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        <title>Graph Based Sentiment Aggregation using ConceptNet Ontology</title>
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        <namePart type="given">Srikanth</namePart>
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    <abstract>The sentiment aggregation problem accounts for analyzing the sentiment of a user towards various aspects/features of a product, and meaningfully assimilating the pragmatic significance of these features/aspects from an opinionated text. The current paper addresses the sentiment aggregation problem, by assigning weights to each aspect appearing in the user-generated content, that are proportionate to the strategic importance of the aspect in the pragmatic domain. The novelty of this paper is in computing the pragmatic significance (weight) of each aspect, using graph centrality measures (applied on domain specific ontology-graphs extracted from ConceptNet), and deeply ingraining these weights while aggregating the sentiments from opinionated text. We experiment over multiple real-life product review data. Our system consistently outperforms the state of the art - by as much as a F-score of 20.39% in one case.</abstract>
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%0 Conference Proceedings
%T Graph Based Sentiment Aggregation using ConceptNet Ontology
%A Tamilselvam, Srikanth
%A Nagar, Seema
%A Mishra, Abhijit
%A Dey, Kuntal
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F tamilselvam-etal-2017-graph
%X The sentiment aggregation problem accounts for analyzing the sentiment of a user towards various aspects/features of a product, and meaningfully assimilating the pragmatic significance of these features/aspects from an opinionated text. The current paper addresses the sentiment aggregation problem, by assigning weights to each aspect appearing in the user-generated content, that are proportionate to the strategic importance of the aspect in the pragmatic domain. The novelty of this paper is in computing the pragmatic significance (weight) of each aspect, using graph centrality measures (applied on domain specific ontology-graphs extracted from ConceptNet), and deeply ingraining these weights while aggregating the sentiments from opinionated text. We experiment over multiple real-life product review data. Our system consistently outperforms the state of the art - by as much as a F-score of 20.39% in one case.
%U https://aclanthology.org/I17-1053
%P 525-535

Markdown (Informal)

[Graph Based Sentiment Aggregation using ConceptNet Ontology](https://aclanthology.org/I17-1053) (Tamilselvam et al., IJCNLP 2017)

• Graph Based Sentiment Aggregation using ConceptNet Ontology (Tamilselvam et al., IJCNLP 2017)

ACL

• Srikanth Tamilselvam, Seema Nagar, Abhijit Mishra, and Kuntal Dey. 2017. Graph Based Sentiment Aggregation using ConceptNet Ontology. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 525–535, Taipei, Taiwan. Asian Federation of Natural Language Processing.