A consensus approach to sentiment analysis

Date

2017-06-04

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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.

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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