Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis […]
The relevance and importance of contextualizing data analytics is described.
Qualitative characteristics might form the context of quantitative analysis.
Topics that are at issue include: contrast, baselining, secondary data sources,
supplementary data sources, dynamic and heterogeneous data. In geometric data
analysis, especially with the Correspondence Analysis platform, various case
studies are both experimented with, and are reviewed. In such aspects as
paradigms followed, and technical implementation, implicitly and explicitly, an
important point made is the major relevance of such work for both burgeoning
analytical needs and for new analytical areas including Big Data analytics, and
so on. For the general reader, it is aimed to display and describe, first of
all, the analytical outcomes that are subject to analysis here, and then
proceed to detail the more quantitative outcomes that fully support the
analytics carried out.
