6,013 research outputs found
A Machine Learning Approach For Opinion Holder Extraction In Arabic Language
Opinion mining aims at extracting useful subjective information from reliable
amounts of text. Opinion mining holder recognition is a task that has not been
considered yet in Arabic Language. This task essentially requires deep
understanding of clauses structures. Unfortunately, the lack of a robust,
publicly available, Arabic parser further complicates the research. This paper
presents a leading research for the opinion holder extraction in Arabic news
independent from any lexical parsers. We investigate constructing a
comprehensive feature set to compensate the lack of parsing structural
outcomes. The proposed feature set is tuned from English previous works coupled
with our proposed semantic field and named entities features. Our feature
analysis is based on Conditional Random Fields (CRF) and semi-supervised
pattern recognition techniques. Different research models are evaluated via
cross-validation experiments achieving 54.03 F-measure. We publicly release our
own research outcome corpus and lexicon for opinion mining community to
encourage further research
Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data
In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres
Detecting and Monitoring Hate Speech in Twitter
Social Media are sensors in the real world that can be used to measure the pulse of societies.
However, the massive and unfiltered feed of messages posted in social media is a phenomenon that
nowadays raises social alarms, especially when these messages contain hate speech targeted to a
specific individual or group. In this context, governments and non-governmental organizations
(NGOs) are concerned about the possible negative impact that these messages can have on individuals
or on the society. In this paper, we present HaterNet, an intelligent system currently being used by
the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that
identifies and monitors the evolution of hate speech in Twitter. The contributions of this research
are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social
network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on
hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification
approaches based on different document representation strategies and text classification models. (4)
The best approach consists of a combination of a LTSM+MLP neural network that takes as input the
tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area
under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the
literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation
grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge
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
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