A systematic literature review on sentiment analysis techniques, challenges, and future trends

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A47G87XT7" target="_blank" >RIV/00216208:11320/26:47G87XT7 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10115-025-02365-x" target="_blank" >http://dx.doi.org/10.1007/s10115-025-02365-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10115-025-02365-x" target="_blank" >10.1007/s10115-025-02365-x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A systematic literature review on sentiment analysis techniques, challenges, and future trends

Popis výsledku v původním jazyce

In the last ten years, social media site mining, including Twitter, Facebook, Instagram, and all other websites, has become a popular area of study. In the world of digital media today, people are getting more and more vocal because they love to share their opinions. User-generated material abounds on social media platforms and apps such as Facebook, WhatsApp, and Twitter, providing wealthy content to gather sentiments. Comments are another way that the most active and social voices can make themselves heard, which in turn gives us a window into their feelings. Sentiment analysis is the task of comprehending the emotions and opinions conveyed in text and other media. It has various applications in domains along with social media such as e-commerce, health, politics, and marketing. This paper delineates the generic process of sentiment analysis and reviews the main methods, challenges, and trends in this field. The main goal of this survey paper is to survey the current state-of-the-art research works on sentiment analysis (SA) techniques and related fields and to compare the performance of different deep learning models for sentiment polarity. The paper also discusses the recent studies that have employed machine learning, deep learning, and hybrid models to address sentiment polarity problems, which is the categorization of text into positive, negative, or neutral sentiments. The paper evaluates the results of different models on a series of datasets. The paper’s main contributions are the elaborate classifications of numerous recent articles and the demonstration of the recent research directions in sentiment analysis and its related fields. The paper aims to provide a comprehensive overview of SA techniques with succinct details. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

Název v anglickém jazyce

A systematic literature review on sentiment analysis techniques, challenges, and future trends

Popis výsledku anglicky

In the last ten years, social media site mining, including Twitter, Facebook, Instagram, and all other websites, has become a popular area of study. In the world of digital media today, people are getting more and more vocal because they love to share their opinions. User-generated material abounds on social media platforms and apps such as Facebook, WhatsApp, and Twitter, providing wealthy content to gather sentiments. Comments are another way that the most active and social voices can make themselves heard, which in turn gives us a window into their feelings. Sentiment analysis is the task of comprehending the emotions and opinions conveyed in text and other media. It has various applications in domains along with social media such as e-commerce, health, politics, and marketing. This paper delineates the generic process of sentiment analysis and reviews the main methods, challenges, and trends in this field. The main goal of this survey paper is to survey the current state-of-the-art research works on sentiment analysis (SA) techniques and related fields and to compare the performance of different deep learning models for sentiment polarity. The paper also discusses the recent studies that have employed machine learning, deep learning, and hybrid models to address sentiment polarity problems, which is the categorization of text into positive, negative, or neutral sentiments. The paper evaluates the results of different models on a series of datasets. The paper’s main contributions are the elaborate classifications of numerous recent articles and the demonstration of the recent research directions in sentiment analysis and its related fields. The paper aims to provide a comprehensive overview of SA techniques with succinct details. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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

—

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

—

Svazek periodika

67

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

68

Strana od-do

3967-4034

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85218679587