An Introduction to Text Mining

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Bettina Berendt

Department of Computer ScienceKU Leuven, Belgiumhttp://people.cs.kuleuven.be/~bettina.berendt/

Vienna Summer School on Digital HumanitiesJuly 7th, 2015, Vienna, Austria

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Starting questions for today

•What is a text?•What questions can we ask of a text?•What kind of answers "make us happy"?

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Some answers that would make you happy, and how (semi-)automatic text analysis could help

• Author▫ Usually a metadatum that is extracted from the metadata set▫ Can also be an inference: „“can you find out who is the author of this text?“

This is a text-mining task that has been studied for example in online texts (one subtype of the de-anonymization problem)

• Genre▫ A text-mining classification task (given a text, classify it into one from a list of genres)

• Style▫ Same (stylometry classification)

• statement / summary▫ Text mining task “summarization“ (e.g. of news texts)

• Content▫ The most typical text mining task: identify topics, classify into a content class, ...

• function, intention▫ (I‘m still not quite sure what this ... So this is for a future summer school ;-) )

• sequence of signs1. Sequential analysis of texts is common (e.g. In co-occurrence and collocation analysis)2. Signs (in the sense of arbitrary words standing for concepts): a key element of theories of

semantics, e.g. In the Semantic Web and Linked Open Data (e.g. DBPedia: a concept network version of Wikipedia) – are used in text mining, for example for improving topic modelling and classification

July 9th 2015: I‘ll add references to these yet, but wanted to get the slides out to you already!

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Motivation (1)

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Motivation (2)

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Goals and non-goals• Goals

▫ Understand the basic ideas of data mining▫ Understand how computer-scientist text miners approach texts▫ Compare it with your own approaches▫ Learn about some pitfalls and encourage a critical view▫ Get your hands on some tools and real data▫ Have an overview of other necessary steps (such as pre-processing)

that take too much time to be included in this course▫ Have pointers for inquiring and going further

• Non-goals (selection)▫ the statistical background of methods▫ a comprehensive overview of the state-of-the-art of text mining

methods▫ a comprehensive overview of the state-of-the-art of text mining

applications in the digital humanities or social or behavioural sciences▫ An introduction to big data computing or big DH infrastructuress

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A more modest goal than revolutionising knowledge as such?!“As long as there have been books there have been more books than you could read. … Knowing how to "not-read" is just as important as knowing how to read”

(Mueller, 2007).

“data mining and machine learning are best understood in terms of “provocation”—the potential for outlier results to surprise a reader into attending to some aspect of a text not previously deemed significant—as well as “not-reading” or “distant reading,” the automated search for patterns across a much wider corpus than could be read and assimilated via traditional humanistic methods of “close reading.””

(Kirschenbaum, 2007)

You use text mining every dayTexts as strings and feature vectors Text mining: steps and basic tasksEvaluationAbout today‘s dataset

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You use text mining every dayTexts as strings and feature vectors Text mining: steps and basic tasksEvaluationAbout today‘s dataset

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and/or its older

brother:Information

retrieval

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Origins of text mining. Or: What is a text for information retrieval? Let‘s do some reverse engineering ...

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Words, source relevance, and personalization

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Words and knowledge bases (1)

Metadata as output

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Knowledge-based text processing (2)

Metadata as input?

Requires different search

interfaces!

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PS: the ranking includes network analytics ( Thursday)

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PS: the ranking also includes adaptation; here: relevance feedback

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Trending topics: a form of summarization

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Finding “similar“ texts: Clustering(example Google News)

18Going further: What topics exist in a collection of texts, and how do they evolve?News texts,

scientific publications, …

Mei & Zhai (2005)

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Guiding questions• Information retrieval:

▫ Given the current user‘s information need, which are the most relevant documents?

• Text mining:▫ What do the documents tell us? What‘s in the texts? What can

we learn about the texts, their authors, ...▫ Many different subquestions▫ Summarization (of one text, of many texts) is just one of them

• Cf.▫ “Distant reading“ (Moretti)

understanding literature not by studying particular texts, but by aggregating and analyzing massive amounts of data.

