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Chapter 17
Tools for Social Media Data [#]

Social media provides data that is highly valuable to many organizations, for example as a way to track public opinion about a company’s products or to discover attitudes towards “hot topics” and breaking news stories. However, processing social media text presents a set of unique challenges, and text processing tools designed to work on longer and more well-formed texts such as news articles tend to perform badly on social media. To obtain reasonable results on short, inconsistent and ungrammatical texts such as these requires tools that are specifically tuned to deal with them.

This chapter discusses the tools provided by GATE for use with social media data.

17.1 Tools for Twitter [#]

The Twitter plugin contains several tools useful for processing tweets. This plugin depends on the Stanford_CoreNLP plugin, which must be loaded first. This includes tools to load and save documents in GATE using the JSON format provided by the Twitter APIs, a tokeniser and POS tagger tuned specifically for Tweets, a tool to split up multi-word hashtags, and an example named entity recognition application called TwitIE which demonstrates all these components working together.

17.2 Twitter JSON format [#]

Twitter provides APIs to search for Tweets according to various criteria, and to collect streams of Tweets in real-time. These APIs return the Tweets in a structured JSON format1 which includes the text of the Tweet plus a large amount of supporting metadata. The GATE Twitter plugin contains a format analyser for this JSON format which allows you to load a file of one or more JSON Tweets into a GATE document. The format analyser can handle multiple Tweets in one file, represented as any of:

Loading the plugin registers the document format with GATE, so that it will be automatically associated with files whose names end in “.json”; otherwise you need to specify text/x-json-twitter for the document mimeType parameter. This will work both when directly creating a single new GATE document and when populating a corpus.

Each tweet object’s text value is converted into the document content2, which is covered with a Tweet annotations whose features represent (recursively when appropriate, using Map and List) all the other key-value pairs in the tweet object. Note: these recursive values are difficult to work with in JAPE; the special corpus population tool described next allows important key-sequences to be “brought up” to the document content and the top level of the annotation features. Any entities described by the standoff markup “entities” JSON property will be converted into their corresponding GATE annotations (see below for details).

Multiple tweet objects in the same JSON file are separated by blank lines (which are not covered by Tweet annotations).

As well as the document format parser to load Tweets into a single GATE document, the plugin provides a “Populate from Twitter JSON files” option on the GATE Corpus right-click menu. Selecting this option bringgs up a dialog that allows you to select one or more files of tweets in the Twitter API’s JSON format and set the following options to populate the corpus.

The default here is UTF-8 (regardless of your Java default) to conform to Twitter JSON.
One document per tweet
If this box is ticked (the default), each tweet will produce a separate document. If not, each input file will produce one GATE document.
Annotations for “entities”
If this box is ticked (the default), any entities described by the standoff markup “entities” JSON property will be converted into their corresponding GATE annotations (see below).
Content keys
The values of these JSON keys are converted into strings and concatenated into each tweet’s document content. Colon-delimited strings specify nested keys, e.g., “user:name” will yield the value of the name key in the map that is the value of the user key. Missing key sequences are ignored. Each span of text will be covered by an annotation whose type is the key sequence.
Feature keys
The key sequences and values of these JSON keys (where present) are turned into feature names and values on the tweet’s main Tweet annotation.
Save configuration
This button saves the current options in an XML file for re-use later.
Load configuration
This button sets the options according to a saved XML configuration.

Again, the input can be in any of the three formats discussed above (an array of Tweets, a search result, or a stream of concatenated objects). Every tweet in the resulting GATE documents is covered by a Tweet annotation with features specified by the “feature keys” option. Multiple tweets in the same GATE document are separated by a blank line (two newlines).

Corpus population from Twitter JSON files is also accessible programmatically when this plugin is loaded, using the public static void method gate.corpora.twitter.Population.populateCorpus(final Corpus corpus, URL inputUrl, String encoding, List<String> contentKeys, List<String> featureKeys, int tweetsPerDoc, boolean processEntities).

17.2.1 Entity annotations in JSON [#]

Twitter’s JSON format provides a mechanism to represent annotations over the Tweet text as standoff markup, via a JSON property named “entities”. The value of this property is an object with one property for each entity type, whose value is a list of objects representing the individual annotations. Within each individual entity object, the “indices” property gives start and end character offsets of the annotation within the Tweet text.

  "text":"@some_user this is a nice #example",  

Both the single document format parser and the corpus population tool are able to convert this structure into GATE annotations. The entity type (e.g. user_mentions) becomes the annotation type, the indices property provides the offsets, and the other properties become features of the generated annotation.

