Browsing by Author "Prakhash, S"

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    Categorizing food names in restaurant reviews
    (2016-04) Prakhash, S; Nazick, A; Panchendrarajan, R; Brunthavan, M; Ranathunga, S; Pemasiri, A; Jayasekara, AGBP; Bandara, HMND; Amarasinghe, YWR
    There are many aspects such as food, service, and ambience that a customer would look for, when deciding on a restaurant to dine in. Among these aspects, the type of food it sells and the food quality are the most important. Therefore, when automatically rating restaurants based on customer reviews, the food aspect plays a major role. There exists some research on rating individual food items in a restaurant. However, a potential customer requires not the ranking of an individual food item, but the ranking of a particular food category in general. In order to do that, a categorization of food names is required. This paper presents two techniques for food name categorization using document similarity measurements.
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    Cheap food or friendly staff? weighting hierarchical aspects in the restaurant domain
    (IEEE, 2016-05) Panchendrarajan, R; Murugaiah, B; Prakhash, S; Ahamed, MNN; Ranathunga, S; Pemasiri, A; Jayasekara, AGBP; Bandara, HMND; Amarasinghe, YWR
    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.