▫ “Machine reading“ (UCL Machine Reading Group) machines that can read and "understand" this textual information,

converting it into interpretable structured knowledge to be leveraged by humans and other machines alike

You use text mining every dayTexts as strings and feature vectors Text mining: steps and basic tasksEvaluationAbout today‘s dataset

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Speed-reading(Woody Allen)

I took a course in speed reading and was able to read War and Peace in twenty minutes. It's about Russia.

... also quoted differently:

I took a speed reading course and read 'War and Peace' in twenty minutes. It involves Russia.

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A personal “experiment“- deliberately a bit silly, more a gentle introduction to a great tool and to some pitfalls of “distant reading“

(I haven‘t read War and Peace yet.)

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Speed-reading with word clouds:The Voyant tool (single-digit number of seconds)

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Note about „said“: Compare Joyce‘s Dubliners

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Word frequencies vs. Woody Allen

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Can we find out more about the 3?

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Double-check in Wikipedia (method: string search)• Count Pyotr Kirillovich (Pierre) Bezukhov: The large-bodied, ungainly, and

socially awkward illegitimate son of an old Russian grandee. Pierre, educated abroad, returns to Russia as a misfit. His unexpected inheritance of a large fortune makes him socially desirable. Pierre is the central character and often a voice for Tolstoy's own beliefs or struggles.

• Prince Andrey Nikolayevich Bolkonsky: A strong but skeptical, thoughtful and philosophical aide-de-camp in the Napoleonic Wars.▫ Some searching needed ... Andrew ... Andrei ... Andrey

• Countess Natalya Ilyinichna (Natasha) Rostova: A central character, introduced as "not pretty but full of life" and a romantic young girl, although impulsive and highly strung, she evolves through trials and suffering and eventually finds happiness. She is an accomplished singer and dancer.

• ...• Prince Anatole Vasilyevich Kuragin: Hélène's brother and a very handsome

and amoral pleasure seeker who is secretly married yet tries to elope with Natasha Rostova.

• Vasily Dmitrich Denisov: Nikolai Rostov's friend and brother officer, who proposes to Natasha.

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From Wikipedia‘s plot summary(method: string search)• ...• Natasha is convinced that she loves Anatole and writes to Princess Maria,

Andrei's sister, breaking off her engagement [with Andrei]. At the last moment, Sonya discovers her plans to elope and foils them. Pierre is initially horrified by Natasha's behavior, but realizes he has fallen in love with her. During the time when the Great Comet of 1811–2 streaks the sky, life appears to begin anew for Pierre.

• Prince Andrei coldly accepts Natasha's breaking of the engagement. He tells Pierre that his pride will not allow him to renew his proposal. Ashamed, Natasha makes a suicide attempt and is left seriously ill.

• ...• Having lost all will to live, [Andrei] forgives Natasha in a last act before

dying.• Pierre's wife Hélène dies from an overdose of abortion medication (Tolstoy

does not state it explicitly but the euphemism he uses is unambiguous). Pierre is reunited with Natasha, while the victorious Russians rebuild Moscow. Natasha speaks of Prince Andrei's death and Pierre of Karataev's. Both are aware of a growing bond between them in their bereavement. With the help of Princess Maria, Pierre finds love at last and, revealing his love after being released by his former wife's death, marries Natasha.

Total time: 29 mins since creation of word cloud, 17 mins since creation of Pierre-Natasha-Andrew chart(includes making these slides for you)

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Questions

•How much of this was “really automatic“?•What existing knowledge (in my head and

in others‘) went into this analysis,•and how?•Can you think of another reason why this

(deliberately) turned out silly?

