Authors:
M. Fernandes Caíña
;
R. Díaz Redondo
and
A. Fernández Vilas
Affiliation:
University of Vigo, Spain
Keyword(s):
Opinion Mining, Sentiment Analysis, Business Decision-Making, Longitudinal Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Intelligence Applications
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Pre-Processing and Post-Processing for Data Mining
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
Social networks have become a major source of information, opinions and sentiments about almost any
subject. The purpose of this work is to provide evidences of the applicability of opinion mining methods to
find out how some events may impact into public opinion about a brand, product or service. We report an
experiment that mined Twitter data related to two particular brands during specific periods that have been
selected from events that was supposed to affect the user’s perception. To find out conclusions, the
methodology of the experiment applies several pre-processing techniques to extract sentiment information
from the posts (e.g., case alterations, Part-of-Speech tagging using a Natural Language Toolkit, symbols
removal, sentence and n-gram separation). The SenticNet 2 Corpus is used for polarity classification by
means of a supervised algorithm where several threshold values are defined to mark positive, negative and
neutral opinions. A longitudinal inspection of the polariz
ed results on histograms allows identifying the "hot
spots" and relating them to real world events. Although this paper shows the finding in our initial
experiments, the ultimate goal of the research initiative, which we call Marble, is to provide a cloud solution
for early detection of opinion shifts by the automatic classification of events according to their impact on
opinion (propagation speed, intensity and duration), and its relationship with the normal behavior around a
brand, product or service.
(More)