Hybrid sentiment classification on twitter aspect-based sentiment analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F18%3A50014741" target="_blank" >RIV/62690094:18450/18:50014741 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s10489-017-1098-6" target="_blank" >http://dx.doi.org/10.1007/s10489-017-1098-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10489-017-1098-6" target="_blank" >10.1007/s10489-017-1098-6</a>

Result language

angličtina

Original language name

Hybrid sentiment classification on twitter aspect-based sentiment analysis

Original language description

Social media sites and applications, including Facebook, YouTube, Twitter and blogs, have become major social media attractions today. The huge amount of information from this medium has become an attractive resource for organisations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. Early work on sentiment analysis approached this problem at a document-level, where the overall sentiment was identified, rather than the details of the sentiment. This research took into account the use of an aspect-based sentiment analysis on Twitter in order to perform a finer-grained analysis. A new hybrid sentiment classification for Twitter is proposed by embedding a feature selection method. A comparison of the accuracy of the classification by the principal component analysis (PCA), latent semantic analysis (LSA), and random projection (RP) feature selection methods are presented in this paper. Furthermore, the hybrid sentiment classification was validated using Twitter datasets to represent different domains, and the evaluation with different classification algorithms also demonstrated that the new hybrid approach produced meaningful results. The implementations showed that the new hybrid sentiment classification was able to improve the accuracy performance from the existing baseline sentiment classification methods by 76.55, 71.62 and 74.24%, respectively.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

APPLIED INTELLIGENCE

ISSN

0924-669X

e-ISSN

—

Volume of the periodical

48

Issue of the periodical within the volume

5

Country of publishing house

DE - GERMANY

Number of pages

15

Pages from-to

1218-1232

UT code for WoS article

000429401100011

EID of the result in the Scopus database

2-s2.0-85037730962