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More interesting / serious examples (1)(from the summer school participants)

•Analysis of ego-shooter missions (thanks to Kathrin Trattner)

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Comment B. Berendt – compare this with an earlier text-mining analysis of reporting on the same events by CNN in comparison with Al Jazeera

• See next slide

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Unsupervised learning of bias

Nearest neighbour / best reciprocal hitfor document matching;Kernel Canonical Correlation Analysisand vector operationsfor finding topics and characteristic keywords

[Fortuna, Galleguillos, & Cristianini, 2009]

What characterizes different news sources?

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Additional information from Sentistrength analysis

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More interesting / serious examples (2)(from the summer school participants)• Analysis of ideological documents: • Charter of Hamas (thanks to Alexandra

Preitschopf):• analyses word usage and – interestingly – also

the absence of specific words▫Note: this shows clearly why we need domain

knowledge to interpret frequencies!• It also shows the difficulties of using sentiment

analysis when the real object of analysis is opinion/bias.

• For more details, see her presentation (also linked on my Summer School Web page)

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More interesting / serious examples (3)(from the summer school participants)• Joseph Goebbels‘ sportpalast

speech (a famous propaganda speech from 1943: “Do you want the total war?“)

• frequencies of negatively connotated words (“bolshevism“, “judaism / the Jews“) vs. positively connotated words (“Germans“) suggest:▫ The speech starts with a threat

scenario and ends with a positive vision of the future

• Remark B. Berendt: This is borne out by reading the full text, and it is also a classical rhetorical structure.

Text from:http://www.1000dokumente.de/index.html?c=dokument_de&dokument=0200_goe&object=translation&st=&l=de

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More interesting / serious examples (4)(from others)

Examples of Voyant in Research:http://docs.voyant-tools.org/about/examples-gallery/

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Some formalism: the vector-space model of text (basic model used in information retrieval and text mining)

▫ Basic idea: Keywords are extracted from texts. These keywords describe the (usually) topical

content of Web pages and other text contributions. ▫ Based on the vector space model of document

collections: Each unique word in a corpus of Web pages = one

dimension Each page(view) is a vector with non-zero weight for

each word in that page(view), zero weight for other words

Words become “features” (in a data-mining sense)

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• Starting point is the raw term frequency as term weights

• Other weighting schemes can generally be obtained by applying various transformations to the document vectors

nova galaxy heat actor film rolediet

A 1.0 0.5 0.3

B 0.5 1.0

C 0.4 1.0 0.8 0.7

D 0.9 1.0 0.5

E 0.5 0.7 0.9

F 0.6 1.0 0.3 0.2 0.8

Document Ids

a documentvector

Features

Document Representation as Vectors

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Other features (usually metadata of different sorts) can be added

•Tags or other categories•Special content (e.g. URLs, images, Twitter

mentions)•Source•Number of followers of source• ...

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https://aeshin.org/textmining/

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You use text mining every dayTexts as strings and feature vectors Text mining: steps and basic tasksEvaluationAbout today‘s dataset

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The idea of text mining ...

•... is to go beyond frequency-counting•... is to go beyond the search-for-

documents framework•... is to find patterns (of meaning) within

and especially across documents

•(but boundaries are not fixed)

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Data mining (aka Knowledge Discovery)

The non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data

(Fayyad, Platetsky-Shapiro, Smyth, 1996)

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CRISP-DM: Cross-Industry Standard Process for Data Mining

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The steps of text mining

1.Application understanding 2.Corpus generation3.Data understanding4.Text preprocessing5.Search for patterns / modelling

Topical analysis Sentiment analysis / opinion mining

6.Evaluation 7.Deployment

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Application understanding; Corpus generation

▫What is the question?▫What is the context?▫What could be interesting sources, and

where can they be found?

▫Use an existing corpus▫Crawl▫Use a search engine and/or archive and/or

API

▫Get help!

