Neural networks with emotion associations, topic modeling and supervised term weighting for sentiment analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F21%3A39917730" target="_blank" >RIV/00216275:25410/21:39917730 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.worldscientific.com/doi/abs/10.1142/S0129065721500131" target="_blank" >https://www.worldscientific.com/doi/abs/10.1142/S0129065721500131</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0129065721500131" target="_blank" >10.1142/S0129065721500131</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural networks with emotion associations, topic modeling and supervised term weighting for sentiment analysis

Popis výsledku v původním jazyce

Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.

Název v anglickém jazyce

Neural networks with emotion associations, topic modeling and supervised term weighting for sentiment analysis

Popis výsledku anglicky

Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.

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

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

International Journal of Neural Systems

ISSN

0129-0657

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

18

Strana od-do

2150013

Kód UT WoS článku

000696596800004

EID výsledku v databázi Scopus

2-s2.0-85100784177