In the era of big data, businesses grapple with massive datasets that are often unstructured, making the analysis and processing of information a challenging endeavor. Keyword extraction emerges as a crucial automated process, unveiling the most relevant words and expressions from text, and it plays a pivotal role in leveraging existing business data.
For a collection of texts, such as a blog, keywords can serve as tags. Why tagging textual data is important? Because tagging is a critical aspect of enhancing the organization and accessibility of content on websites. When you create posts or articles, incorporating keywords as tags is akin to annotating data, providing crucial metadata that improves the overall searchability and categorization of your content. It goes beyond merely identifying key terms; tagging serves as a structured system for classifying and linking related information.
In cases where numerous posts lack tags (i.e. an archive of text files or documents) the importance of implementing a robust tagging strategy becomes evident. This process, analogous to annotating data, not only aids in content discovery but also facilitates seamless navigation for users seeking specific information. Keywords as tags can be stored efficiently using various formats such as YAML frontmatter or JSON, ensuring consistency and ease of retrieval.
Read Also How to automatically tag posts in Hugo Static Site Generator with Python
Ultimately, tagging is an indispensable practice, especially when dealing with a substantial volume of documents, as it empowers websites to organize keywords systematically, creating a well-structured and easily navigable repository of information.
Here are some key arguments highlighting the importance of using tags on websites:
Improved Searchability: Tags serve as a powerful tool for enhancing search functionality on websites. When users are searching for specific content, having well-organized tags facilitates quick and accurate retrieval of relevant information.
Enhanced User Experience: Tagging contributes to a more user-friendly experience by allowing visitors to navigate seamlessly through related content. It enables them to explore topics of interest efficiently, promoting engagement and satisfaction.
Content Categorization: Tags act as a natural categorization system, helping to organize content into meaningful groups. This categorization makes it easier for both users and content creators to understand the context and relationships between different pieces of information.
SEO Benefits:
Meaningfully and properly tagged content can improve search engine optimization (SEO) by making it more likely for the website to appear in relevant search results. We are not simply talking about meta keywords tags, but of category pages that are automatically generated by most CMS’s and serve as indices for related content.
Structured Information: Tags provide a structured way to add metadata to content. This metadata not only aids in the organization of information but also facilitates data analysis, helping websites derive insights about popular topics and user interests.
Content Discoverability: Tags contribute to the discoverability of content, especially in cases where users may not be aware of specific keywords. By browsing through tags, users can stumble upon related content that aligns with their interests.
Consistent Data Management: Tags offer a standardized method for managing and organizing data. Whether stored in YAML frontmatter, JSON, or other formats, tags provide consistency in how information is labeled and retrieved across various documents.
Facilitation of Automation: Tags play a crucial role in automating processes such as content recommendation systems. By using tags, websites can implement automated features that suggest related articles or content based on users’ preferences and past interactions.
In a world where over 80% of daily-generated data is unstructured, businesses need automated keyword extraction to process and analyze customer data. As an example, this process reveals insights into what customers say, allowing to discern what they deem important. Questions like the percentage of customer reviews discussing pricing or user experience become answerable, aiding in the formulation of data-driven business strategies. Beyond business applications, keyword extraction proves invaluable in research and academia. It serves as the key to navigating through vast sets of data, such as articles, papers, or journals, enabling researchers to identify relevant keywords without the need to read entire content. Same is true for news sites and newspapers, documentation archives, and so on.
If you see a title and a list of tags that makes sense, you can immediately grasp what the text is about. So, keyword extraction and automatic tagging is an assistive technology to aid us, humans, nagivate in the oceans of texts. Incorporating tags into website content is a strategy that enhances searchability, categorization, and overall user experience, while also providing SEO benefits and facilitating structured data management.
This brings us to the next questions, namely: How to extract keywords automatically? and How to automatically map text content with keyword metadata? This brings us into the realm of text mining and Natural Language Processing (NLP). You need to be able to automatically mine meaningful keywords from textual data, so that you can efficiently tag texts. This is by no means a trivial task. A new technique is called the Negative Binomial Distribution method. It is widely used in ecology and (not so widely) in corpus linguistics.
