8/13/2019 uno o dos problemas
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Topic Models from Twitter Hashtags
December 2, 2013
1 Problem Definition
Topic modelling is about capturing knowledge from a set of
semantically related documents in a mathematical frame-work that
represents and abstraction of what the topic means. Typical problem
of topic modelling involves a documentcollection and a procedure to
discover which topics occurs in. Its output is statistical
information about what topicsappears in documents, normally as a
probability distribution (or a set of distributions).
Several machine learning tasks take advantage of topic models
using them as alternative or complementary repre-sentations for
items. Topic modelling also allows to discover groups of documents
or groups of features that containsrelated information. Groups of
terms represents extensive descriptions of what the topics are,
while collections actmuch like implicit descriptions to learn from.
Some methods try to discover how many topics there are in a
collectionwhile for others it is necessary to manually determine
the number of topics the algorithm must consider.
Techniques of topic modelling are usually applied on large
collections of extense documents as scientific literatureor news,
where the topic stays relatively unchanged so the effect of time
can be ignored. However, the popularizationsof user generated real
time streams of textual information raises new problems that known
procedures does not solvecompletely.
On the other hand, this kind of sparse and unstructured data are
plenty of information that could be exploded if it ispossible to
gather together messages that potentially refers to same subjects.
Most extensively used applications rangefrom training messages
classifiers to user preferences prediction, items and news
recommendation and personalizedsearch. High rates at which messages
are generated and its availability in real time also motivates the
exploitation ofsuch source of news, interests and opinions.
Some of this documents also contains semi-structural
information, as hashtags or social tags, that are usuallyintegrated
in models as evidence of the relevance to a given topic, but as
many people uses these tags indiscriminately,
they also represents a potential source of noise.The big picture
about the scenario we are trying to mine information from can be
summarized in next assertions:
Social generated messages, as tweets or sms, are short, normally
with a fixed maximum length.
They are noisy in both grammatical and semantic manners
When containing social labels or hashtags, these are also
susceptible of noise, because some users attach tagsthat does not
corresponds with the semantic content of the message.
The number of messages about a given topic or event is function
of time and the social impactof the topic.
The content of the messages about certain topic changes in time
accordingly to the public interest or opinions.
Last two points relates to the dynamic aspect of user generated
content and means that information in social
environments is not static as in closed document collections.
Information in social environments grows and is updatedaccording
with the development of the events it refers to. In our model of
the problem this is called the model decayand clearly is a function
of time.
Once stated the typical scenario for topic modelling and the
characteristics of the documents domain we are tryingto capture,
our works is concerned in developing a solution that allows to
capture models that better describe theinformation in such dynamic
social environments. The general and specific questions that guide
our works can beenunciated as follows:
Can higher-order relations between words be used as features to
represents items?
Does they capture more time persistent information that allow us
to identify relevant documents generated moretime after the
training that traditional methods with acceptable accuracy?
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