

In this sample NLP project, TF-IDF Vectorizer and Countvectorizer are fitted and transformed on a clean set of documents, and topics are extracted using sklearn LSA and LDA packages, respectively and proceeded with 10 topics for LDA and LSA algorithms.

Top 5 Topic Modelling NLP Project Ideas

Here are five exciting topic modeling project ideas:

1. Hot Topic Detection and Tracking on Social Media

Topic Modeling can be used to get the most commonly utilized keywords out of a bag of words (hot debatable topics) appearing in the news or social media posts.

2. Disease or Disorder Prediction Application from Patient Reports

This can be done by the appropriate topic model that can infer the corresponding disease from all the symptoms extracted from a set of document reports of a patient. Patients showing the same symptoms can be grouped into a cluster, and a topic (here, disease) can be allocated by the model.

3. Product Recommendation Engines and Systems

Topic Modeling can be used to analyze user preferences and curate a list of recommendations after identifying their topics of interests. For example, an e-commerce website that sells books can use topic modeling to identify the main topics present in the books, such as "romance," "mystery," "science fiction," and so on. And, then they can analyze user previous purchasing patterns and browsing history to suggest them books for their next purchase.

4. Exam Grading Systems

Topic Modeling can be used in exam grading systems to prepare a more detailed analysis of students’ performance. For example, consider of a large dataset of students’ exam responses in a literature course and the task at hand is to grade each student. The teacher can use topic modeling to identify which topics each response contains and assist them in grading the students in an unbiased way.

5. Document Clustering Systems

Topic Modeling can be used to group together similar documents based on the topics they cover. In particular, in fields such as bioinformatics, topic modeling can be used by researchers to group together genes or proteins that share similar functions or similar expression patterns. 

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Top 5 Topic Modelling Datasets to Explore

Below are a few intriguing datasets to explore for Topic Modeling in NLP:

1. Fashion 144K 

2. 20 Newsgroups

3. OpoSum

4. COVID-19 Twitter Chatter Dataset

5. OAGT (Paper Topic Dataset)

Master Topic Modelling in NLP by Building Projects

Now that you have understood the importance of Topic modeling for developing real-life applications, it is time to apply your knowledge and master this technique by building NLP projects and working hands-on!

It is strongly recommended to build NLP projects like chatbots, text summarizers, and recommender engines that deploy Topic Modeling methods. The projects that can be developed as a real-life use case of Topic Modeling rely on keyword extraction to get the context of information from a lot of data. And by doing this project on short text topic modeling in python, you would get a head start on your mastery of the NLP concepts.

Other advanced topic modeling methods like BERTopic and Top2Vec are recent. After the keyword extraction has been done, they perform topic modeling for tasks like topic detection, classification, and topic extraction from an entire corpus of documents. You could also start exploring them after gaining a thorough understanding of topic modeling using the LDA algorithm in Python, as shown in the sample NLP project implemented on the RACE dataset.

As a beginner in NLP, it is important to actively engage in practical experiences and apply your understanding of various concepts to real-world situations. It will help you to enhance your retention power and develop critical thinking and problem-solving skills. And in case you don’t know where to start, check out the ProjectPro repository of solved projects in Data Science and Big Data. It contains solutions to practical problems that will help you gain professional experience in the two domains.

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FAQs on Topic Modeling in NLP

1) What is the purpose of Topic Modelling?

Topic modeling is a part of NLP that is used to determine the topic of a set of documents based on the content and generate meaningful insights from the similar words in the entire corpus of text data, thereby performing documents-based contextual analysis to analyze the context.

An ongoing and pervasive issue in Data Science is the ability to automatically extract value from various sources without (or with little) a priori knowledge. Unsupervised machine learning techniques, like topic modeling, require less user input than supervised algorithms for text classification. This is because they don't require human training with manually tagged data.

2) What is an example of topic modelling?

An example of Topic Modeling is scanning through a set of news articles and labeling the important topics discussed. For example, consider an article that has been written on Climate change. One can use LDA to identify the primary topics discussed in the article. The algorithm will analyze the words and phrases and come up with topics such as ‘greenhouse gas emissions’, ‘rising sea levels’, ‘renewable energy’, and ‘policy solutions’.

 

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About the Author

Simran

Simran Anand is a Software Engineer graduated from Vellore Institute of Technology. Her expertise includes technical paradigms like Machine Learning, Deep Learning, Data Analytics and Competitive Programming. She has worked on various hands-on projects in the domains of Data Science, Artificial

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