64,175 research outputs found
Replication issues in syntax-based aspect extraction for opinion mining
Reproducing experiments is an important instrument to validate previous work
and build upon existing approaches. It has been tackled numerous times in
different areas of science. In this paper, we introduce an empirical
replicability study of three well-known algorithms for syntactic centric
aspect-based opinion mining. We show that reproducing results continues to be a
difficult endeavor, mainly due to the lack of details regarding preprocessing
and parameter setting, as well as due to the absence of available
implementations that clarify these details. We consider these are important
threats to validity of the research on the field, specifically when compared to
other problems in NLP where public datasets and code availability are critical
validity components. We conclude by encouraging code-based research, which we
think has a key role in helping researchers to understand the meaning of the
state-of-the-art better and to generate continuous advances.Comment: Accepted in the EACL 2017 SR
Active learning in annotating micro-blogs dealing with e-reputation
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 shows that reducing noise might induce the loss of
crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science -
Vol 3 - Contextualisation digitale - 201
A Tangled Web: The Perceived Influence of Broad-Based Black Economic Empowerment Legislation on Corporate Social Investment in South Africa
Since 2004 South Africa has had in place legislation that regulates the responsibilities of business to the transformation of society, and this regulation includes an element that relates to corporate philanthropy.To date, however, very little has been documented about the influence of this legislation on corporate philanthropy. A new research report by Halima Mahomed, A Tangled Web: The Perceived Influence of Broad-Based Black Economic Empowerment Legislation on Corporate Social Investment in South Africa, aims to partially fill this gap. The research explores the perceived influence of the legislation on issues such as the extent, flexibility and approaches to giving; highlights the limitations that arise from the structure and framework of the legislation; and interrogates some of its unintended consequences.As discussions on the feasibility of regulatory mechanisms gain traction in other places, it is hoped that this research will help to raise key issues for consideration and exploration
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