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Preprocessing (1)

•Data cleaning▫Goal: get clean ASCII text▫Remove HTML markup*, pictures,

advertisements, ...▫Automate this: wrapper induction

* Note: HTML markup may carry information too (e.g., <b> or <h1> marks something important), which can be extracted! (Depends on the application)

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Preprocessing (2)• Goal: get processable lexical / syntactical units• Tokenize (find word boundaries)• Lemmatize / stem

▫ ex. buyers, buyer buyer / buyer, buying, ... buy• Remove stopwords• Find Named Entities (people, places, companies, ...);

filtering• Resolve polysemy and homonymy: word sense

disambiguation; “synonym unification“• Part-of-speech tagging; filtering of nouns, verbs,

adjectives, ...• ...

• Most steps are optional and application-dependent!• Many steps are language-dependent; coverage of non-

English varies• Free and/or open-source tools or Web APIs exist for most

steps

Do you see a problemhere for DH?What implicit assumptions are made?

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Preprocessing (3)

•Creation of text representation▫Goal: a representation that the modelling

algorithm can work on▫Most common forms: A text as

a set or (more usually) bag of words / vector-space representation: term-document matrix with weights reflecting occurrence, importance, ...

a sequence of words a tree (parse trees)

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An important part of preprocessing:Named-entity recognition (1)

This 2009 OpenCalais screenshot visualizes nicely what today is mostly markup. E.g. in the tool http://www.alchemyapi.com/api/entity-extraction

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An important part of preprocessing:Named-entity recognition (2)

•Technique: Lexica, heuristic rules, syntax parsing

•Re-use lexica and/or develop your own ▫configurable tools such as GATE

•An example challenge: multi-document named-entity recognition▫Several solution proposals

•A more difficult problem: Anaphora resolution

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Styles of statistics-based analysis

• Statistics: descriptive – inferential• Data mining: descriptive – predictive (D – P)• Machine learning, data mining: unsupervised –

supervised

• Typical tasks in text analysis:▫D: Frequency analysis, collocation analysis,

association rules ▫D: Cluster analysis▫P: Classification▫ Interactive knowledge discovery: combines various

forms and involves “the human in the loop“

“It involves Russia.“

“It‘s about Russia.“

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https://aeshin.org/textmining/

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Tools we will see (you‘ll have to choose, based on your prior knowledge)• Frequency analysis, collocation analysis

▫Voyant ▫ (also offers many other forms, see

http://docs.voyant-tools.org/tools /) • More visualization (based on clustering)

▫DocumentAtlas• Classification

▫ Weka (can also do lots of other data mining tasks, such as association rules, and it is not made specifically for texts)

• Interactive knowledge discovery ▫Ontogen: Ontology learning based on clustering

and manual post-processing; includes DocumentAtlas

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Basic process of classification/predictionGiven a set of documents and their classes, e.g.

▫Spam, no-spam▫Topic categories in news: current affairs,

business, sports, entertainment, ...▫Any other classification

1. Learn which document features characterise the classes = learn a classifier

2. Predict, from document features, the classes

▫For old documents with known classes▫For new documents with unknown classes

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What makes people happy?

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Happiness in blogosphere

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• Well kids, I had an awesome birthday thanks to you. =D Just wanted to so thank you for coming and thanks for the gifts and junk. =) I have many pictures and I will post them later. hearts

• Well kids, I had an awesome birthday thanks to you. =D Just wanted to so thank you for coming and thanks for the gifts and junk. =) I have many pictures and I will post them later. hearts

current mood:

Home alone for too many hours, all week long ... screaming child, headache, tears that just won’t let themselves loose.... and now I’ve lost my wedding band. I hate this.

Home alone for too many hours, all week long ... screaming child, headache, tears that just won’t let themselves loose.... and now I’ve lost my wedding band. I hate this.

current mood:

What are the characteristic words of these two moods?

[Mihalcea, R. & Liu, H. (2006). In Proc. AAAI Spring Symposium CAAW.]

Slides based on Rada Mihalcea‘s presentation.