But first, let us say few of words on what the state or art in keyword extraction.
There are severeral approaches to keyword extraction, and, as a result tag mapping.
Word Frequency: - Pros: Identifies recurrent terms efficiently. - Cons: Treats documents as a ‘bag of words,’ overlooking meaning nuances and dismissing synonyms. Stop list is necessary to filter off function or grammar words, pronouns, etc.
Word Collocations and Co-occurrences: - Pros: Understands semantic structures; identifies frequently occurring word pairs. Method of word n-grams. - Cons: May not consider non-adjacent but semantically related words; same as word frequency method but for several words, such as word pairs.
TF-IDF (Term Frequency-Inverse Document Frequency): - Pros: Measures word importance; widely used in search engines. - Cons: Relies on statistical metrics; may overlook word importance in single documents compared against the entire collection of texts.
Morphological or Syntactic Information: - Pros: Uses parts-of-speech, dependency grammar for keyword extraction. - Cons: Highly dependent on linguistic analysis; requires linguistic knowledge; typically a language-specific method.
Discourse Markers and Semantic Information: - Pros: Considers discourse organization and shades of meaning. - Cons: Requires additional semantic information; complexity in implementation.
Graph-based approaches, exemplified by the widely-used TextRank model, represent text as a graph with interconnected vertices. Words are treated as vertices connected by edges, either directed (one-way) or undirected (bidirectional, as in co-occurrence representations). The central idea is to measure the importance of vertices based on graph structure, often utilizing metrics like the degree of a vertex or the number of immediate vertices (neighborhood size). Once a graph is constructed, various methods determine vertex importance scores, guiding the extraction of keywords from the text.
Consider the text, «Automatic graph-based keyword extraction is pretty straightforward. A document is represented as a graph, and a score is given to each of the vertices in the graph. Depending on the score of a vertex, it might be chosen as a keyword.» Applying the neighborhood size measure in a graph of dependencies, the extracted keyphrase might be «automatic graph-based keyword extraction» due to the highest neighborhood size of the head noun «extraction.»
Machine learning, a subfield of artificial intelligence (AI), is integral to text analysis tasks, including keyword extraction. To make sense of unstructured text, machine learning systems transform it into vectors containing representative features. Various algorithms, such as Support Vector Machines (SVM) and deep learning, are employed for keyword extraction. Conditional Random Fields (CRF) stands out as a statistical approach within machine learning, considering contextual patterns and relationships between variables in a word sequence. While CRF enables the creation of complex patterns and generalization across domains, its use demands robust computational skills for feature weight calculations across word sequences.
If looking for an effecient way to mine texts for keywords, one should consider if keyword extraction method is reasonably easy to implement. In other words, the method should be comparably simple. While purely statistical methods offer such simplicity, linguistic approaches delve deeper into the intricacies of natural language but can work with a specific tongue. On the other hand, most successful systems integrate linguistic information, outperforming purely statistical ones. It’s advisable to explore a combination of approaches to extract the most relevant keywords from text collection and this is why we offer a statistical approach which is linguistically motivated.
Our approach is based on the estimation of negative binomial distribution (NBD) parameters for words within a collection of texts. This innovative method goes beyond conventional statistical techniques by focusing on words that exhibit properties of aggregation, representing «frequent rare events» within individual texts. On this site you will find a demonstration of results for the NBD technique of text mining.
By analyzing the distribution patterns of such words across a corpus, be it a collection of news articles, chapters of a book, or any knowledge base, the NBD method allows for the identification of keywords that carry significant meaning within the context of the entire dataset, entire website, entire domain of knowledge. This nuanced method offers a means of pinpointing keywords that hold relevance and significance within a broader linguistic landscape, providing valuable insights into the underlying themes and trends present in your textual archive and knowledge repositories.
Keywords extracted with the help of NBD method are meaningful in the context of all documents in your archive or website, so you can, indeed, use them in a traditional tag cloud, without the necessity to add our JS viewer to your site. The scripts to extract keywords, using
pythonandR, is available at our repo.
The NBD menthod is simple to implement; in the next posts we will show you how to do it, based on a real-life case of tagging a Hugo website with several thousand posts.
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