Introduction

This tutorial introduces Text Analysis (see Bernard and Ryan 1998; Kabanoff 1997; Popping 2000), i.e. computer-based analysis of language data or the (semi-)automated extraction of information from text. Text Analysis is associated with a divers set of computational methods that enable researchers to explore text and analyse unstructured data, i.e., text (unstructured is used here in contrast to structured, i.e., tabular data). Due to the increasing availability of large amounts of textual data, text analytics methods and distant reading techniques are becoming more and more important and relevant to a larger body of researchers and disciplines.

This tutorial introduces basic concepts of Text Analysis and Distant Reading. The aim is not to provide a fully-fledged analysis but rather to discuss and explore selected useful methods associated with text analysis and distant reading.

What is Text Analysis?

Since Text Analysis extracts and analyses information from textual data, it can be considered a derivative of computational linguistics or an application of Natural Language Processing (NLP). As such, Text Analysis represents the application of computational methods in the humanities and thus falls within computational humanities research.

The advantages of Text Analysis include:

• Extraction of information from large textual data sets

• Replicability and reproducibility of analyses

What is relevant to consider here is that Text Analysis contrasts with traditional or close-reading techniques which do not employ computational means of exploring and analyzing texts. Text Analysis, while allowing for qualitative analysis, builds upon quantitative information, i.e. information about frequencies or conditional probabilities.

Distant Reading is a cover term for applications of Text Analysis that allow to investigate literary and cultural trends by analyzing large amounts of textual data. close reading refers to reading texts in the traditional sense. Text Analysis and distant reading are similar with respect to the methods that are used but with different outlooks. The outlook of distant reading is to extract information from text without close reading, i.e. reading the document(s) itself but rather focusing on emerging patterns in the language that is used.

Text Analysis is rapidly gaining popularity in the humanities because textual data is readily available and because computational methods can be applied to a huge variety of research questions. The attractiveness of computational text analysis based on digitally available texts and in their capability to provide insights that cannot be derived from close reading techniques.

While there are some nuances, Text Mining, Text Analytics, and Distant Reading are more or less synonymous with Text Analysis. Regarding these minor differences, Text Analysis is commonly considered more qualitative while Text Analytics is considered to be quantitative. In contrast to Text Analysis, Text Mining is often more data-driven and usually applies methods without substantive supervision or assistance from the human researcher. Distant Reading is used mostly when dealing with literary or academic texts while Text Mining is associated with social media or more generally Big Data. In the following, we use Text Analysis as a cover term encompassing Text Mining, Text Analytics, and Distant Reading.

While rapidly growing as a valid approach to analyzing textual data, Text Analysis is critizised for lack of "quantitative rigor and because its findings are either banal or, if interesting, not statistically robust (see here. This criticism is correct in that most of the analysis that performed in Computational Literary Studies (CLS) are not yet as rigorous as analyses in fields that have a longer history of computational based, quantitative research, such as, for instance, corpus linguistics. However, the practices and methods used in CLS will be refined, adapted and show a rapid increase in quality if more research is devoted to these approaches. Text Analysis simply offers an alternative way to analyze texts that is not in competition to traditional techniques but rather complements them.

So far, most of the applications of Text Analysis are based upon a relatively limited number of key procedures or concepts (e.g. concordancing, word frequencies, annotation or tagging, parsing, collocation, text classification, Sentiment Analysis, Entity Extraction, Topic Modeling, etc.). In the following, we will explore these procedures and introduce some basic tools that help you perform the introduced tasks.

Tools versus Scripts

It is perfectly fine to use tools for the analyses exemplified below. However, the aim of LADAl is not primarily to show how to perform text analyses but how to perform text analyses in a way that complies with practices that guarantee sustainable, transparent, reproducible research. As R code can be readily shared and optimally contains all the data extraction, processing, visualization, and analysis steps, using scripts is preferable over using (commercial) software.

In addition to being not as transparent and hindering reproduction of research, using tools can also lead to dependencies on third parties which does not arise when using open source software.

Finally, the widespread use of R particularly among data scientists, engineers, and analysts reduces the risk of software errors as a very active community corrects flawed functions typically quite rapidly.

Glossary of Important Concepts

Below, you will find explanations of concepts and methods that are important in Text Analysis and also links to relevant resources (including LADAL tutorials).

Word

What a word is is actually very tricky. For instance, How many words are there in this sentence?

The cat sat on the mat One answer is that there are six words; that is, there are six groups of characters which are separated according to typographical convention. But there is another answer: There are five words, that is five distinct sequences of characters and one of those sequences (the) occurs twice. The terms commonly used to make this distinction are type and token. Tokens are instances of types, therefore if we count tokens, we count without considering repetition, while if we count types, we do consider repetition. In our example, there are five types (the, cat, sat, on, mat) but six tokens, because there are two tokens of one of the types (the).

There is a further distinction we may need to make which we can see if we consider another question: are cat and cats the same word? They are distinct types, and therefore must also be distinct as tokens. But we have an intuition that at some level they are related, that there is some more abstract item which underlies both of them. This concept is usually referred to as a lemma.