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Data, data preparation and learning

•LiveJournal.com – optional mood annotation

•10,000 blogs: ▫5,000 happy entries / 5,000 sad entries▫average size 175 words / entry▫pre-processing – remove SGML tags,

tokenization, part-of-speech tagging

Results: Corpus-derived happiness factors

yay 86.67shopping 79.56awesome 79.71birthday 78.37lovely77.39concert 74.85cool 73.72cute 73.20lunch 73.02books 73.02

goodbye 18.81hurt 17.39tears 14.35cried 11.39upset 11.12sad 11.11cry 10.56died 10.07lonely 9.50crying 5.50happiness factor of a word =

the number of occurrences in the happy blogposts / the total frequency in the corpus

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Weka – classification with Naive Bayes

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Using classifier learning for literature analysis – here: a (Weka) decision tree (early example: MONK)

Sara Steger (2012).Patterns of Sentimentality in Victorian Novels.Digital Studies 3(2).

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Many other tasks (ex. news/blogs mining)Tasks in news / (micro-)blogs mining can be grouped by different criteria:• Basic task and type of result: description, classification and

prediction (supervised or unsupervised, includes for example topic identification, tracking, and/or novelty detection; spam detection); search (ad hoc or filtering); recommendation (of blogs, blog posts, or (hash-)tags); summarization

• Higher-order characterization to be extracted: especially topic or event; opinion or sentiment

• Time dimension: nontemporal; temporal (stream mining); multiple streams (e.g., in different languages, see cross-lingual text mining)

• User adaptation: none (no explicit mention of user issues and/or general audience); customizable; personalized

Berendt (Encyclopedia of Machine Learning and Data Mining, in press).

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Real-world applications of news/blogs miningReal-world applications increasingly employ selections or, more often, combinations of these tasks by their intended users and use cases, in particular:• News aggregators allow laypeople and professional users (e.g. journalists) to see “what’s

in the news” and to compare different sources’ texts on one story. Reflecting the presumption that news (especially mainstream news – sources for news aggregators are usually whitelisted) are mostly objective/neutral, these aggregators focus on topics and events. News aggregators are now provided by all major search engines.

• Social-media monitoring tools allow laypeople and professional users to track not only topical mentions of a keyword or named entity (e.g. person, brand), but also aggregate sentiment towards it. The focus on sentiment reflects the perceptions that even when news-related, social media content tends to be subjective and that studying the blogosphere is therefore an inexpensive way of doing market research or public-opinion research. The whitelist here is usually the platforms (e.g. Twitter, Tumblr, LiveJournal, Facebook) rather than the sources themselves, reflecting the huge size and dynamic structure of the blogosphere / the Social Web. The landscape of commercial and free social-media monitoring tools is wide and changes frequently; up-to-date overviews and comparisons can easily be found on the Web.

• Emerging application types include text mining not of, but for journalistic texts, in particular natural language generation in domains with highly schematized event structures and reporting, such as sports and finance reporting (e.g. Allen et al., 2010; narrativescience.com) and social-media monitoring tools for helping journalists find sources (Diakopoulos et al., 2012).

Berendt (Encyclopedia of Machine Learning and Data Mining, in press).

You use text mining every dayTexts as strings and feature vectors Text mining: steps and basic tasksEvaluationAbout today‘s dataset

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Evaluation of unsupervised learning: e.g. clustering•Do the clusters make sense?•Are the instances within one cluster

similar to one another?•Are the instances in different clusters

dissimilar to one another?•(There are quantitative metrics of #2 and

#3)

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Quality of automatic “mood separation”•naïve bayes text classifier

▫five-fold cross validation•Accuracy: 79.13% (>> 50% baseline)

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Who defines which class a document belongs to?•The researcher?•The author?•The reader?•Someone paid to do exactly this (e.g. a

worker on mTurk)?•Several of them?•Someone else?

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The importance of consensus

Illustration: ESP game (“Games with a purpose“)

von Ahn (2005, 2006)

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Measuring inter-rater reliability• Popular measure of inter-rater agreement from content

analysis• Non-trivial formula (see references), but software exists.

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How good is good: Magic numbers?

