=Paper=
{{Paper
|id=Vol-1445/tweetmt-1-gonzalez
|storemode=property
|title=An Analysis of Twitter Corpora and the Differences between Formal and Colloquial Tweets
|pdfUrl=https://ceur-ws.org/Vol-1445/tweetmt-1-gonzalez.pdf
|volume=Vol-1445
|dblpUrl=https://dblp.org/rec/conf/sepln/Gonzalez15
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==An Analysis of Twitter Corpora and the Differences between Formal and Colloquial Tweets==
https://ceur-ws.org/Vol-1445/tweetmt-1-gonzalez.pdf
An Analysis of Twitter Corpora and the Differences between
Formal and Colloquial Tweets∗
Análisis de Varios Corpus de Twitter y las Diferencias entre Tweets
Formales y Coloquiales
Meritxell Gonzàlez
Oxford University Press, Oxford, United Kingdom
Universitat Politècnica de Catalunya, Barcelona, Spain
meritxell.gonzalezbermudez@oup.com
Abstract: This work reviews recent publications addressing the Twitter
translation task, and highlights the lack of appropriate corpora that represents the
colloquial language used in Twitter. It also discusses the most well-know issues in
the Twitter genre: the use of hashtags and the amount of OOVs, with especial focus
in comparing the differences between formal and colloquial texts.
Resumen: Este trabajo resume las publicaciones recientes en el área de la
traducción automática de tweets, destacando la falta de un corpus que represente
el lenguaje coloquial presente en Twitter. También se tratan los problemas más
conocidos del género de Twitter: el uso de hashtags i la gran cantidad de palabras
OOV, con especial enfoque en las diferencias entre tweets formales y coloquiales.
Keywords/Palabras clave: corpus, tweets, hashtags, and OOV
1 Introduction occur, while the second has been typically
The success and increasing popularity of addressed by combining large amounts of
microblogging has raised the need to analyse general purpose data and smaller subsets of
and process its content. Traditional methods domain specific datasets. The creation of
for natural language processing fail when a gold standard in MT requires the use of
applied over these texts. The reason parallel data that helps to assess the quality
is not circumscribed to few nor simple of the output.
issues. Roughly, microblogs documents do When addressing the automatic
not follow the traditional structure of a translation within the microblogging
formal text or document, they use a number genre, one has to deal with the additional
of language variants, styles and registers difficulty of having little or no context and
among other linguistic phenomena, and can the fact that microblogs exhibit fleeting
even include multimedia content as a way of domains. Twitter is not different from
communication (Jehl, 2010; Fabrizio Gotti other microblogs, and has, in addition, its
and Phillippe Langlais and Atefeh Farzindar, own particularities. As described in (Jehl,
2014; Kaufmann, Max and Kalita, Jugal, 2010), tweets actually share the spontaneity
2010; Bertoldi, Nicola and Cettolo, Mauro and expressiveness of the spoken language,
and Federico, Marcello, 2010). but limited to 140 characters. Due this
Machine Translation (MT) is a hard task constraint, tweets have usually a very
within the natural language processing field. simple syntax. However, they are mined
It has received considerable attention during of ungrammaticalities, misspellings and an
the last decades, and it is still an active field unlimited number of lexical variants created
with many research challenges. As in other out of the human imaginary and the common
natural language processing tasks, it counts ground of part of the audience.
among its difficulties the ambiguity of the In this document, Section 2 summarises
language, and the need of corpora and a gold recent studies in this field and different
standard. The former can be addressed by approaches followed to address these
analysing the context in which a sentence phenomena. Next, Sections 3 to 5 give a
∗
This work was partially funded by the TACARDI
numerical analysis of 6 different corpora
project (TIN2012-38523-C02) of the Spanish of tweets written in Basque, Catalan, and
Ministerio de Economı́a y Competitividad. Spanish. The goal of this analysis is to
sketch the content of the Twitter messages deal with the noisy input from colloquial
(tweets), highlight which are their principal texts, but either they do not belong to
characteristics and discuss the differences the Twitter genre or they do not contain
between formal and colloquial tweets. parallel data. (Kaufmann, Max and Kalita,
Jugal, 2010) describes an MT system able
2 Recent Work on Twitter to translate from colloquial English into
Translation standard English. The rationale is that
traditional NLP techniques can be applied
The automatic translation of tweets, in
over standardised text. Their methodology
general, is more difficult than regular MT.
