Reputation Generation Research Articles

Aggregating Users’ Online Opinions Attributes and News Influence for Cryptocurrencies Reputation Generation

Reputation generation systems are decision-making tools used in different domains including e-commerce, tourism, social media events, etc. Such systems generate a numerical reputation score by analyzing and mining massive amounts of various types of user data, including textual opinions, social interactions, shared images, etc. Over the past few years, users have been sharing millions of tweets related to cryptocurrencies. Yet, no system in the literature was designed to handle the unique features of this domain with the goal of automatically generating reputation and supporting investors’ and users’ decision-making. Therefore, we propose the first financially oriented reputation system that generates a single numerical value from user-generated content on Twitter toward cryptocurrencies. The system processes the textual opinions by applying a sentiment polarity extractor based on the fine-tuned auto-regressive language model named XLNet. Also, the system proposes a technique to enhance sentiment identification by detecting sarcastic opinions through examining the contrast of sentiment between the textual content, images, and emojis. Furthermore, other features are considered, such as the popularity of the opinions based on the social network interactions (likes and shares), the intensity of the entity’s demand within the opinions, and news influence on the entity. A survey experiment has been conducted by gathering numerical scores from 827 Twitter users interested in cryptocurrencies. Each selected user assigns 3 numerical assessment scores toward three cryptocurrencies. The average of those scores is considered ground truth. The experiment results show the efficacy of our model in generating a reliable numerical reputation value compared with the ground truth, which proves that the proposed system may be applied in practice as a trusted decision-making tool.

Blockchain-Based Secure Energy Trading With Mutual Verifiable Fairness in a Smart Community

This article proposes an energy trading model basedon blockchain to manage and supervise the trading process. In the model, proof-of-energy reputation generation and proof-of-energy reputation consumption consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by the existing consensus mechanisms. Similarly, a mutual verifiable fairness mechanism based on time commitment is presented, which is introduced to prevent cheating attacks in the model. The proposed model’s performance is assessed using energy cost, peak-to-average ratio, and trust. The simulation results show that the energy cost of the proposed model decreases by 40%. The results for the load balancing depict that the values of peak-to-average ratio of the proposed model with 20% and 50% peak demand reduction are 6.88 and 3.50, which are lower than 9.17 of the benchmark model. Moreover, the proposed model’s results show satisfactory performance for privacy and security of the system.

MINING ONLINE REVIEWS TO SUPPORT CUSTOMERS’ DECISION-MAKING PROCESS IN E-COMMERCE PLATFORMS: A NARRATIVE LITERATURE REVIEW

ABSTRACT By dint of the massive daily production of user-generated content (textual reviews) in E-commerce platforms, the need to automatically process it and extract different types of knowledge from it becomes a necessity. In this work, an attempt has been made to summarize some studies that aim to propose systems, which automatically mine textual reviews expressed in natural languages for the purpose of supporting customers’ decision-making process in E-commerce (buying, renting, and booking). The given review is the first work of this type and it includes 44 studies (30 aspect/feature-based summarizers and 14 reputation systems) published from 2004 to 2021. First, it investigates aspect and feature-based summarizers that aim to help customers in making an informed decision toward online entities (products, movies, hotels, services …). Second, it introduces reputation generation systems that seek to provide valuable information about online items. Finally, it provides recommendations for future research directions and open problems.

Cross-Platform Reputation Generation System Based on Aspect-Based Sentiment Analysis

The active growth of Internet-based applications such as social networks and e-commerce websites leads people to generate a tremendous amount of opinions and reviews about products and services. Thus, it becomes very crucial to automatically process them. Over the last ten years, many systems have been proposed to generate and visualize reputation by mining textual and numerical reviews. However, they have neglected the fact that online reviews could be posted by malicious users that intend to affect the reputation of the target product. Besides, these systems provide an overall reputation value toward the entity and disregard generating reputation scores toward each aspect of the product. Therefore, we developed a system that incorporates spam filtering, review popularity, review posting time, and aspect-based sentiment analysis to generate accurate and reliable reputation values. The proposed model computes numerical reputation values for an entity and its aspects based on opinions collected from various platforms. Our proposed system also offers an advanced visualization tool that displays detailed information about its output. Experiment results conducted on multiple datasets collected from various platforms (Twitter, Facebook, Amazon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\dots $ </tex-math></inline-formula> ) show the efficacy of the proposed system compared with state-of-the-art reputation generation systems.

Leveraging Lexicon-Based and Sentiment Analysis Techniques for Online Reputation Generation

Leveraging Lexicon-Based and Sentiment Analysis Techniques for Online Reputation Generation

MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis

Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.