Concordancing

In Text Analysis, concordancing refers to the extraction of words from a given text or texts (Lindquist 2009). Commonly, concordances are displayed in the form of key-word in contexts (KWIC) where the search term is shown with some preceding and following context. Thus, such displays are referred to as key word in context concordances. A more elaborate tutorial on how to perform concordancing with R is available here. If you do not want to use coding to extract concordances, a highly recommendable tool for extracting concordances (and many other TA tasks) is AntConc.

Concordancing is helpful for seeing how the term is used in the data, for inspecting how often a given word occurs in a text or a collection of texts, for extracting examples, and it also represents a basic procedure and often the first step in more sophisticated analyses of language data.

Corpus (pl. Corpora)

A corpus is a machine readable and electronically stored collection of natural language texts representing writing or speech chosen to be characteristic of a variety or the state of a language (see Sinclair 1991). Corpora are great for extracting examples of natural examples and testing research hypotheses as it is easy to obtain information on frequencies, grammatical patterns, or collocations and they are commonly publicly available so the research results can be contrasted, compared and repeated.

There are four main types of corpora:

1. Monitor corpora: large collections of texts from different genres/modes that aim at representing a language or language variety, e.g., International Corpus of English (ICE), Corpus of Contemporary Corpus of American English (COCA), that are, e.g., used to analyse the use of certain linguistic phenomena or to investigate collocations of certain words/topics

2. Learner corpora: Contain data from language learners - these can be either L1 learners, e.g., Child Language Data Exchange System (CHILDES), and/or L2 learners, e.g., the International Corpus of Learner English (ICLE)) - to study, e.g., how L1 and/or L2 speakers learn/acquire (aspects of) a language and to see how learners differ from native speakers.

3. Historical or diachronic corpora: Contain data from different points in time that allow to analyse the development of a language or language variety (e.g., Penn Parsed Corpora of Historical English,The Helsinki Corpus of English Texts) to study, e.g., how language changes or how genres develop over time.

4. Specialized corpora: Contain data representing a specific genre/text type (e.g., British Academic Written English Corpus (BAWE)) to study, e.g., (linguistic) features of a genre (e.g. academic writing) or language in class rooms.

Collocations

Collocations are words that are attracted to each other (and that co-occur or co-locate together), e.g., Merry Christmas, Good Morning, No worries, or Fuck off. Collocations are important because any word in any given language has collocations, i.e., others words that are attracted to that word or words that that word is attracted to allow us to anticipate what word comes next and collocations are context/text type specific. It is important to note that collocations to not have to appear/occur right next to each other but that other words can be in between. There are various different statistical measures are used to define the strength of the collocations, like the Mutual Information (MI) score and log-likelihood (see here for an over view of different association strengths measures).

Document Classification

Document or Text Classification (also referred to as text categorization) generally refers to process of grouping texts or documents based on similarity. This similarity can be based on word frequencies or other linguistics features but also on text external features such as genre labels or polarity scores.

Document-Term Matrix

Document-Term Matrices (DTM) and Term- Document Matrices (TDM) contain the frequencies of words per document. DTM and TDM differ in whether the words or the documents are represented as rows. Thus, the words (terms) are listed as row names and the documents represent the column names while the matrix itself contains the frequencies of the words in the documents.

Frequency Analysis

Frequency Analysis is a suit of methods which extract and compare frequencies of different words (tokens and/or types), collocations, phrases, sentences, etc. These frequencies are the often tabulated to show lists of words, phrases, etc. descending by frequency, visualized to show distributions, and/or compared and analyzed statistically to find differences between texts or collections fo texts.

Keyword Analysis

Keyword Analysis refers to a suit of methods that allow to detect words that are characteristic of on text or collection of texts compared to another text/collection of texts. There are various keyness measures such as Log-Likelihood or the term frequency–inverse document frequency (tf-idf).

Lemma (Lemmatization)

Lemma refers to the base form of a word (example: walk, walked, and walking are word forms of the lemma WALK). Lemmatization refers to a annotation process in which word forms are associated with their base form (lemma). Lemmatization is a very common and sometimes useful processing step for further analyses. In contrast to stemming - which is a related process - lemmatization also takes into account semantic differences (differences in the word meaning), while stemming only takes the orthography of words into consideration.

N-Gram

N-grams are combinations/sequences of words, e.g. the sentence I really like pizza! has the bi-grams (2-grams): I really, really like, and like pizza and the tri-grams (3-grams) I really like and really like pizza. N-grams play an important part in natural language processing (e.g. part-of-speech tagging), language learning, psycholinguistics models of language production, and genre analysis.

Natural Language Processing

Natural Language Processing (NLP) is an interdisciplinary field in computer science that has specialized on processing natural language data using computational and mathematical methods. Many methods used in Text Analysis have been developed in NLP.

Network Analysis

Network Analysis is the most common way to visualize relationships between entities. Networks, also called graphs, consist of nodes (typically represented as dots) and edges (typically represented as lines) and they can be directed or undirected networks.

