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

Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog

2015-2016 > Academic research: refereed > Publication in refereed journa

ANALYZING IMAGE TWEETS IN MICROBLOGS

Ph.DDOCTOR OF PHILOSOPH

Credtwi:a research tool for social media credibility analysis

Abstract. In recent years, social media platforms have solidified their position as major information-sharing networks. People now also proactively look for information online and on social media for their everyday problems. There is a more sinister side to this development as well, as organizations and malevolent actors have taken the opportunity to spread false information and fake news online. For these reasons, it has become increasingly important to research the credibility of shared information. In this thesis, we designed and implemented a generalizable crowdsourcing research tool in the form of a browser plugin. Our tool, Credtwi, injects credibility questionnaires into the user’s Twitter feed. These customizable questionnaires are attached to each tweet. Utilizing Credtwi, we carried out a week-long field study where participants assessed the credibility of tweets on certain topics. Analysing these assessments and the accompanying onboarding and post-experiment questionnaires, we identified which elements affect the participants’ perceived credibility. These include factors such as the author’s verification status, the linked information source, and the author’s relevance to the topic. The participant’s perception of Twitter as an information source, in general, had lowered statistically significantly after using Credtwi for a week. Pulling together the results, the analysis, and the discussion at the end of this thesis, we contribute a timely piece of research to the domain of online content credibility. Further, we propose implications for crowdsourced credibility research with browser plugins including using a multi-dimensional credibility scale and adding cognitive load to the assessment process.Credtwi : tutkimus apuväline uskottavuuden analyysointiin sosiaalisessa mediassa. Tiivistelmä. Viime vuosina sosiaalisen median alustat ovat vakiinnuttaneet asemiaan merkittävinä tiedon jakamisverkostoina. Nykyään ihmiset myös ennakoivasti etsivät tietoa verkosta ja sosiaalisesta mediasta heidän jokapäiväisiin ongelmiinsa. Tähän kehitykseen liittyy myös pahaenteisempi puoli, kun organisaatiot ja pahantahtoiset toimijat ovat hyödyntäneet tämän mahdollisuuden levittääkseen valheellisia uutisia sekä tietoja verkossa. Näistä syistä on enenevässä määrin tärkeää tutkia jaetun tiedon uskottavuutta. Tässä diplomityössä suunnittelimme ja toteutimme joukkoustamistutkimus yleistyökalun selain lisäosan muodossa. Työkalumme, Credtwi, lisää kyselyitä uskottavuudesta käyttäjän Twitter syötteeseen. Nämä muokattavat kyselyt on yhdistettynä jokaiseen tviittiin. Credtwiä käyttäen toteutimme viikon pituisen kenttätutkimuksen, jossa osallistujat arvioivat valittujen aiheiden tviittien uskottavuutta. Näistä arvioista sekä alku- ja loppukyselyistä tunnistettiin mitkä elementit vaikuttavat havaittuun uskottavuuteen esimerkiksi tviitin kirjoittajan verifikaatio tila, linkitetty tiedonlähde sekä tviitin kirjoittajan asiaankuuluvuus aiheeseen. Osallistujien havaitsema uskottavuus Twitteristä yleisenä tiedonlähteenä laski tilastollisesti merkittävästi heidän käytettyään Credtwiä viikon ajan. Yhdistettynä tulokset, analyysit sekä loppukeskustelut edesautamme verkkosisällön uskottavuuden tutkimusalaa ajankohtaisella tutkimuksella. Lisäksi ehdotamme seuraamuksia tulevaisuuden joukkoustamistutkimuksiin, jotka hyödyntävät selain lisäosia. Näitä seuraamuksia ovat esimerkiksi moniulotteisen uskottavuusasteikon käyttö sekä kognitiivisen kuorman lisäys arviointiprosessiin

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques

With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data. Precision observed in our experiments is typically lower than the one reported by the original authors, which we attribute to the lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment