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Sentiment analysis using common-sense and context information

Authors:

Basant Agarwal,

Sonal GargAuthors Info & Claims

Computational Intelligence and Neuroscience, Volume 2015

Article No.: 30, Page 30

https://doi.org/10.1155/2015/715730

Published: 01 January 2015 Publication History

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Abstract

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.

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Cited By

Ho CChean KLim T(2024)Leveraging Machine Translation to Enhance Sentiment Analysis on Multilingual TextProceedings of the 2024 13th International Conference on Software and Computer Applications10.1145/3651781.3651819(242-248)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1145/3651781.3651819
Chauhan SShet JBeram SJagota VDighriri MAhmad MHossain MRizwan A(2023)Rule Based Fuzzy Computing Approach on Self-Supervised Sentiment Polarity Classification with Word Sense Disambiguation in Machine Translation for Hindi LanguageACM Transactions on Asian and Low-Resource Language Information Processing10.1145/357413022:5(1-21)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3574130
Punetha NJain G(2023)Bayesian game model based unsupervised sentiment analysis of product reviewsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119128214:COnline publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119128
Show More Cited By

Index Terms

Sentiment analysis using common-sense and context information
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Hardware
  1. Power and energy
    1. Power estimation and optimization
      1. Platform power issues

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience Volume 2015, Issue

January 2015

1117 pages

ISSN:1687-5265

EISSN:1687-5273

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Publisher

Hindawi Limited

London, United Kingdom

Publication History

Accepted: 23 February 2015

Revised: 19 February 2015

Published: 01 January 2015

Received: 19 August 2014

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Cited By

Ho CChean KLim T(2024)Leveraging Machine Translation to Enhance Sentiment Analysis on Multilingual TextProceedings of the 2024 13th International Conference on Software and Computer Applications10.1145/3651781.3651819(242-248)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1145/3651781.3651819
Chauhan SShet JBeram SJagota VDighriri MAhmad MHossain MRizwan A(2023)Rule Based Fuzzy Computing Approach on Self-Supervised Sentiment Polarity Classification with Word Sense Disambiguation in Machine Translation for Hindi LanguageACM Transactions on Asian and Low-Resource Language Information Processing10.1145/357413022:5(1-21)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3574130
Punetha NJain G(2023)Bayesian game model based unsupervised sentiment analysis of product reviewsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119128214:COnline publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119128
Wang CChen YZhang SZhang Q(2022)Stock market index prediction using deep Transformer modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.118128208:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.118128
Yuan PJiang LLiu JZhou DLi PGao Y(2021)Dual-Level Attention Based on a Heterogeneous Graph Convolution Network for Aspect-Based Sentiment ClassificationWireless Communications & Mobile Computing10.1155/2021/66258992021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6625899
Zhu YZheng WTang H(2020)Interactive Dual Attention Network for Text Sentiment ClassificationComputational Intelligence and Neuroscience10.1155/2020/88587172020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/8858717
de Kok SFrasincar FHung CCerny TShin DBechini A(2020)Using word embeddings for ontology-driven aspect-based sentiment analysisProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3373848(834-842)Online publication date: 30-Mar-2020
https://dl.acm.org/doi/10.1145/3341105.3373848
Gan DShen JXu MStamenkovic Z(2019)Adaptive Learning Emotion Identification Method of Short Texts for Online Medical Knowledge Sharing CommunityComputational Intelligence and Neuroscience10.1155/2019/16043922019Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1155/2019/1604392
Zhang ZBi X(2018)RETRACTED ARTICLE: Research and Experiment of Intelligent Natural Language Processing AlgorithmsWireless Personal Communications: An International Journal10.1007/s11277-018-5316-2102:4(2927-2939)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11277-018-5316-2
Hoffen MHagge MBetzing JChasin F(2018)Leveraging social media to gain insights into service deliveryInformation Systems and e-Business Management10.1007/s10257-017-0358-716:2(247-269)Online publication date: 1-May-2018
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