What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Collaboration or competition: The impact of incentive types on urban cycling

Bicycling is an important mode of transport for cities and many cities are interested in promoting its uptake by a larger portion of the population. Several cycling mobile applications primarily rely on competition as a motivation strategy for urban cyclists. Yet, collaboration may be equally useful to motivate and engage cyclists. The present research reports on an experiment comparing the impact of collaboration-based and competition-based rewards on users’ enjoyment, satisfaction, engagement with, and intention to cycle. It involved a total of 57 participants in three European cities: Münster (Germany), Castelló (Spain), and Valletta (Malta). Our results show participants from the study reporting higher enjoyment and engagement with cycling in the collaboration condition. However, we did not find a significant impact on the participants’ worldview when it comes to the intentions to start or increase cycling behavior. The results support the use of collaboration-based rewards in the design of game-based applications to promote urban cycling

An Empirical Study on Instance Selection Strategies in Self-training for Sentiment Analysis

Sentiment analysis is a crucial task in natural language processing that involves identifying and extracting subjective sentiment from text. Self-training has recently emerged as an economical and efficient technique for developing sentiment analysis models by leveraging a small amount of labeled data and a larger amount of unlabeled data. However, the performance of a self-training procedure heavily relies on the choice of the instance selection strategy, which has not been studied thoroughly. This paper presents an empirical study on various instance selection strategies for self-training on two public sentiment datasets, and investigates the influence of the strategy and hyper-parameters on the performance of self-training in various few-shot settings.Comment: 6 pages, 2 figure

MAKING THE RIGHT IMPRESSION FOR CORPORATE REPUTATION: ANALYZING IMPRESSION MANAGEMENT OF FINANCIAL INSTITUTIONS IN SOCIAL MEDIA

The concept of corporate reputation reflects the standing of a firm based on the public perception. Firms with high corporate reputation are better able to sustain superior performance. During the financial crisis, the corporate reputation of financial institutions has decreased resulting in a bad perception of the financial sector by the public. However, improving their corporate reputation is of major importance for financial institutions as they are heavily dependent on the trust of the market participants. Thus, managing the public perception about a financial institution is of critical importance. Therefore, firms can deploy organizational impression management tactics to influnce how the public perceives them. In this research, we obtained data from 54 corporate Twitter accounts of 36 financial institutions broadcasting more than 21,000 messages between October 2012 and June 2013 to the public. Thereby, we combine two former separately discussed theories in a single research approach and examine which organizational impression management tactics can be identified based on the social media messages regarding corporate reputation. The results indicate that based on the different dimensions of corporate reputation financial institutions deploy different organizational impression management tactics in social media to manage their reputation

Verifying baselines for crisis event information classification on Twitter

Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software