The Role of Electronic Word of Mouth in Shaping Consumer Attitudes

The purpose of this research was to investigate the role of electronic word of mouth (eWOM) in shaping consumer attitudes towards various products and services with concentration on the consumer attitude change. eWOM has long been proven to play an important role in influencing consumer attitudes and has been researched from a variety of perspectives. This study attempts to look deeper into the process of consumer attitude change by applying as the central theory of the study the Elaboration Likelihood Model of Persuasion by Petty and Cacioppo. In the processes of examining the background academic and empirical research the Internet and Web 2.0 are closely depicted in order to understand how throughout the past centuries technology allowed the rise of various mediums where consumers can not only share their opinions online about products and services but also communicate with other consumers. Manuel Castel’s Internet Galaxy, Gildin’s, Carl and Noland’s, Hennig-Thurau, Gwinner, Walsh and Gremler’s researches on eWOM are the central works that helped to shape both the theoretical and empirical parts of this study. The mixed method approach was chosen as a research method for this study. An online survey was conducted via the Surveymonkey.com platform and eight qualitative in-depth interviews were conducted. The results of the study show that central route queues as text quality and text argumentativeness are more prominent among the research subjects and the peripheral route queues: source credibility and source expertise did not show considerable significance. Also more experience and participation consumers have with user-rating websites and applications more inclined they are to elaborate on the central route cues and are more likely to search for opinions that they consider rational and credible. Also these respondents are less inclined to search for ratings that confirm their existing beliefs about products or services. Less experience/participation they have about eWOM more likely they are to search for reviews confirmatory to their own.

HFST—Framework for Compiling and Applying Morphologies

HFST–Helsinki Finite-State Technology ( hfst.sf.net ) is a framework for compiling and applying linguistic descriptions with finite-state methods. HFST currently connects some of the most important finite-state tools for creating morphologies and spellers into one open-source platform and supports extending and improving the descriptions with weights to accommodate the modeling of statistical information. HFST offers a path from language descriptions to efficient language applications in key environments and operating systems. HFST also provides an opportunity to exchange transducers between different software providers in order to get the best out of each finite-state library.

Do wordnets also improve human performance on NLP tasks?

FinnWordNet is a wordnet for Finnish that complies with the format of the Princeton WordNet (PWN) (Fellbaum, 1998). It was built by translating the PrincetonWordNet 3.0 synsets into Finnish by human translators. It is open source and contains 117000 synsets. The Finnish translations were inserted into the PWN structure resulting in a bilingual lexical database. In natural language processing (NLP), wordnets have been used for infusing computers with semantic knowledge assuming that humans already have a sufficient amount of this knowledge. In this paper we present a case study of using wordnets as an electronic dictionary. We tested whether native Finnish speakers benefit from using a wordnet while completing English sentence completion tasks. We found that using either an English wordnet or a bilingual English Finnish wordnet significantly improves performance in the task. This should be taken into account when setting standards and comparing human and computer performance on these tasks.