• (Kappa is a related measure; the boundaries are the same)• Boundaries are disputed and tend to get higher• Inter-rater agreement often systematically low, e.g. in text

summarization: slightly over 50% (Berendt et al., 2014)• Recent approaches attempt to accept this ambiguity and

work with it: e.g. Poesio et al. (2013)

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In what sense is this an alternative?

• “Given that there is no ground truth is a discipline like literary criticism, it is difficult to know how influential these results will prove.

• A scholar would have to write them up in traditional article or monograph form, wait for the article or monograph to move through the peer-review process (this can take months or years) and then other scholars in the field will have to read it, be influenced by its arguments, and adjust their own interpretations of Dickinson—in turn publishing these in their own articles and monographs.

• Nonetheless, we believe that the Nora system has suggested that classification and prediction can be useful agents of provocation in humanistic study.”

(Kirschenbaum, 2007)

You use text mining every dayTexts as strings and feature vectors Text mining: steps and basic tasksEvaluationAbout today‘s dataset

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#gamergate

“GamerGate is a grassroots movement with the goal of supporting ethics in game journalism. Some feminists have claimed it is a hateful, misogynistic movement, but they haven't been able to meet the burden of proof on that.”

http://drunken-peasants-podcast.wikia.com/wiki/GamerGate

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(Only) one reason this is interesting for text analysis“Ethics aren't the only thing #Gamergate is concerned with. As the movement made the shift from ad hominem attacks to insisting that its only interest in Quinn was as an example of nepotism and corruption in the gaming industry, it also began co-opting the language of social justice movements and of journalism to legitimize its complaints.

Although their movement targets women specifically, #Gamergaters insist they speak for a victimized "demographic," and that anyone who opposes misogyny while making generalizations about gamers must be a hypocrite.” http://gawker.com/what-is-gamergate-and-why-an-explainer-for-non-geeks-1642909080

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Gamergate tweets• Based on the work of Budac, A., Chartier, R., Suomela, T., Gouglas, S.,

& Rockwell, G. (see sources at the end of this slideset)• I received the data for the purposes of this summer school (i.e. also for

you)▫ Condition: we all respect the associated ethics code▫ This is an interesting document in itself, and we will use it for part 3

• Data post-processed for you: “most retweeted tweets“ Oct‘14 – Mar’15, in 4 versions (each version assembled into one ZIP file)▫ 1 document per month, tweet texts ordered by count of retweets (desc.)

Voyant▫ 1 document per tweet, sorted into 1 folder per month

DocumentAtlas/Ontogen▫ 1 document overall ( Weka), with fields

anonymized user ID Month Count in that month‘s dataset Tweet text

- The same, but with some post-processing that will make your analysis easier

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The post-processing applied(& user removed, &1000 highest-ranking attr.s selected)

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I suggest you run trees J48with settings such as these, and Test Options: Use Training Set

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Thank you!

I‘ll be more than happy to hear your

s?

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ReferencesA good textbook on Text Mining:• Feldman, R. & Sanger, J. (2007). The Text Mining Handbook. Advanced Approaches in Analyzing

Unstructured Data. Cambridge University Press.An introduction similar to this one, but also covering unsupervised learning in some detail, and with lots of pointers to books, materials, etc.:• Shaw, R. (2012). Text-mining as a Research Tool in the Humanities and Social Sciences. Presentation at the

Duke Libraries, September 20, 2012. https://aeshin.org/textmining/ An overview of news and (micro-)blogs mining:• Berendt, B. (in press). Text mining for news and blogs analysis. To appear in C. Sammut & G.I. Webb (Eds.),

Encyclopedia of Machine Learning and Data Mining. Berlin etc.: Springer. http://people.cs.kuleuven.be/~bettina.berendt/Papers/berendt_encyclopedia_2015_with_publication_info.pdf

See http://wiki.esi.ac.uk/Current_Approaches_to_Data_Mining_Blogs for more articles on the subject.