In directed networks, the direction of edges is captured. For instance, the exports of countries. In such cases the lines are directed and typically have arrows to indicate direction. The thickness of lines can also be utilized to encode information such as frequency of contact.

Part-of-Speech Tagging

Part-of-Speech (PoS) Tagging identifies the word classes of words (e.g., noun, adjective, verb, etc.) in a text and adds part-of-speech tags to each word. There are various part-of-speech tagsets, e.g. the Penn Treebank is the most frequently used tagset used for English. A more detailed tutorial on how to perform part-of-speech tagging in R can be found here.

Project Gutenberg

The Project Gutenberg is a excellent resource for accessing digitized literary texts. The Project Gutenberg library contains over 60,000 ebooks that are out of copyright in the US. A tutorial on how to download texts form the Project Gutenberg library using the GutenbergR package can be found here.

Regular Expression

Regular Expressions - often simply referred to as regex - are symbols or sequence of symbols utilized to search for patterns in textual data. Regular Expressions are very useful and widely used in Text Analysis and often different programming languages will have very similar but slightly different Regular Expressions. A tutorial on how to use regular expression in R can be found here and here is a link to a regex in R cheat sheet.

Semantic Analysis

Semantic Analysis refers to a suit of methods that allow to analyze the semantic (semantics) fo texts. Such analyses often rely on semantic tagsets that are based on word meaning or meaning families/categories. Two examples of such semantic tagsets are the URCEL tagset and the Historical Thesaurus Semantic Tagger (Alexander and Wattam 2015) developed at the University of Glasgow.

Sentiment Analysis

Sentiment Analysis is a computational approach to determine if words or texts are associated with (positive or negative) polarity or emotions.Commonly, sentiments analyses are based on sentiment dictionaries (words are annotated based on whether they occur in a list of words associated with, e.g., positive polarity or emotion, e.g., fear, anger, or joy. A tutorial on how to perform sentiment analysis in R can be found here.

String

In computational approaches, a string is a specific type of data that represents text and is often encoded in specific format, e.g., Latin1 or UTF8. Strings may also be present in other data types such as lists or data frames. A tutorial on how to work with strings in R can be found here.

Term Frequency–Inverse Document Frequency (tf-idf)

Term Frequency–Inverse Document Frequency is a statistical measure of keyness which reflects how characteristic a word is of a specific text. Term Frequency–Inverse Document Frequency is based on the frequencies of words in a text compared to the frequency of documents in which it occurs

Topic Modeling

Topic modelling is a machine learning method seeks to answer the question: given a collection of documents, can we identify what they are about?

Topic model algorithms look for patterns of co-occurrences of words in documents. We assume that, if a document is about a certain topic, one would expect words that are related to that topic to appear in the document more often than in documents that deal with other topics. Topic model commonly use Latent Dirichlet Allocation (LDA) to find topics in textual data.

There are two basic types of Topic models

• supervised or seeded topics models where the researchers provides seed terms around which the LDS looks for topics (collections of correlating terms)

• unsupervised or unseeded topic models which try to find a predefined number of topics (collections of correlating terms)

A tutorial on how to work with strings in R can be found here.

Text Analysis at UQ

As LADAL has been established at The University of Queensland, we have listed selected resources on Text Analysis offered by UQ.

The UQ Library offers a very handy and attractive summary of resources, concepts, and tools that can be used by researchers interested in Text Analysis and Distant Reading. Also, the UQ library site offers short video introductions and addresses issues that are not discussed here such as copyright issues, data sources available at the UQ library, as well as social media and web scaping.

In contrast to the UQ library site, the focus of this introduction lies on the practical how-to of text analysis. this means that the following concentrates on how to perform analyses rather than discussing their underlying concepts or evaluating their scientific merits.

Citation & Session Info

Schweinberger, Martin. 2022. Introduction to Text Analysis. Brisbane: The University of Queensland. url: https://slcladal.github.io/introta.html (Version 2022.09.13).

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author = {Schweinberger, Martin},
title = {Introduction to Text Analysis},
year = {2022},
organization = "The University of Queensland, Australia. School of Languages and Cultures},
edition = {2022.09.13}
}
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## Running under: Ubuntu 22.04.1 LTS
##
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## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
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References

Alexander, Baron, M., and S. Wattam. 2015. “The Historical Thesaurus Semantic Tagger.”

Bernard, H Russell, and Gery Ryan. 1998. “Text Analysis.” Handbook of Methods in Cultural Anthropology 613.

Kabanoff, Boris. 1997. “Introduction: Computers Can Read as Well as Count: Computer-Aided Text Analysis in Organizational Research.” Journal of Organizational Behavior, 507–11.

Lindquist, Hans. 2009. Corpus Linguistics and the Description of English. Edinburgh: Edinburgh University Press.

Popping, Roel. 2000. Computer-Assisted Text Analysis. Sage.

Sinclair, John. 1991. Oxford: Oxford University Press.