@inproceedings{majumder-etal-2018-iarm,
title = "{IARM}: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis",
author = "Majumder, Navonil and
Poria, Soujanya and
Gelbukh, Alexander and
Akhtar, Md. Shad and
Cambria, Erik and
Ekbal, Asif",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1377",
doi = "10.18653/v1/D18-1377",
pages = "3402--3411",
abstract = "Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6{\%} on average in two distinct domains: restaurant and laptop.",
}
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<abstract>Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6% on average in two distinct domains: restaurant and laptop.</abstract>
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%0 Conference Proceedings
%T IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis
%A Majumder, Navonil
%A Poria, Soujanya
%A Gelbukh, Alexander
%A Akhtar, Md. Shad
%A Cambria, Erik
%A Ekbal, Asif
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F majumder-etal-2018-iarm
%X Sentiment analysis has immense implications in e-commerce through user feedback mining. Aspect-based sentiment analysis takes this one step further by enabling businesses to extract aspect specific sentimental information. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. We show that our method outperforms the state of the art by 1.6% on average in two distinct domains: restaurant and laptop.
%R 10.18653/v1/D18-1377
%U https://aclanthology.org/D18-1377
%U https://doi.org/10.18653/v1/D18-1377
%P 3402-3411
Markdown (Informal)
[IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis](https://aclanthology.org/D18-1377) (Majumder et al., EMNLP 2018)
ACL