includes the use of aligned data from a
Although the MT community has already
corpus of SMSs that contains most common
addressed the translation of tweets, there are
acronyms and short forms. (Bertoldi, Nicola
still few works in this area, mainly because
and Cettolo, Mauro and Federico, Marcello,
of the lack of corpora, and especially those
2010) and (Formiga, Lluı́s and Fonollosa,
showing a fair representation of colloquial
José A. R., 2012) address the problem
texts. The number of authors publishing
of translating noisy input. The former
content in multiple languages is not small,
by trying to simulate and generate noisy
but their messages tend to be correct and
input automatically; the latter by adding a
well structured, in contrast to those posted
preprocessing layer to convert the input into
by the gross of the users.
clean text. Finally, the corpus described
2.1 Twitter Corpora in (Alegria, Iñaki and Aranberri, Nora
and Comas, Pere R and Fresno, Vıctor
The availability of parallel corpora for
and Gamallo, Pablo and Padró, Lluis and
Twitter is growing but still scarce. The
San Vicente, Iñaki and Turmo, Jordi and
following four works gathered parallel data
Zubiaga, Arkaitz, 2014) was distributed to
following diverse approaches, but them all
the participants of the TweetNorm shared
contain formal texts only. (Gotti, Fabrizio
task. This is a monolingual corpus of Spanish
and Langlais, Philippe and Farzindar,
tweets. Since this corpus has been used in
Atefeh, 2013) gathered data from Canadian
this study it is further detailed in Section 3.
Government Agencies, written in French
and English. This work describes an MT 2.2 Linguistic Phenomena
system that uses in-domain parallel data
crawled from the links appearing in the Although the previous works addressed
tweets. Hence, tuning was conducted with different problems, they share a common
documents from the same domain. The ground on the principal difficulties of the
corpus built in (Ling, Wang and Marujo, Twitter genre. First, the translation of
Luis and Dyer, Chris and Black, Alan W hashtags is an open issue that includes its
and Trancoso, Isabel, 2014) contains tweets segmentation, identification and analysis of
written in Chinese and English. This work its role in sentences (Fabrizio Gotti and
describes a tool and a methodology to help Phillippe Langlais and Atefeh Farzindar,
users to identify parallel excerpts in the 2014). Second, the correct tokenisation of
messages and to annotate their boundaries. the text is essential but difficult due the
The data obtained with this method was extreme noisiness of the text. Also, making
fairly cheap (crowd-sourced) and it resulted the translation fit in 140 characters can harm
to have a high degree of quality. (Jehl, Laura the quality of the output, although (Jehl,
and Hieber, Felix and Riezler, Stefan, 2012) 2010) addressed this issue in her thesis and
used a corpus of Arabic sentences that were reported good results.
manually translated into English. The data The increasing interest in the field has
was crawled by filtering the topic (Arabic promoted the design of tools to create
Spring) and was cleaned and pruned, also especialised corpora. However, the human
by means of crowd-sourcing. Finally, the translation of tweets also raises open
shared task described in (Alegria et al., 2015) questions (S̆ubert and Bojar, 2014). For
distributed a collection of parallel corpora instance, how to translate idioms and slang,
in the languages spoken in the Iberian out-of-vocabulary words, onomatopoeias,
peninsula. These corpora have been used in emphasises (jajaaaaa), or irony. But also,
this study and they are detailed in Section 3. how to approach the translation of hashtags
In contrast, the following four works and symbols (such as emoticons), how to
interpret wrong syntax, find the translated CAES.ca CAES.es
version of a link, and fit the final translation # tweets 4, 000 4, 000
into 140 characters, among others. # tokens 66, 559 66, 113
All in all, the creation of synthetic corpus avg. tokens/tweet 16.39 16.53
to simulate these phenomena seem a feasible EUES.eu EUES.es
approach (Bertoldi, Nicola and Cettolo, # tweets 4, 000 4, 000
Mauro and Federico, Marcello, 2010), yet out # tokens 58, 368 51, 782
of the scope of this study. Last, but not least, avg. tokens/tweet 14.59 12.94
an appropriate methodology and measures TNORM TSM
to assess the quality of Twitter translations
# tweets 1, 132 8, 571
including its particular characteristics has # tokens 14, 497 123, 679
not been addressed so far. avg. tokens/tweet 12.80 14.43
3 Description of the Used Table 1: Statistics on number of tweets and
Corpora tokens in each corpus.
The next sections analyse six datasets of
tweets from the Tweet-Norm (Alegria, Iñaki general domain set of tweets randomly
and Aranberri, Nora and Comas, Pere R selected. So similarly to TNORM, it
and Fresno, Vıctor and Gamallo, Pablo and contains both formal and colloquial tweets.