Customized reputation generation of entities using sentiment analysis

PurposeIn this study, the authors have used the customer reviews of books and movies in natural language for the purpose of sentiment analysis and reputation generation on the reviews. Most of the existing work has performed sentiment analysis and reputation generation on the reviews by using single classification models and considered other attributes for reputation generation.Design/methodology/approachThe authors have taken review, helpfulness and rating into consideration. In this paper, the authors have performed sentiment analysis for extracting the probability of the review belonging to a class, which is further used for generating the sentiment score and reputation of the review. The authors have used pre-trained BERT fine-tuned for sentiment analysis on movie and book reviews separately.FindingsIn this study, the authors have also combined the three models (BERT, Naïve Bayes and SVM) for more accurate sentiment classification and reputation generation, which has outperformed the best BERT model in this study. They have achieved the best accuracy of 91.2% for the movie review data set and 89.4% for the book review data set which is better than the existing state-of-art methods. They have used the transfer learning concept in deep learning where you take knowledge gained from one problem and apply it to a similar problem.Originality/valueThe authors have proposed a novel model based on combination of three classification models, which has outperformed the existing state-of-art methods. To the best of the authors’ knowledge, there is no existing model which combines three models for sentiment score calculation and reputation generation for the book review data set.

Aggregating Customer Review Attributes for Online Reputation Generation

In this paper, we face the problem of generating reputation for movies, products, hotels, restaurants and services by mining customer reviews expressed in natural language. To the best of our knowledge, previous studies on reputation generation for online entities have primarily examined semantic and sentiment orientation of customer reviews, disregarding other useful information that could be extracted from reviews, such as review helpfulness and review time. Therefore, we propose a new approach that combines review helpfulness, review time, review attached rating and review sentiment orientation for the purpose of generating a single reputation value toward various entities. The contribution of the paper is threefold. First, we design two equations to compute review helpfulness and review time scores, and we fine-tune Bidirectional Encoder Representations from Transformers (BERT) model to predict the review sentiment orientation probability. Second, we design a formula to assign a numerical score to each review. Then, we propose a new formula to compute reputation value toward the target entity (movie, product, hotel, restaurant, service, etc). Finally, we propose a new form to visualize reputation that depicts numerical reputation value, opinion categories, top positive review and top negative review. Experimental results coming from several real-world data sets of miscellaneous domains collected from IMDb, TripAdvisor and Amazon websites show the effectiveness of the proposed method in generating and visualizing reputation compared to three state-of-the-art reputation systems.

A hybrid approach for generating reputation based on opinions fusion and sentiment analysis

ABSTRACT Amazon, eBay, IMDb as well as several websites provide a convenient platform where users share their opinions on any entities without hindrance. Though those opinions are too many to be examined one by one, this is why a general reputation value will help people make a decision toward a target entity (purchase, download, rent …). This fact makes reputation generation task very challenging because an inaccurate reputation system will directly damage the credibility and popularity of the target entity. This paper aims to improve a recent work that handles the task of generating reputation based on fuzing and mining opinions expressed in natural languages and user feedback ratings. Therefore, we have proposed a hybrid approach that, (i) separates reviews into positive and negative based on their sentiment polarity by applying the two classifiers Naïve Bayes and Linear Support Vector Machine (LSVM), (ii) groups positive and negative reviews into principal opinion sets based on their semantic relations, (iii) calculates a custom reputation value separately for positive and negative groups by considering some statistics of principal opinion sets and finally (iv) computes the final reputation value using Weighted Arithmetic Mean. Experimental results show a significant improvement with respect to recent work.

An Unsupervised Approach for Reputation Generation

Nowadays, watching a movie, buying a product, making hotel reservations and other e-commerce trades are strung to consulting other peoples reviews and recommendations on the target entity. Indeed, Amazon, IMDB (Internet Movie Database) as well as several websites provide a convenient platform where users share freely their opinions and their subjective attitudes towards the target entity with no restrictions. However, those opinions are too much to be examined one by one, this is why a general reputation value makes the task of choosing the right product much easier. In this paper, we propose a reputation generation approach based on opinion clustering and semantic analysis. In our approach, opinions are grouped into a number of clusters that contain opinions with the same attitude or preference. By aggregating the ratings attached to the clusters, we generate the reputation of an entity. Experimental results demonstrate the effectiveness of the proposed approach in generating reputation value.

