Hybrid sentiment classification on twitter aspect-based sentiment analysis

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid sentiment classification on twitter aspect-based sentiment analysis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Hybrid sentiment classification on twitter aspect-based sentiment analysis

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

APPLIED INTELLIGENCE

ISSN

0924-669X

e-ISSN

—

Svazek periodika

48

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

15

Strana od-do

1218-1232

Kód UT WoS článku

000429401100011

EID výsledku v databázi Scopus

2-s2.0-85037730962