Padró, Lluis and San Vicente, Iñaki and They were manually processed to classify
Turmo, Jordi and Zubiaga, Arkaitz, 2014), them according to the language of the
Tweet-MT (Alegria et al., 2015) and Social tweet and annotate different layers such
Media (Roser Saurı́, 2013) corpora. The as communication function, polarity, target,
goal is to discuss a few of the phenomena and topic. This process included some clean
mentioned in the previous section. up of the twitter mark-up for privacy reasons.
A set of four datasets was obtained Hence, the author id and user mentions,
from the Tweet-MT corpora. It consists hashtags and URLs were substituted with
of 2 bitexts for Catalan–Spanish and the labels @USER, #HASHTAG and [URL],
Basque–Spanish language pairs. The four respectively.
datasets contain both, the development and The six datasets were processed to
the test sets for each language: CAES.ca, have similar characteristics: the tokens
CAES.es, EUES.eu and EUES.es. The that correspond to the author id and RT
tweets in these datasets were obtained from (re-tweet) were removed when present, and
a sample of manually selected accounts they were tokenised using an adaptation
of authors that tend to tweet in various to Spanish and Catalan languages of the
languages, being namely public organisations Twokenize tool (Brendan O’Connor and
and personalities. Hence, the content of the Michel Krieger and David Ahn, 2010).
messages is mainly formal, i.e., they do not Table 1 shows the number of tweets, the
contain misspellings and do not abuse of the number of tokens and the average number of
use of symbols. tokens per tweet in each corpus. Regardless
The fifth dataset, TNORM, was obtained the differences in nature of the datasets and
from the Tweet-Norm corpus that gathered a their size, they show a similar number of
random selection of geolocated tweets within tokens per tweet, being CAES.ca the dataset
the Iberian peninsula, excluding multilingual with longer ones and EUES.eu the shortest.
areas where other languages than Spanish The messages in the two colloquial corpora
are spoken. The corpus was processed TNORM and TSM seem to have slightly
to identify and annotate out-of-vocabulary shorter posts compared with their formal
words. Hence, it contains not only correct ones in the same language CAES.es and
messages, but also colloquial ones. The EUES.es.
dataset used in this work contains the two Although tweets are similar in length,
development sets and the test provided in the a deeper analysis of their content shows
workshop. remarkable differences between the formal
The last dataset used in this work and the colloquial corpora. This section
is TSM. It is a portion of the Social analyses the use of user mentions and
Media Corpus, and in particular the corpus URLs whereas Section 4 analyses the use
of tweets in Spanish. It contains a of hashtags. Although dealing with user
CAES.ca CAES.es CAES.ca CAES.es
# @users 743 873 # hashtags 3, 286 3, 821
avg. @users/tweet 0.18 0.22 # hashtag types 198 430
% @users wrt. tokens 1.13% 1.32% # avg. hashtags/tweet 0.82 0.96
% hashtags wrt. tokens 5.01% 5.78%
# URLs 3, 511 3, 525
# tweets > 1 hashtag 1, 520 1, 750
avg. URLs/tweet 0.88 0.88
% URLs wrt. tokens 5.36% 5.33% EUES.eu EUES.es
EUES.eu EUES.es # hashtags 4, 828 4, 608
# hashtag types 584 438
# @users 1, 947 2, 070
# avg. hashtags/tweet 1.21 1.52
avg. @users/tweet 0.49 0.52
% hashtags wrt. tokens 8.27% 8.90%
% @users wrt. tokens 3.76% 3.55%
# tweets > 1 hashtag 2, 358 2, 364
# URLs 3, 461 3, 458
TNORM TSM
avg. URLs/tweet 0.86 0.86
% URLs wrt. tokens 6.68% 5.92% # hashtags 182 1, 046
# hashtag types 157 1
TNORM TSM
# avg. hashtags/tweet 0.16 0.12
# @users 665 3, 439 % hashtags wrt. tokens 1.26% 0.85%
avg. @users/tweet 0.59 0.40 # tweets > 1 hashtag 103 744
% @users wrt. tokens 4.59% 2.78%
# URLs 69 743 Table 3: Statistics on hashtag use in each
avg. URLs/tweet 0.06 0.09 dataset.
% URLs wrt. tokens 0.47% 0.60%
4 On the Importance of the
Table 2: Statistics on user mentions (@users)
and URLs use in each corpus.