Individual sources cited on the slides• Fortuna, B., Galleguillos, C., & Cristianini, N. (2009). Detecting the bias in media with statistical learning

methods. In Text Mining: Classification, Clustering, and Applications, Chapman & Hall/CRC, 2009. • Qiaozhu Mei, ChengXiang Zhai: Discovering evolutionary theme patterns from text: an exploration of

temporal text mining. KDD 2005: 198-207• Mihalcea, R. & Liu, H. (2006). A corpus-based approach to finding happiness, In Proc. AAAI Spring

Symposium on Computational Approaches to Analyzing Weblogs. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.6759

• Kirschenbaum, M. "The Remaking of Reading: Data Mining and the Digital Humanities." In NGDM 07: National Science Foundation Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation. http://www.cs.umbc.edu/~hillol/NGDM07/abstracts/talks/MKirschenbaum.pdf

• Mueller, M. “Notes towards a user manual of MONK.” https://apps.lis.uiuc.edu/wiki/display/MONK/Notes+towards+a+user+manual+of+Monk, 2007.

• Massimo Poesio, Jon Chamberlain, Udo Kruschwitz, Livio Robaldo and Luca Ducceschi, 2013. Phrase Detectives: Utilizing Collective Intelligence for Internet-Scale Language Resource Creation. ACM Transactions on Intelligent Interactive Systems, 3(1). http://csee.essex.ac.uk/poesio/publications/poesio_et_al_ACM_TIIS_13.pdf

• Luis von Ahn (2005). Human Computation. PhD Dissertation. Computer Science Department, Carnegie Mellon University. http://reports-archive.adm.cs.cmu.edu/anon/usr0/ftp/usr/ftp/2005/abstracts/05-193.html

• Luis von Ahn: Games with a Purpose. IEEE Computer 39(6): 92-94 (2006)

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More DH-specific tools

Overviews of 71 tools for Digital Humanists•Simpson, J., Rockwell, G., Chartier, R.,

Sinclair, S., Brown, S., Dyrbye, A., & Uszkalo, K. (2013). Text Mining Tools in the Humanities: An Analysis Framework. Journal of Digital Humanities, 2 (3), http://journalofdigitalhumanities.org/2-3/text-mining-tools-in-the-humanities-an-analysis-framework/

•See also the link collection on the Voyant documentation Web page

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Tools (powerful, but require some computing experience)• Ling Pipe

▫ linguistic processing of text including entity extraction, clustering and classification, etc.

▫ http://alias-i.com/lingpipe/• OpenNLP

▫ the most common NLP tasks, such as POS tagging, named entity extraction, chunking and coreference resolution.

▫ http://opennlp.apache.org/• Stanford Parser and Part-of-Speech (POS) Tagger

▫ http://nlp.stanford.edu/software/tagger.shtm/• NTLK

▫ Toolkit for teaching and researching classification, clustering and parsing▫ http://www.nltk.org/

• OpinionFinder▫ subjective sentences , source (holder) of the subjectivity and words that are included in

phrases expressing positive or negative sentiments.▫ http://code.google.com/p/opinionfinder/

• Basic sentiment tokenizer plus some tools, by Christopher Potts▫ http://sentiment.christopherpotts.net

• Twitter NLP and Part-of-speech tagging▫ http://www.ark.cs.cmu.edu/TweetNLP/

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Further tools (thanks for your suggestions!)•Atlas TI: “Qualitative data analysis“

▫http://atlasti.com/▫Commercial product, has free trial version

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Gamergate sources

• Budac, A., Chartier, R., Suomela, T., Gouglas, S., & Rockwell, G. (2015) #GamerGate: Distant Reading Games Discourse. Paper presented at the CGSA 2015 conference at the HSSFC Congress at University of Ottawa, Ottawa, Ontario, June 2015.

• Rockwell, G. (2015). Appendix 1: Ethics of Twitter Gamergate Research.

• Rockwell, Geoffrey; Suomela, Todd, 2015, "Gamergate Reactions", http://dx.doi.org/10.7939/DVN/10253 V5 [Version].

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More sources

•Please find the URLs of pictures and screenshots in the Powerpoint “comment“ box

•Thanks to the Internet for them!