A Movie Weekly Box-office Revenues Prediction Model Based on Online Reviews

To predict the movie weekly box-office revenues, this paper proposes a new prediction model based on ensemble machine learning method. Firstly, we extract some important features from movie online reviews. Then, due to the limited ability of the single machine learning model, an ensemble machine XGboost is employed to predict the movie weekly box-office revenues in this paper. Finally, we collect the movie online reviews from Douban.com, and use about 600 movies to verify the performance of the model. The experimental results show that the effectiveness and practicability of this model

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

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

What Is Important When We Evaluate Movies? Insights from Computational Analysis of Online Reviews

The question of what is important when we evaluate movies is crucial for understanding how lay audiences experience and evaluate entertainment products such as films. In line with this, subjective movie evaluation criteria (SMEC) have been conceptualized as mental representations of important attitudes toward specific film features. Based on exploratory and confirmatory factor analyses of self-report data from online surveys, previous research has found and validated eight dimensions. Given the large-scale evaluative information that is available in online users’ comments in movie databases, it seems likely that what online users write about movies may enrich our knowledge about SMEC. As a first fully exploratory attempt, drawing on an open-source dataset including movie reviews from IMDb, we estimated a correlated topic model to explore the underlying topics of those reviews. In 35,136 online movie reviews, the most prevalent topics tapped into three major categories—Hedonism, Actors’ Performance, and Narrative—and indicated what reviewers mostly wrote about. Although a qualitative analysis of the reviews revealed that users mention certain SMEC, results of the topic model covered only two SMEC: Story Innovation and Light-heartedness. Implications for SMEC and entertainment research are discussed

Three Papers on the Role of Information in Online Consumer Reviews

This dissertation is comprised of three papers. The first two papers investigate how the various informational elements of online reviews, including their textual portion, impact the perceived helpfulness of those reviews. The third paper proposes a methodological refinement to improve the process by which reviews and reviewers are ranked with respect to their helpfulness and has potential applicability in fake review detection. These three papers utilize natural language processing methods, including Latent Semantic Analysis (LSA), which allows for the automatic analysis of large amounts of text with minimal human intervention.Ph.D., Business Administration -- Drexel University, 201

The effect of e-WOM on customer satisfaction through ease of use, perceived usefulness and e-wallet payment

Currently, streaming applications have been widely used by users to get comfort and pleasure in life. Users communicate with each other on social media related to the activities carried out. Communication is formed online as electronic word of mouth (e-WOM) between one user to another. The data distributed was 1238 respondents using streaming applications and 324 respondents in Indonesia who had used e-wallet payments as members. The analysis data was to answer all research hypotheses using partial least squares. The data processing results show that e-WOM impacts the perceived ease of use of e-wallets by 0.408. E-WOM positively impacts the perceived usefulness of the e wallet by 0.270. E-WOM has an impact on e-wallet payment intention of 0.190. Perceived ease of use has an effect of 0.175 and perceived usefulness of 0.259 on e-wallet payment intention. Perceived ease of use influences perceived usefulness of 0.395. Perceived ease of use and perceived usefulness impact customer satisfaction in terms of 0.157 and 0.217. Finally, it was found that e-wallet payment intention has an impact of 0.173 on customer satisfaction. The results of this study contribute to ewallet payment users and managers building two-way and effective communication through social media so that they can quickly and accurately solve user problems. The theoretical contribution is to enrich the theory of marketing behavior and technology acceptance models in electronic commerce

The effects of online reviews and promotional messages on product performance : review helpfulness and the power of language

This dissertation contains two essays. For Essay One, previous studies on review helpfulness focus on what makes a review helpful and how to predict review helpfulness. In so doing, researchers hope to identify the most helpful reviews for consumers and improve the recommendation system. However, little is known about the effect of these helpful reviews on product performance. Thus, this paper investigates how helpful reviews or the helpfulness votes influence product sales. Since product sales are only available at the group (product) level, estimating the effect of helpfulness votes presents a challenging multilevel problem. This research considers both the disaggregating (individual) and the aggregating (group) approaches and compares four competing models in their parameter estimates and model fitness. The results suggest that the average number of votes performs the worst while the mean-adjusted model slightly improves predictive power. Among them, the total number of helpfulness votes renders the best predictive performance. For Essay Two, crowdfunding has become a trendy way to raise funding nowadays. Budding entrepreneurs try to make a convincing pitch to attract potential backers\u27 interest. Existing studies have found that linguistic styles such as the narrative tone, the use of emotional or informational arguments, concreteness, precision, and interactivity are signals of underlying project quality. Nevertheless, this body of research lacks proof of the effect of micro-level linguistic elements on the success of crowdfunding. In this essay, we conduct two studies to investigate the effect of word-level and topic-level linguistic characteristics on crowdfunding outcomes. In Study One, we adopt a multimethod approach which includes N-gram natural language processing model, penalized logistic regression (PLR), and linguistic analysis to analyze the narratives of projects on Kickstarter. We find that speaking the same language and careful choice of words is critical to the success of crowdfunding. Further, the psychological meanings of the words and phrases associated with the success and failure of crowdfunding Our findings will provide a valuable insight for entrepreneurs to prepare their pitches. In Study Two, we switch our focus from the choice of word to the choice of topic. We use topic entropy to measure the theme complexity for each project pitch and examine how it would affect the probability of crowdfunding success. We find a significant prediction power of the topic entropy, with the lower (higher) the value, the more probability the success (failure) of the project. Among successful projects, certain words and topics that have more positive or negative impacts vary depending on the movie genre. This essay is one of the first in marketing research to use advanced text analysis to evaluate the effect of micro-level linguistic features on message persuasiveness. In addition, this work has further proved the power of language in effective marketing communications

Three Essays on the Role of Unstructured Data in Marketing Research

This thesis studies the use of firm and user-generated unstructured data (e.g., text and videos) for improving market research combining advances in text, audio and video processing with traditional economic modeling. The first chapter is joint work with K. Sudhir and Minkyung Kim. It addresses two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, we develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, we address the problem of missing attributes in text in constructing attribute sentiment scores---as reviewers write only about a subset of attributes and remain silent on others. We develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, we show superior accuracy in converting text to numerical attribute sentiment scores with our model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings. The second essay, which is joint work with Aniko Oery and Joyee Deb is an information-theoretic model to study what causes selection in valence in user-generated reviews. The propensity of consumers to engage in word-of-mouth (WOM) differs after good versus bad experiences, which can result in positive or negative selection of user-generated reviews. We show how the strength of brand image (dispersion of consumer beliefs about quality) and the informativeness of good and bad experiences impacts selection of WOM in equilibrium. WOM is costly: Early adopters talk only if they can affect the receiver’s purchase. If the brand image is strong (consumer beliefs are homogeneous), only negative WOM can arise. With a weak brand image or heterogeneous beliefs, positive WOM can occur if positive experiences are sufficiently informative. Using data from Yelp.com, we show how strong brands (chain restaurants) systematically receive lower evaluations controlling for several restaurant and reviewer characteristics. The third essay which is joint work with K.Sudhir and Khai Chiong studies success factors of persuasive sales pitches from a multi-modal video dataset of buyer-seller interactions. A successful sales pitch is an outcome of both the content of the message as well as style of delivery. Moreover, unlike one-way interactions like speeches, sales pitches are a two-way process and hence interactivity as well as matching the wavelength of the buyer are also critical to the success of the pitch. We extract four groups of features: content-related, style-related, interactivity and similarity in order to build a predictive model of sales pitch effectiveness