Hashtag Occurrences
This section analyses the use of hashtags in
the datasets. This study and the next one in
Section 5 follow the procedure in (Fabrizio
mentions (@user) and links is not a big issue, Gotti and Phillippe Langlais and Atefeh
they are discussed here to stand out how they Farzindar, 2014) that resulted very clear and
are used in Twitter. Table 2 gives the figures appropriate to this end. Table 3 shows some
for the use of @user and URLs in the body statistics on the occurrences of hashtags.
of the messages. @user do not seem to follow The different number of hashtags between
any pattern. The number of @user in the two formal and colloquial datasets is noticeable.
bitexts of the TweetMT datasets is opposite: The former contains more than one hashtag
the EUES datasets contain more than twice per tweet, whereas the latter contains a
@user than the CAES ones, and almost three remarkable low number of them.1 It seems
times the proportion of @user with respect to indicate that formal tweets tend to use
to the number of tokens. Similarly, the hashtags to categorise its topic and, maybe,
TNORM dataset shows a higher use of @user create a trend. This is also reflected in
than the TSM one. It is worth to note that Figure 1: the most of the formal tweets,
not all @user tokens have their counterpart in the bitexts, contain one or two hashtag,
in the translated text, even though this token whereas the most of the colloquial ones have
does not need to be translated. none.
A more interesting issue is the translation
In contrast, the use of URLs seems to be of hashtags. In terms of the number of
consistent across the two types of datasets. occurrences, each side of the bitexts contain
The four bitexts contain almost the same a similar amount. However, the number
number of URLs, and we can find almost of hashtag types in CAES.ca is much lower
one URLs in each tweet. In return, TNORM than the ones in CAES.es. A peer review
and TSM contain a remarkable small number of the hashtag sets reveals that the Spanish
of URLs, less than 0.1% per tweet. Out of versions contain more written variants than
curiosity, the majority of URLs in the bitexts their counterparts in Catalan. For instance,
link to documents in the same language the hashtag “#revistapremsa” (Catalan) has
as the tweet. Given that the selected four variants in the Spanish text: “#revista”,
authors post multilingual messages, it seems
reasonable that they also link to the right 1
The number of hashtag types in TSM is 1 because
URL when available. the corpus contains only the #HASHTAG label.
CAES.ca CAES.es
TSM
3 % tweets with a prologue 2.85% 3.42%
TNORM % tweets with an epilogue 43.6% 49.48%
% of # in a prologue 3.50% 3.61%
EUES.es % of # in an epilogue 75.72% 73.46%
EUES.eu EUES.eu EUES.es
2
CAES.es % tweets with a prologue 10.28% 10.90%
CAES.ca % tweets with an epilogue 55.23% 55.13%
% of # in a prologue 9.13% 10.63%
% of # in and epilogue 57.27% 60.11%
1
TNORM TSM
% tweets with a prologue 2.03% 2.39%
% tweets with an epilogue 5.74% 3.83%
% of # in a prologues 17.03% 20.08%
0 % of # in a epilogues 40.66% 35.09%
0% 20% 40% 60% 80% 100% Table 4: Statistics on hashtag (#) use as
prologues and epilogues in each dataset.
Figure 1: % tweets with exactly n hashtags,
for n ∈ [0, 1, 2, 3]. colloquial texts, roughly half of them appear
inline, and hence, they play a syntactic role
“#revistadeprensa”, “#revistaprensa”, and in the message. This is important since
“#revistaprensa”. they may contain an essential part of the
According to (Fabrizio Gotti and semantics and thus worth to deal with them.
Phillippe Langlais and Atefeh Farzindar, Unfortunately, hashtags contains mainly of
2014), hashtags can be classified by the out-of-vocabulary words, as discussed next
role they play in the text. They distinguish in Section 5.
between hashtags that appear at the
beginning of the text (prologue), in the text 5 On the OOV words in Twitter
(inline) and at the end of the text (epilogue). The use of out-of-vocabulary (OOV) words
Correctly identifying this role is important in Twitter has been claimed to be a hard
since a number of hashtags may have a issue. The reason is not only the high number
syntactic function inside the text (inline), or of misspellings, symbols and orthographic
can help to identify the domain of the text errors, that could be partially tackled by
(prologue and epilogue). A simple heuristic using spell-checkers, but also the use of
was used to split the tweets into these three specific lexica and lexical variants. For
parts, and the results shown are in line with instance, the use of word combinations (e.g.,
the mentioned study. We can observe, in in hashtags), the combination of different
Table 4, how the hashtag role within the languages (especially in multilingual regions,
text varies in each corpus. Although in but also English terms) and the unlimited
different proportion, the gross of hashtags in ability of the microblogging sphere to invent
the formal datasets appear in the epilogue, new terms.
