Difference between Hamlet II and other text-based analytical software

Hamlet II is an approach to quantitative text-based analytical software. This article reviews the difference between them and Hamlet II. The table below presents various text-based analytical software available commercially.

Software Name
CatPac II Read any text and summarize its main ideas, needs no precoding or vocabulary list allows lemmatization and performs cluster analyses of textual data. MS Windows
Diction Computer-aided text analysis program for determining the tone of a verbal message reads a variety of text formats, e.g., HTML, docx, txt, pdf, uses predefined dictionaries, and analyses on semantic features. iOS and MS Windows
Leximancer Java program, analyse the content of collections of textual documents, uses three-level network model an algorithm based in Bayesian statistics to generate concept maps, automatically grouping words, i.e., character strings into suggested clusters of meaning. Java, Linux, iOS and Windows
T-LAB Flexible and transparent software environments for content analysis and text mining, and allows corpus normalization, multi-word and stop-word detection, segmentation into elementary contexts (i.e. sentences or paragraphs), automatic lemmatization or stemming (see the below table), and key-terms selection. MS Windows
Text Analyst Analyzing large volumes of textual information, a semantic network of a text completely autonomously, and do not use pre-dictionaries, allow thematic clusters, and also allows navigation, and search of unstructured texts. MS Windows

Features of Hamlet II

Some crucial features of Hamlet II are represented in the image below.

Figure 1: Features of Hamlet II as compared to other text-based analytical software

Figure 1: Features of Hamlet II as compared to other text-based analytical software

Hamlet II versus other text-based analytical software

  1. The most important difference is that Hamlet II allows a range of statistical models and statistical analyses such as Jaccard coefficient, Sokal’s matching coefficient, and van Eck and Waltman’s proximity or probabilistic affinity index, ignored by other software.
  2. Moreover, Models such as Procrustean Individual Differences Scaling (PINDIS), Individual Differences Scaling (INDSCAL), Singular Value Decomposition (MDPREF), multidimensional scaling (MRSCAL), MINISSA (Michigan-Nijmegen Integrated Smallest Space Analysis), and Latent Dirichlet Allocation (LDA), not provided by other software.
  3. Another difference is that it allows comparative analysis of lists of words. Only some software provides such features.
  4. Although the Hamlet software II lacks a variety of graphical presentation, there is software that allows advanced and more attractive graphs.
  5. Unlike other software, the Hamlet II use critical algorithms for statistical analyses of texts.

Hamlet II has many advantages over other text-based analytical software. However, certain limitations pertain to file extensions and usage of pre-defined dictionaries for textual analyses. The next article discusses the interface of the Hamlet II software.

Avishek Majumder

Avishek Majumder

Research Analyst at Project Guru
Avishek is a Master in Biotechnology and has previously worked with Lifecell International Private Limited. Apart from data analysis and biological research, he loves photography and reading. He loves to play football and basketball in his spare time with an avid interest in adventure and nature. He was also a member of the Scouts in his school and has attended Military training.
Avishek Majumder

Related articles

  • Text frequency or wordlist analysis in Hamlet II Text analysis, or text frequency analysis, is an important and common text-based analysis using Wordlist. In this analysis, the transcript or the text file is assessed for occurrence or repetition or frequency of words. This is known as the wordlist analysis in Hamlet II.
  • Performing wordlist comparing, KWIC and text profile in Hamlet II This article presents the steps to perform frequency analyses which are, keyword in-context or KWIC and graphical analysis of wordlist and compare wordlist.
  • Joint frequencies analysis using Hamlet II Joint frequencies analysis helps to search inter-connections between a number of keywords or character strings occurring in the text. It produces matrices of joint frequencies of the items of a specified vocabulary list with respect to a suitably chosen unit of context.
  • How to perform hierarchical clustering using Hamlet II? Hierarchical clustering uses methods to segregate the texts according to the similar vocabularies and then similar words or context are clustered together.
  • PINDIS in Hamlet II for multidimensional scaling PINDIS method applies a series of variations by increasing flexibility in each iteration. The aim is to maximize the optimization. It provides better specifics when compared to the INDSCAL results.


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