[1406.0032] Comparing and Combining Sentiment Analysis Methods

ByPollyanna Goncalves Matheus Araujo Fabricio Benevenuto Meeyoung Cha analysis, computation, current, language, networks, online, popular, proceedings, science, social, study Comments Off

Computer Science > Computation and Language

Title:
Comparing and Combining Sentiment Analysis Methods

Authors:Pollyanna Gonçalves

Matheus Araújo

Fabrício Benevenuto

Meeyoung Cha

(Submitted on 30 May 2014)

Abstract: Several messages express opinions about events, products, and services,
political views or even their author’s emotional state and mood. Sentiment
analysis has been used in several applications including analysis of the
repercussions of events in social networks, analysis of opinions about products
and services, and simply to better understand aspects of social communication
in Online Social Networks (OSNs). There are multiple methods for measuring
sentiments, including lexical-based approaches and supervised machine learning
methods. Despite the wide use and popularity of some methods, it is unclear
which method is better for identifying the polarity (i.e., positive or
negative) of a message as the current literature does not provide a method of
comparison among existing methods. Such a comparison is crucial for
understanding the potential limitations, advantages, and disadvantages of
popular methods in analyzing the content of OSNs messages. Our study aims at
filling this gap by presenting comparisons of eight popular sentiment analysis
methods in terms of coverage (i.e., the fraction of messages whose sentiment is
identified) and agreement (i.e., the fraction of identified sentiments that are
in tune with ground truth). We develop a new method that combines existing
approaches, providing the best coverage results and competitive agreement. We
also present a free Web service called iFeel, which provides an open API for
accessing and comparing results across different sentiment methods for a given
text.