Collocation traces the appearance of words that commonly appear next to each other in a text or series of text in order to analyze the words' importance. 

Out-of-the-Box

• Voyant Tools
  Web-based reading and analysis environment for digital texts, for performing tasks such as word frequencies, collocations, concordance, visualization (graphs, grids, word clouds, etc.)

• AntConc
  Freeware, multi-platform, multi-purpose corpus analysis toolkit that hosts a comprehensive set of tools, including a powerful concordancer, word and keyword frequency generators, tools for cluster and lexical bundle analysis, and a word distribution plot

• WordHoard
  Application for the close reading and scholarly analysis of deeply tagged texts, including word frequencies, concordances, collocations, and scripting

• CasualConc
  Concordance program for macOS, designed for exploratory-type text analysis and visualization of frequency data, including keyword in context (KWIC) concordance lines, word clusters, collocation analysis, and word count

Programmatic

Python

• NLTK (Natural Language Toolkit)
  For accessing corpora and lexicons, tokenization, stemming, (part-of-speech) tagging, parsing, transformations, translation, chunking, collocations, classification, clustering, topic segmentation, concordancing, frequency distributions, sentiment analysis, named entity recognition, probability distributions, semantic reasoning, evaluation metrics, manipulating linguistic data (in SIL Toolbox format), language modeling, and other NLP tasks

• spaCy
  For tokenization, named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more

• scikit-learn
  For classification, regression, clustering, dimensionality reduction, model selection, and preprocessing

R

• tidytext
  For converting to and from non-tidy formats, word and document frequency analysis (tf-idf), n-grams and correlations, sentiment analysis with tidy data, and topic modeling

• openNLP
  For NLP tasks such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution

• RcmdrPlugin.temis
  For performing a series of text mining tasks such as importing and cleaning a corpus, and analyses like terms and documents counts, vocabulary tables, terms co-occurrences and documents similarity measures, time series analysis, correspondence analysis and hierarchical clustering

• RWEKA
  For stemming, data transformation, distribution-based balancing of datasets, replacing missing numerical values, dataset resampling, anonymization, normalization, classification, regression, clustering, association rules, and visualization

• tm
  For importing and handling corpus data, metadata management, stemming, stop word deletion, removal of white space, string processing, count-based analysis methods, text clustering, text classification, and string kernels

• Getting Collocates for Words

• Collocations - NLTK Documentation

• A Collocations Notebook

• NLT Collocations Examples

• Python script using Spacy

• Perkins, Jacob. 2014. Python 3 Text Processing with NLTK 3 Cookbook. Pack Publishing.

• Merriam, Thomas. 2019. “‘Six-Word Collocations in Shakespeare and Sir Thomas More’—Revisited.” Notes and Queries 66 (3): 415–16.