By default, the entity annotations are created in the “Original markups” annotation set, as is the usual convention for annotations generated by a document format. However, if the entity type contains a colon character (e.g. "Key:Person":[...]) then the portion before the colon is taken to be an annotation set name and the portion after the colon is the annotation type (in this example, a “Person” annotation in the “Key” annotation set). An empty annotation set name (i.e. ":Person") creates the corresponding annotations in the default annotation set. This scheme is designed to be compatible with the GATE JSON export mechanism described in the next section.

17.3 Exporting GATE documents as JSON [#]

Loading the Twitter plugin also adds a “GATE JSON” option to the “Save as…” right-click menu on documents and corpora, to export GATE documents in the Twitter-style JSON format. This tool can save a document or corpus of documents as a single file where each Tweet in the document or corpus is represented as a JSON object, and the set of objects are represented either as a single top-level JSON array ([{...},{...}]) or simply as one object per line (as per Twitter’s streaming APIs). This exporter can be used for any GATE document, not just for documents that were initially loaded from Twitter JSON format, and can be used as a much more compact alternative to GATE XML, or as an easy-to-parse interchange format to pass GATE-annotated documents to non-GATE tools.

The format is the same as Twitter’s – the text becomes a property “text” in the JSON, and annotations are represented as standoff markup in the “entities” property, which is an object whose keys are annotation types and whose corresponding values are arrays of objects representing the annotations.


Figure 17.1: Options dialog for saving a document or corpus as JSON

The available options for the JSON exporter are shown in figure 17.1. In detail, they are:

the annotation set and type that should be treated as covering each span of text that should be output as a separate JSON object. By default this is annotations of type “Tweet” in the “Original markups” set (i.e. the annotations covering individual Tweets parsed by the JSON document format parser or corpus population tool). If a document contains any annotations of the specified type then one JSON object will be output for each such annotation X, with the text and entity annotations constrained to the span of X. In addition, features of X will become top-level properties of the resulting JSON object. Text that is not covered by any such annotation will not be saved. If there are no document annotations found in a particular document (or if the documentAnnotationType parameter is unset) then the whole of the document text will be output as a single JSON object.
the primary annotation set that should be scanned for entity annotations.
the entity annotation types to output.
if true, output the objects as a top-level JSON array. If false (the default), output the JSON objects directly at the top level, separated by newlines.

Annotation types to be saved can be specified in two ways. Plain annotation type names such as “Person” will be taken from the specified entitiesAnnotationSetName, but if a type name contains a colon character (e.g. “Key:Person”) then the portion before the colon is treated as the annotation set name and the portion after the colon as the annotation type. The full name including the colon will be used as the type label in the “entities” object, so if the resulting JSON were re-loaded into GATE the annotations would be re-created in the same annotation sets they originally came from.

17.4 Low-level PRs for Tweets [#]

The Twitter plugin provides a number of low-level language processing components that are specifically tuned to Twitter data.

The “Twitter Tokenizer” PR is a specialization of the ANNIE English Tokeniser for use with Tweets. There are a number of differences in the way this tokeniser divides up the text compared to the default ANNIE PR:

The “Tweet Normaliser” PR uses a spelling correction dictionary to correct mis-spellings and a Twitter-specific dictionary to expand common abbreviations and substitutions. It replaces the string feature on matching tokens with the normalised form, preserving the original string value in the origString feature.

The “Twitter POS Tagger” PR uses the Stanford Tagger (section 23.26) with a model trained on Tweets. The POS tagger can take advantage of expanded strings produced by the normaliser PR.

17.5 Handling multi-word hashtags [#]

When rendering a Tweet on the web, Twitter automatically converts contiguous sequences of alpha-numeric characters following a hash (#) into links to search for other Tweets that include the same string. Thus “hashtags” have rapidly become the de-facto standard way to mark a Tweet as relating to a particular theme, event, brand name, etc. Since hashtags cannot contain white space, it is common for users to form hashtags by running together a number of separate words, sometimes in “camel case” form but sometimes simply all in lower (or upper) case, for example “#worldgonemad” (as search queries on Twitter are not case-sensitive).

The “Hashtag Tokenizer” PR attempts to recover the original discrete words from such multi-word hashtags. It uses a large gazetteer of common English words, organization names, locations, etc. as well as slang words and contractions without the use of apostrophes (since hashtags are alphanumeric, words like “wouldn’t” tend to be expressed as “wouldnt” without the apostrophe). Camel-cased hashtags (#CamelCasedHashtag) are split at case changes.

More details, and an example usecase, can be found in [Maynard & Greenwood 14].

17.6 The TwitIE Pipeline [#]

The Twitter plugin includes a sample ready-made application called TwitIE, which combines the PRs described above with additional resources borrowed from ANNIE and the TextCat language identification PR to produce a general-purpose named entity recognition pipeline for use with Tweets. TwitIE includes the following components:

Full details of the TwitIE pipeline can be found in [Bontcheva et al. 13].


2HTML entity references &amp;, &lt; and &gt; are decoded into the corresponding characters