9,621 research outputs found

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

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    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

    Exploring the Mechanisms of Information Sharing

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    abstract: Online product ratings offer consumers information about products. In this dissertation, I explore how the design of the rating system impacts consumers’ sharing behavior and how different players are affected by rating mechanisms. The first two chapters investigate how consumers choose to share their experiences of different attributes, how their preferences are reflected in numerical ratings and textual reviews, whether and how multi-dimensional rating systems affect consumer satisfaction through product ratings, and whether and how multi-dimensional rating systems affect the interplay between numerical ratings and textual reviews. The identification strategy of the observational study hinges on a natural experiment on TripAdvisor when the website reengineered its rating system from single-dimensional to multi-dimensional in January 2009. Rating data on the same set of restaurants from Yelp, were used to identify the causal effect using a difference-in-difference approach. Text mining skills were deployed to identify potential topics from textual reviews when consumers didn’t provide dimensional ratings in both SD and MD systems. Results show that ratings in a single-dimensional rating system have a downward trend and a higher dispersion, whereas ratings in a multi-dimensional rating system are significantly higher and convergent. Textual reviews in MDR are in greater width and depth than textual reviews in SDR. The third chapter tries to uncover how the introduction of monetary incentives would influence different players in the online e-commerce market in the short term and in the long run. These three studies together contribute to the understanding of rating system/mechanism designs and different players in the online market.Dissertation/ThesisDoctoral Dissertation Business Administration 201

    Sentiment analysis of ASOS product reviews using machine learning algorithms by comparing several models.

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    Digital ratings are crucial in improving international customer communications and impacting consumer purchasing trends. To obtain important data from a massive number of customer reviews, they must be sorted into positive and negative opinions. Sentiment analysis is a computational method for extracting emotive information from a text. In this particular research, over 3000 reviews have been obtained from the ASOS website and classified into three different sentiments: excellent, average, and bad. The obtained reviews have been pre-processed, then feature extraction is applied to the pre-processed data to remove the redundant data. Finally, distinct machine learning algorithms will be utilized to build disparate models. This research is vital as it allows the ASOS organization to gain insight into how consumers perceive about specific issues and detect urgent issues such as delivery delays and misplaced packages in the current time period before the issue goes outof control. The key results of this research show that the Nu-Support Vector Classification model obtained the highest accuracy score of 85.99% and the lowest accuracy score of 51.47% was obtained for the AdaBoost classifier model

    Big data and Sentiment Analysis considering reviews from e-commerce platforms to predict consumer behavior

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    Treballs Finals del Màster de Recerca en Empresa, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2019-2020, Tutor: Javier Manuel Romaní Fernández ; Jaime Gil LafuenteNowadays and since the last two decades, digital data is generated on a massive scale, this phenomenon is known as Big Data (BD). This phenomenon supposes a change in the way of managing and drawing conclusions from data. Moreover, techniques and methods used in artificial intelligence shape new ways of analysis considering BD. Sentiment Analysis (SA) or Opinion Mining (OM) is a topic widely studied for the last few years due to its potential in extracting value from data. However, it is a topic that has been more explored in the fields of engineering or linguistics and not so much in business and marketing fields. For this reason, the aim of this study is to provide a reachable guide that includes the main BD concepts and technologies to those who do not come from a technical field such as Marketing directors. This essay is articulated in two parts. Firstly, it is described the BD ecosystem and the technologies involved. Secondly, it is conducted a systematic literature review in which articles related with the field of SA are analysed. The contribution of this study is a summarization and a brief description of the main technologies behind BD, as well as the techniques and procedures currently involved in SA

    Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

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    The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work. Available at: https://aisel.aisnet.org/pajais/vol9/iss2/4

    Automatic Summarization of Customer Reviews: An Integrated Approach

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    The proliferation of interactivity between Web content producers and consumers underscores the development of the Internet in recent years. In particular, customer reviews posted on the Web have grown significantly. Because customers represent the primary stakeholder group of a company, understanding customers’ concerns expressed in these reviews could help marketers and business analysts to identify market trends and to provide better products and services. However, the large volume of textual reviews written in informal language makes it difficult to understand customers’ concerns. This paper describes an integrated approach to summarizing customer reviews. The approach consists of the steps of sentence extraction, aspect identification, sentiment classification, and review summarization. We report preliminary results of using our approach to summarize product reviews extracted from Amazon.com. Our work augments existing work by considering nonstandard input and by incorporating linguistic resources and clustering in automatic summarization

    Management Responses to Online Reviews: Big Data From Social Media Platforms

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    User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competitive advantage and improve customer relationship management by leveraging big data, social media analytics, and deep learning techniques. Since negative reviews' harmful effects are greater than positive comments' contribution, firms must strategise their responses to intervene in and minimise those damages. Although current literature includes a sheer amount of research that presents effective response strategies to negative reviews, they mostly overlook an extensive classification of response strategies. The first study consists of two phases and focuses on comprehensive response strategies to only negative reviews. The first phase is explorative and presents a correlation analysis between response strategies and overall ratings of hotels. It also reveals the differences in those strategies based on hotel class, average customer rating, and region. The second phase investigates effective response strategies for increasing the subsequent ratings of returning customers using logistic regression analysis. It presents that responses involving statements of admittance of mistake(s), specific action, and direct contact requests help increase following ratings of previously dissatisfied returning customers. In addition, personalising the response for better customer relationship management is particularly difficult due to the significant variability of textual reviews with various topics. The second study examines the impact of personalised management responses to positive and negative reviews on rating growth, integrating a novel method of multi-topic matching approach with a panel data analysis. It demonstrates that (a) personalised responses improve future ratings of hotels; (b) the effect of personalised responses is stronger for luxury hotels in increasing future ratings. Lastly, practical insights are provided

    Investigating the Effects of Dimension-Specific Sentiments on Product Sales: The Perspective of Sentiment Preferences

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    While the literature has reached a consensus on the awareness effect of online word-of-mouth (eWOM), this paper studies its persuasive effect—specifically, dimension-specific sentiment effects on product sales.We examine the sentiment information in eWOM along different product dimensions and reveal different persuasive effects on consumers’ purchase decisions based on consumers’ sentiment preference, which is defined as the relative importance that consumers place on various dimension-specific sentiments. We use an aspect-level sentiment analysis to derive dimension-specific sentiment and PVAR (panel vector auto-regression) models, and estimate their effects on product sales using a movie panel dataset. The findings show that three dimension-specific sentiments (star, genre, and plot) are positively related to movie sales.Regarding consumers’ sentiment preferences, we find a positive relationship to movie sales that is stronger for plot sentiment, relative to star sentiment for low-budget movies. For high-budget movies, we find a positive relationship to movie sales that is stronger for star sentiment, relative to plot or genre sentiment
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