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In Natural Language Understanding tasks, we can use a queue of lenses to extract significant meaning from words to sentences to paragraphs to documents. Being very precise, and if we talk about extracting the meaning or analyzing a document, it is by analyzing its topic. Topic Modelling refers to analyzing and extracting the meaning by examining the topic across a collection of documents.
By examining text usage patterns in various publications, topic modeling aims to identify latent subjects within a corpus of text. This entails locating words and phrases that commonly appear together in a single document and those that are uncommon or particular to that document.
A topic modeling method produces a list of topics, each represented by a list of the most closely related words. Researchers or analysts can then interpret the underlying themes and patterns within the text corpus.
Major applications of topic modeling include content analysis, trend analysis, sentiment analysis, and recommendation systems. For example, topic modeling can identify emerging trends and topics within social media discussions, monitor customer feedback and sentiment, and recommend related content to users based on their interests.
Content analysis, trend analysis, sentiment analysis, and recommendation systems are some of the major uses of topic modeling. Topic modeling, for instance, can be used to track consumer feedback and sentiment, identify new trends and subjects within social media discussions, and provide relevant material to users based on their preferences.
There are some conventional approaches like LDA, but these approaches do not capture the relationship between the words. Therefore, we will discuss one promising approach named Top2Vec, which addresses the drawback of other conventional models.
Top2Vec is an unsupervised ML technique that combines topic modeling and word embedding and creates an efficient model to assess text data. The aim is to identify the latent topic, and conventional topic modeling techniques mostly rely on the probabilistic model, but Top2Vec relies on vector-based clustering to similar cluster documents.
One of the key advantages of Top2Vec is its ability to handle large volumes of text data efficiently. Traditional topic modeling techniques often struggle with scalability as the number of topics and documents in the corpus increases. Top2Vec addresses this issue by using a hierarchical approach to clustering, allowing it to scale millions of documents easily.
Top2Vec also offers superior accuracy compared to traditional topic modeling techniques. This is due to its ability to capture the semantic meaning of words in the document rather than just their frequency of occurrence. This means that Top2Vec can identify subtle nuances in the text data which other techniques can miss.
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- Document preprocessing: The first step is to preprocess the documents in the corpus. This involves cleaning the text, removing stop words, stemming, and lemmatizing.
- Document embedding: Top2Vec generates a dense vector representation for each document in the corpus using pre-trained or user-trained word embeddings after the documents are preprocessed. These document embeddings capture the semantic meaning of the document and allow for accurate clustering.
- Dimensionality reduction: The document embeddings are then reduced to a lower-dimensional space using principal component analysis (PCA). This step reduces the clustering process’s computational complexity and helps identify the most important features in the data.
- Clustering: Top2Vec uses a hierarchical clustering algorithm to group similar documents into clusters or topics. Clustering is performed using cosine similarity as the distance metric between document embeddings.
- Topic extraction: Top2Vec extracts topics from the clusters after the clustering process. The topic extraction is based on word co-occurrence and cluster density. The algorithm identifies the most representative words for each cluster, which are used to summarize the topics.
- Topic ranking: Top2Vec ranks the topics based on their relevance to the entire text corpus. This is done using a combination of topic coherence and cluster size. The most coherent and representative topics are ranked higher than the less relevant ones.
Advantages and Disadvantages
Advantages of Top2Vec:
- Scalability: Top2Vec is highly scalable and can easily handle large volumes of text data.
- Accuracy: Top2Vec uses a vector-based clustering approach that captures the semantic meaning of the words in the document, allowing it to identify subtle nuances in the text data
- Efficiency: Efficient in both time and memory usage.
- Flexibility: Top2Vec can be used for various tasks on text data, including topic modeling, document clustering, and document similarity analysis.
- Interpretable: Top2Vec produces interpretable results, making it easy for users to understand and interpret the topics extracted from the text data.
Disadvantages of Top2Vec:
- Computational requirements: Top2Vec requires significant computational resources, particularly when dealing with large text datasets.
- Limited interpretability: While Top2Vec produces interpretable results, the interpretability of the model may be limited in some cases. For example, the model may struggle to identify topics with a limited vocabulary in specific or technical domains.
- Dependence on pre-trained word embeddings: Top2Vec relies on pre-trained word embeddings for generating document embeddings. This means that the quality of the document embeddings depends on the quality of the pre-trained word embeddings used. Training custom word embeddings may sometimes be necessary to achieve optimal performance.
This blog briefly explains the Top2Vec model used for the topic modeling. It is a powerful technique that provides promising results compared to conventional topic modeling techniques as it tries to understand the relationship. I hope you found this blog informative and useful as well.
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Drop a query if you have any questions regarding Top2Vec and I will get back to you quickly.
1. What are the other Topic Modelling Techniques available?
ANS: – Some of the techniques are listed below:
- Latent Dirichlet Allocation (LDA).
- Non-negative Matrix Factorization (NMF).
- Probabilistic Latent Semantic Analysis.
2. What is Word Embedding?
ANS: – Representing the words in the numerical format is known as Word Embedding so that compute can analyze and process the text data. Here word embedding transforms the text into vectors to capture the semantic meaning and the relationships.
3. Is it required to remove stop words before using Top2Vec?
ANS: – As stop words appear in nearly all documents within the corpus, they are equidistant from all topics and do not emerge as the closest words to any specific topic.
WRITTEN BY Parth Sharma