A T1OWA Fuzzy Linguistic Aggregation Methodology for Searching Feature-based Opinions.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Online services such as Amazon, Tripadvisor, Ebay, etc., allow users to express sentiments about different products or services. Not only that, in some cases it is also possible to express sentiments about the different features characterizing those products or services. Most users express sentiments about individual features by using numerical values, which sometimes do not allow users to reflect properly what they are meaning and therefore they are misleading. To overcome this key issue and make users’ opinions in online services more comprehensive, a new methodology for representing sentiments using linguistic term sets instead of numerical values is presented. In addition, this methodology will allow to implement importance degrees on the different features characterizing users’ opinions. From both sentiments and importance of the features, the most important opinions for each user is derived via an aggregation step based on the Type-1 Ordered Weighted Averaging (T1OWA) operator, which is able to aggregate the corresponding fuzzy set representations of linguistic terms. Furthermore, the final output of the T1OWA based-search process can easily be interpreted by users because it is always of the same type (fuzzy) and defined in the same domain of the original fuzzy linguistic labels. A case study is presented where the T1OWA operator methodology is used to assess different opinions according to different user profiles

Sentiment Analysis of Smartphone Accounting Application Users

The focus of this research is to summarize the reviews conducted by accounting application users to explore what aspects they like about the accounting application. This research uses review sentences with a total of 4923 review sentences on Google and Apple platforms. The review mining method used in this study implements the Feature-Based Summarization (FBS). The conclusion of this study is that there are six product features that are preferred by accounting application users. The product features are reports, transactions, bookkeeping, profit, category, and customers. This research has explored product features in accounting applications, but not all product features are discussed by users. Therefore, the discussion on review sentences focuses on the six product features. This study is able to provide practical recommendations to Small-Medium Enterprises (SMEs) actors in making smartphone-based application decisions they will use. This study recommends SMEs to use accounting applications with the above product features. This is because the strong discussion of opinions on product features explains the preference for product features for actors in helping them prepare financial reports. As qualitative research, this study does not have the ability to generalize the results of the study to a population