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A glossary of Text Analysis terms that you may come across most frequently
API (Application Program Interface): Software intermediary that allows two applications to talk to each other. In our case to access the features or data of an operating system, application, or other service.
Lemmatization: Identifying the base form of the word such as "run" in run, ran, run
Named Entity Recognition: Identifying proper names in a corpus
Natural Language Processing: Ability of a machine or program to understand human text or speech
N-grams: Probabilistic model in computational linguistics which identifies sequences of syllables, letters, words,etc. that can be expected in a sample of text
Parts of Speech Tagging: Identifying the syntactic role of a word
Stylometric Analysis: The quantitative study of literary style based on the observation that authors tend to write in relatively consistent, recognizable and unique ways.
Relation Extraction: Identifying the relationships between entities such as "daughter of" or "town in ? state"
Stemming: Processing rules to identify the base form of a word
Tokenization: Process of separating a string of characters into tokens which may be words, phrases or sentences. In the process punctuation is removed.
Text preprocessing: Cleaning, normalizing, and preparing text data for analysis by removing stopwords, punctuations, wide spaces, etc.
Topic modeling: Coding texts into meaningful categories
Lemmatization: Application of a dictionary that allows a system to consider variations of a term by using the dictionary entries to normalize words by replacing morphological variations with their root (for example, replacing 'gave' and 'give' with 'give'); more sophisticated than stemming but addresses the same issue (Welbers 2016)
Stemming: Technique used to reduce words to their root form by removing their endings (e.g., searching for hospital* to retrieve records containing the words hospital, hospitalized, hospitalised, hospitals, etc.)
Specificity: In search filter development and diagnostics, refers to the percentage of true negatives (true negatives divided by the sum of true negatives and false positives); the more false positives, the worse the specificity and precision are, but these two measures are calculated differently
TF-IDF (Term Frequency-Inverse Document Frequency): Weighting scheme used in word frequency analysis for storing text as weighted vectors; words with high frequency receive high weight unless they also have a high document frequency (e.g., stop words); "for high document frequency words, the competing effects cancel each other and give the word a low weight"
Categorization: Text categorization is the assignment of labels, typically from a predefined set, to a text document; one approach is based on hand coding, another on machine learning (two types of machine learning approaches: classification and clustering)
Classification: Categorization of documents using supervised machine learning; training set and predetermined labels are provided to train a classifier to correctly assign labels to uncategorized documents (Shatkay 2012)
Clustering: Categorization of documents using unsupervised machine learning; goal is to produce clusters of documents that are similar to each other according to some criterion, with different clusters for dissimilar documents (Shatkay 2012)
Collocates: A word’s collocates are words that appear next to or near it (Glanville 2016)
Geographical Analysis: Using mapping tools along with text analysis to plot terms in space