The Evolution of Text Analysis

Every day we’re surrounded by a large amount of texts whose semantic meaning we have to unravel. While the term “text analytics” might seem like a modern concept, the practice of analyzing texts for meaning and information is as old as writing itself.

In this article, we will try to explore what lies behind computation text analysis and how it can be applied to different industries.

Text analysis and classification before supercomputers

In the Middle Ages, scribes were responsible for copying and preserving manuscripts. They categorized texts based on subject matter, author or intended audience. However, these classifications were often subjective and varied between different libraries and scriptoria.

Manuscripts were handwritten before Gutenberg’s invention of the printing press and contained a wide range of topics, from religious texts to scientific treatises. Without a consistent framework, it was difficult to accurately classify and locate specific texts.

The Victorian era saw a significant increase in the production and dissemination of texts. As libraries grew, the need for efficient classification systems became even more urgent.

One of the most influential figures in the development of library classification systems was Melvil Dewey. His Dewey Decimal Classification system, introduced in 1876, provided a hierarchical system for organizing books based on subject matter. This system revolutionized library organization and made it easier to find specific books.

The Digital Age: The Rise of Computational Text Analysis

With the rise of computers and the digitization of vast amounts of text, new opportunities for text analysis have emerged.

Computational text analysis techniques, such as machine learning and natural language processing, have enabled us to analyze text data on a massive scale. These techniques allow us to identify patterns, extract information and generate insights that would be impossible to achieve through manual methods.

By analyzing the frequency of words and phrases, identifying named entities, and understanding the semantic relationships between words, computers can now classify and categorize texts with exceptional speed. The main computational text analysis methods include:

• Keyword Analysis
• Named Entity Recognition (NER)
• Sentiment Analysis
• Stylometry
• Topic Modeling
• Word Embedding Modeling

Keyword analysis

Keyword analysis is a fundamental text processing technique that involves identifying and extracting significant keywords from a given text. These keywords serve as the foundation for various applications, including document categorization, information retrieval, and metadata generation.

Keyword extraction algorithms use statistical properties of words to automatically identify key terms. By combining these techniques, keyword analysis enables the accurate and efficient extraction of meaningful information from textual data.

Named Entity Recognition (NER)

Named Entity Recognition (NER) involves identifying and classifying named entities within text, such as proper nouns (names, locations, etc.) This fundamental task underpins numerous text analysis applications, including information extraction and text summarization.

NER can be done in three main ways: using rule-based methods with predefined rules, machine learning methods that learn from labeled data, and advanced deep learning methods that use neural networks like RNNs and CNNs for the best results.

Sentiment Analysis

Sentiment analysis is the process of determining the sentiment expressed in a piece of text. It can be used to analyze customer reviews, social media posts, and other textual data to understand public opinion.

To get the best results, sentiment analysis employs lexicon-based methods with sentiment word lists, machine learning on labeled data, or deep learning with neural networks like RNNs and CNNs. You can read more about text sentiment analysis here.

Stylometry

Stylometry is a specialized field that looks into the analysis of linguistic style. By examining the writing style of an author, stylometry can be used to determine authorship, detect plagiarism, or identify stylistic changes over time. Techniques such as function word analysis, which analyzes the frequency of function words like prepositions and conjunctions, and n-gram analysis, which examines the frequency of word sequences, are commonly employed in stylometric studies.

Topic Modeling

Topic modeling is a text mining technique that aims to discover abstract “topics” inherent within a collection of documents. Topic modeling helps us understand the main themes in a collection of documents. It can group similar documents together.

Two common techniques for topic modeling are Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF). LDA treats documents as a mix of topics, and topics as a mix of words. NMF breaks down a document-term matrix into simpler matrices to identify topics.

Word Embedding Modeling

Word embedding models represent words as vectors, capturing their meanings and relationships. They have transformed text analysis, enabling tasks like translation and text generation that have gained widespread recognition throughout the last decade. Several methods include Word2Vec which uses neural networks to predict word contexts; GloVe which learns from word co-occurrence patterns; advanced models like BERT, based on transformers, now lead in many natural language processing tasks.

Employing NLP for text-related tasks

After having narrowed down the main methods behind text analytics, we can dive deeper into the tasks that Natural language processing can help achieve.

Text Extraction

Text extraction is the process of pulling specific information from unstructured text data. This involves identifying and extracting key entities like names, locations, organizations, or dates (we have already covered named entity recognition in the section above). In business and customer service, it can automate invoice and receipt processing, extract insights from customer feedback and summarize complex documents.

Text Summarization

Text summarization is the technique of condensing a large piece of text into a shorter version while preserving its key information. This can be achieved through extractive summarization, which selects and combines important sentences from the original text, or abstractive summarization, which generates new text that conveys the main ideas of the original text. It can summarize news articles, product reviews, customer interactions, help analyze financial reports and improve search engine optimization.

Text Generation

Text generation involves creating new text, such as autocompleting sentences or paragraphs, translating text from one language to another, or generating creative text formats like poems or scripts. Practical applications of text generation include powering chatbots and virtual assistants, automating content creation and aiding language learning.

Text Classification

Text classification is the process of assigning predefined categories or labels to text documents. This can involve sentiment analysis to determine the sentiment of a text, topic modeling to identify the main topics of a document, or intent recognition to understand the intent behind a user’s query. Text classification has applications in social media monitoring, email filtering and customer support.

Final remarks

As we have explored, text analysis has gone a long way from manual information classification to sophisticated techniques that involve computers. In the digital age, textual analytics can be simplified by employing Natural language processing to extract, summarize, generate texts and beyond.

The human-computer interaction opens up new opportunities and ways to automate tasks and helps implement numerous complex projects in different spheres from law to content creation.