Fusing and mining opinions for reputation generation

The Internet provides a convenient platform for people to freely share their opinions on any entities. The opinions expressed in natural languages carry the subjective attitudes and preferences of humans. They represent the public perspectives on any entity, thus impact user decisions and behaviors in some way. Therefore, opinions have been recognized as useful and valuable pieces of information for reputation generation. Fusing and mining opinions offer a promising approach to extract reputation information and track public perspectives. However, the literature lacks studies on this topic. In this paper, we propose a novel reputation generation approach based on opinion fusion and mining. In our approach, opinions are filtered to eliminate unrelated ones, and then grouped into a number of fused principal opinion sets that contain opinions with a similar or the same attitude or preference. By aggregating the ratings attached to the fused opinions, we normalize the reputation of an entity. Meanwhile, various types of recommendations can be generated based on relationships among opinions. To offer sufficient reputation information to users, we also propose a new way of reputation visualization. It shows the details of opinion fusing and mining results, such as the normalized reputation value, principal opinions with popularity and other statistics. Experimental results coming from an analysis of big real-world data collected from several popular commercial websites in both English and Chinese demonstrate the generality and accuracy of the proposed approach, especially the effectiveness of opinion filtering for reputation generation. A small-scale real-world user study further quantifies the user acceptance of the developed reputation visualization method. In the sequel, this implies that the proposed approach can be applied in practice to generate reputation.

Corporate personality perspective in corporate reputation management: a survey of corporate personality perception on business administration students

Corporate Reputation concept has been studied in academic literature since 1950’s and received a lot of interest from various disciplines. Both practitioners and academicians agree that corporate reputation plays a critical role on how the corporate is perceived and whether or not the corporate is performing successfully (Beown, 1998, Fombrun, 1996). Having a good reputation makes the stakeholders of the firm more eager to contribute, attracts good talent, helps retain existing customers (Markham, 1972). There is a positive correlation between good reputation and high revenue generation (Robert and Dowling 1997; Vergin and Qoronfleh 1998). Generally accepted definition of corporate reputation is totality of aggregated observations and perceptions regarding the firm (Bromley 1993; Davies and Miles 1998). There are three distinctive schools of thought regarding corporate reputation. These are; social expectations, corporate personality and trust. Social expectations take into account the anticipated corporate behaviors by the society. Corporate personality approach takes into account various personality traits being attributed to a firm. The last approach investigates the conditions where people trust to a firm. This paper uses the corporate personality approach to measure corporate personality concept. The aim of the research is to investigate the corporate personality perception of university students studying in business administration departments who plan to work in one of these firms after graduation. An exploratory research is made to achieve this purpose. The research is limited with the city of Istanbul and with the university students studying business administration in state and private universities. Due to the homogeneity among the research population, convenient sampling is made. The results are compared, analyzed and further research suggestions are made.

Gauging an Integrated Model of Public Relations Value Assessment (PRVA): Scale Development and Cross-Cultural Studies

This article aims to develop an integrated, 2-level, cross-cultural scale for measuring multidimensional public relations value (called PRVA—Public Relations Value Assessment). Three data sets explore the views of a total of 524 public relations professionals from the Public Relations Society of America (PRSA), the International Public Relations Association (IPRA) and a number of Hong Kong corporations and PR associations, which were incorporated to empirically test the proposed framework. The results show that PRVA, which includes measures of media publicity, organization–public relationship (OPR), organizational reputation, cost reduction, and revenue generation, demonstrated satisfactory construct reliability, structural validity, and cross-cultural applicability. The conceptual Two-level Model of Public Relations Value is also empirically supported across three independent samples. Theoretical, practical and cultural implications derived from the findings for research on public relations value and effects are elaborated.

What’s in a name? Decomposing corporate reputation to assess the relative impact of temporal, firm and industry level factors

While the perceptual nature of corporate reputation is rarely contested, the role of governance and firm financial performance does not have the same consensus. As reputation is an embedded capability that cannot be distinctly valued or traded, the ambiguity in reputation generation clouds researchers’ attempts to understand the relative importance of the underlying causal factors, particularly firm-specific attributes like board characteristics, governance and ownership—independent of the firm’s financial performance over time. Utilizing a resource—based view, we develop a theoretically grounded framework that enables us to deconstruct corporate reputation and parse out the impact at multiple levels and the factors therein. We decompose reputation into time, firm and industry level factors, offer hypotheses on the relative importance of the factors at each level, and thereafter we simultaneously assess within and across the temporal, firm and industry levels to quantify the impact of the causal factors. We find that 49.65 % of the variation in corporate reputation is firm-specific, independent of financial performance, while industry-specific variables account for just 5.04 %. The temporal factors including the multi-level interaction terms explain 46.06 % of reputational variation, of which financial performance accounts for only 18.53 % and the “halo effect” of prior financial performance is short-lived. Furthermore, the commonly accepted factors explain only 26.44 % of the total variation in corporate reputation, and some of the governance and ownership indicators contradict generally accepted agency expectations.