A consensus approach to sentiment analysis

Date

2017-06-04

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Conference

Peer reviewed

Yes

Abstract

There are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnosing of an illness or parliament members looking for consensus on a specific law being passed. In this article we present a method that utilises Induced Ordered Weighted Averaging (IOWA) operators to aggregate a majority opinion from a number of Sentiment Analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers. Previously determined sentence intensity polarity by different SA classification methods are used as input to a specific IOWA operator. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier `most' (IOWA_most) proved to yield superior results compared to those achieved when utilising other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques.

Description

Keywords

Hybrid Sentiment Analysis Method, Naïve Bayes, Maximum Entropy, Consensus, Majority Support, Sentiment Aggregation, IOWA operaor

Citation

Appel O., Chiclana F., Carter J., Fujita H. (2017) A Consensus Approach to Sentiment Analysis. In: Benferhat S., Tabia K., Ali M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science, vol 10350. Springer, Cham

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Research Institute

Institute of Artificial Intelligence (IAI)