which indicates there is a common practice This section gives a numerical analysis
to add any hashtag at the end of the tweet. of OOVs that occur in Twitter. In order
In contrast, the colloquial datasets have a to conduct this analysis, the datasets were
very few proportion of tweets with either processed to remove the user mentions and
a prologue or an epilogue, but a higher URLs, since them all are tokens that do
proportion of them appear in the prologues not need to be translated. Some variants
(in comparison to the formal tweets). of the datasets were built. First, only the
This behaviour may simply indicate that CAES bitext was used due the lack of a
colloquial tweets do not follow necessarily Language Model (LM) for Basque. Then,
any common practice. All datasets actually since the TNORM annotations provide the
exhibit a low rate of tweets having a prologue, corrected forms for some OOV tokens (only
although the EUES bitext show a remarkable spelling variants), they were used to build
higher number in comparison to the rest. a new dataset TNORM-S were OOVs were
Finally, it is worth to note that, although substituted with the correct word when
the number of hashtags is lower in the available. In addition, two different versions
CAES.ca CAES.es TSM CAES.es
# OOV - clean data 5.61% 5.14% # OOV - clean data 11.08% 11.26%
# OOV - no hashtags 2.81% 2.20% # OOV - no hashtags 10.30% 7.51%
ppl - clean data 603 644 ppl - clean data 591 735
ppl - no hashtags 520 543 ppl - no hashtags 591 669
TNORM TNORM-S
Table 6: Count of OOVs and the perplexity
# OOV - clean data 14.23% 12.45%
# OOV - no hashtags 13.53% 11.79%
(ppl) in the TSM and CAES.es corpora using
ppl - clean data 1, 325 1, 211 a LM trained on the TNORM corpus.
ppl - no hashtags 1, 300 1, 192
TSM
available out of the two colloquial ones. The
# OOV - clean data 9.18% new LM was used to obtain the % of OOVs
# OOV - no hashtags 8.38% and perplexity estimations on CAES.es and
ppl - clean data 1, 370
ppl - no hashtags 1, 373 TSM datasets. The results are shown in
Table 6. The % of OOVs is higher in both
Table 5: Count of OOV and perplexity (ppl) cases, most probably due the small size of the
estimation in each corpus using a LM trained corpus. However, the perplexity of the TSM
on the “El Periódico” corpus. (This parallel dataset has decreased. This seems to indicate
corpora is listed in the ELRA catalog) that the LM was able to capture a high
proportion of the particular characteristics
of colloquial tweets, and that these may be
were created out of each dataset. In the recurrent in the colloquial genre and do not
first one (clean data), the hashtags were kept appear in formal texts.
(the # symbol was removed) since they play
an important role in the text, carry part
6 Conclusions and Further Work
of the semantics of the message and need
to be translated in most of the cases. In Twitter has its own particularities that
the second dataset, all the hashtags were makes it a hard genre to deal with. This
removed. The purpose of this second version work reviews recent publications that address
is to highlight the impact of hashtags in the the problem of Twitter translation. The
perplexity estimation of the texts. number of works in this field is still scarce
Table 5 shows the results of this analysis. due the lack of corpora, but also because
As expected, colloquial datasets contain a of the lack of a gold standard and specific
higher number of OOVs. The TNORM-S evaluation methodologies that can help to
contains slightly a lower number of them in assess the quality of a tweet translation.
comparison to the non-normalised version, This work also discusses the most well-know
which indicates that the use of spell-checkers issues in the Twitter genre: the use of
and the substituion of lexical variants in hashtags and the amount of OOVs, with
not enough to deal with OOVs. This is especial focus on comparing the differences
reflected in the figures on the perplexity of between formal and colloquial texts. The
the datasets. The perplexity is high across results obtained are preliminary, but they
all the datasets, and it slightly decreases clearly show that these two registers are
after removing the hashtags from the data, different not only from a linguistic point of
indicating that the language used in the text view, but also in terms of tweet structure
is notable different from the LM. This can and content. Further work has to be done
be ascribed to the fact that the LM was to align the hashtags and the OOVs in
build using an out-of-domain corpus. In bitexts corpora and analyse the way their
turn, removing the hashtags from the data are translated. Also, the annotation layers
decreases the amount of OOVs, and seems of the TSM corpus enables the possibility
to have an impact only in the formal dataset, to fine-grain the study, for instance, by
where half of the OOVs occur in the hashtags. focusing in the differences between tweets
However, their proportion is smaller when with different communication functions. To
compared with the colloquial datasets. conclude, no major differences were found
For the sake of comparison, the same between languages, but this may be ascribed
calculation was carried on using a LM trained to the fact that the datasets were obtained
on TNORM corpus, the only corpus publicly from bitexts corpora.
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