Sentiment Component Extraction from Dependency Parse for Hindi

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ATRK6H5P7" target="_blank" >RIV/00216208:11320/23:TRK6H5P7 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164666277&doi=10.1007%2f978-981-99-1624-5_36&partnerID=40&md5=cc3aa8abf021617cffc4bdd9e120ae9a" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164666277&doi=10.1007%2f978-981-99-1624-5_36&partnerID=40&md5=cc3aa8abf021617cffc4bdd9e120ae9a</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-99-1624-5_36" target="_blank" >10.1007/978-981-99-1624-5_36</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sentiment Component Extraction from Dependency Parse for Hindi

Popis výsledku v původním jazyce

"With the advent of Web 2.0, Natural Language Processing (NLP) gained attention and a new dimension in the NLP application arena. Sentiment analysis is one such popular modern NLP application that tries to extract the feeling, emotions, opinions, etc., expressed in digital text using NLP techniques. Sentiment analysis has become a subtle application over the last decade and found a firm foundation in AI applications. This is especially applicable to product services, reviews, and recommendations domains. It tries to quantify the sentiment better and helps understand the views expressed in a text leading to better decision-making. There is a variety of approaches available for carrying out sentiment analysis ranging from naive to sophisticated machine learning approaches. However, less attention is being paid to linguistics aspects. We undertook a study to project sentiment analysis concerning linguistic dimensions of natural language text which is the least-explored approach to sentiment analysis. Sentiment components of a text are deeply rooted in its syntactic constituents. The only way to figure out the syntactic constituents is parsing. We have tried to portray the use of dependency parsing for extracting the sentiment components from Hindi input text. We have explored the applications of various dependency relations to derive the possible sentiment compositionality from Hindi sentences. Our study and findings related to sentiment components extraction from dependency parse are presented in this paper with a linguistic insight. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd."

Název v anglickém jazyce

Sentiment Component Extraction from Dependency Parse for Hindi

Popis výsledku anglicky

"With the advent of Web 2.0, Natural Language Processing (NLP) gained attention and a new dimension in the NLP application arena. Sentiment analysis is one such popular modern NLP application that tries to extract the feeling, emotions, opinions, etc., expressed in digital text using NLP techniques. Sentiment analysis has become a subtle application over the last decade and found a firm foundation in AI applications. This is especially applicable to product services, reviews, and recommendations domains. It tries to quantify the sentiment better and helps understand the views expressed in a text leading to better decision-making. There is a variety of approaches available for carrying out sentiment analysis ranging from naive to sophisticated machine learning approaches. However, less attention is being paid to linguistics aspects. We undertook a study to project sentiment analysis concerning linguistic dimensions of natural language text which is the least-explored approach to sentiment analysis. Sentiment components of a text are deeply rooted in its syntactic constituents. The only way to figure out the syntactic constituents is parsing. We have tried to portray the use of dependency parsing for extracting the sentiment components from Hindi input text. We have explored the applications of various dependency relations to derive the possible sentiment compositionality from Hindi sentences. Our study and findings related to sentiment components extraction from dependency parse are presented in this paper with a linguistic insight. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd."

Druh

D - Stať ve sborníku

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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

"Lecture Notes in Networks and Systems"

ISBN

978-981991623-8

ISSN

2367-3370

e-ISSN

—

Počet stran výsledku

15

Strana od-do

495-509

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Singapore

Místo konání akce

Singapore

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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