Browsing by Author "Pemasiri, A"

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Embargo
Cheap food or friendly staff? Weighting hierarchical aspects in the restaurant domain
(IEEE, 2016-04-05) Panchendrarajan, R; Murugaiah, B; Prakhash, S; Ahamed, N; Ranathunga, S; Pemasiri, A
In aspect-level opinion mining, each aspect is assigned a rating based on customer reviews. More often than not, these aspects exhibit a hierarchical relationship, and the restaurant domain is no difference. With the existence of such hierarchical relationships, rating of an aspect is based on the composite score of its sub-elements. However, the influence of these sub-aspects on the score of a parent aspect is not uniform, since some sub-aspects are perceived more important than others. Therefore, when calculating the composite score for an aspect, influence of each sub-aspect should be weighted according to its perceived importance. Identifying weights for different aspects is addressed as the problem of multi-attribute weighting. However the existing approaches do not utilize the relationships between aspects to find weights. This paper presents an approach to find weights for aspects that exhibit hierarchical relationships in restaurant domain using an improved version of the Analytic Hierarchy Process (AHP), one of the Multi Attribute Decision Making Techniques (MADTs). Different aspects of the restaurant domain are modeled as a hierarchy and weights for aspects are calculated using AHP. Occurrence counts of aspects in restaurant reviews are used to obtain the relative importance of aspects. This approach provides acceptable consistency ratios for the pairwise comparison matrices obtained for each level in the hierarchy of aspects.
Open Access
Eatery: a multi-aspect restaurant rating system
(Association for Computing Machinery, 2017-07-04) Panchendrarajan, R; Ahamed, N; Sivakumar, p; Murugaiah, B; Ranathunga, S; Pemasiri, A
This paper presents Eatery, a multi-aspect restaurant rating system that identifies rating values for different aspects of a restaurant by means of aspect-level sentiment analysis. Eatery uses a hierarchical taxonomy that represents relationships between various aspects of the restaurant domain that enables finding the sentiment score of an aspect as a composite sentiment score of its sub-aspects. The system consists of a word co-occurrence based technique to identify multiple implicit aspects appearing in a sentence of a review. An improved version of Analytic Hierarchy Process (AHP) is used to obtain weights specific to a restaurant by utilizing the relationships between aspects, which allows finding the composite sentiment score for each aspect in the taxonomy. The system also has the ability to rate individual food items and food categories. An improved version of Single Pass Partition Method (SPPM) is used to categorise food names to obtain food categories.
Open Access
Implicit aspect detection in restaurant reviews using cooccurence of words
(Proceedings of the 7th Workshop on computational approaches to subjectivity, sentiment and social media analysis, 2016-06) Panchendrarajan, R; Ahamed, N; Murugaiah, B; Sivakumar, P; Ranathunga, S; Pemasiri, A
For aspect-level sentiment analysis, the important first step is to identify the aspects and their associated entities present in customer reviews. Aspects can be either explicit or implicit, where the identification of the latter is more difficult. For restaurant reviews, this difficulty is escalated due to the vast number of entities and aspects present in reviews. The problem of implicit aspect identification has been studied for customer reviews in different domains, including restaurant reviews. However, the existing work for implicit aspect identification in customer reviews has the limitation of choosing at most one implicit aspect for each sentence. Furthermore, they deal only with a limited set of aspects related to a particular domain, thus have not faced the problem of ambiguity that arises when an opinion word is used to describe different aspects. This paper presents a novel approach for implicit aspect detection, which overcomes these two limitations. Our approach yields an F1- measure of 0.842 when applied for a set of restaurant reviews collected from Yelp.