Online Movie Reviews Research Articles

Pragma-linguistic and stylistic features of the English online movie review

The paper deals with linguo-pragmatic and semantic-stylistic characteristics of the English-online movie review. Internet communication provides a fertile ground for the development of the modern genre studies due to the emergence and development of digital genres, or Internet genres. The amateur online movie review is one of the most widespread Internet genres due to its high degree of the audience-oriented intention, focus on the wide range of readers, as well as the dialogical nature of communication. The aim of this paper is to reveal and systematize pragmatic and stylistic parameters of the English amateur online movie review. The empirical study is based upon the movie reviews taken from the IMDb website. The analysis of online movie reviews has revealed that their pragmatic function is determined by the use of the wide range of linguistic and stylistic evaluative means. In most cases the ascription of explicit evaluative characteristics to the film and its components in the text of the amateur online movie review is done with the help of the evaluative language. The important finding is that the creation of perlocutionary effect and the attribution of dialogization and intimization to the speech act are created by the use of personal pronouns of the first and second person singular, imperatives, appeals, question-and-answer structure. We have come to the conclusion that various lexical and syntactic means of expressiveness belong to stylistic markers of online movie review carrying evaluative, emotional and emphatic connotations. All the analysis conducted allows us to conclude that online movie review beside the initial function of informing also performs perlocutionary force.

Corrigendum to “Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics”

Corrigendum to “Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics”

Automatic Intelligent Movie Sentiment Analysis Model Creation for Box Office Prediction using Multiview Light Semi Supervised Convolution Neural Network

With the rapid growth of e-commerce, online product and service monitoring is becoming more and more established as an important source of information for both sellers and customers. Emotional surveys and comments for online review analysis are gaining more and more attention as such studies help use information from online reviews for potential economic impacts. Twitter is a widely used social networking site and a trusted source of public opinion. The success of the film can be predicted by analyzing the tweets and researching the impact of the film. This report discusses the application of emotional analysis and in-depth machine learning methods to understand the relationship between online movie reviews, and this story is used to generate revenue at the movie box office. In this paper, this work present a Intelligent Extensive Information Rich Transfer Network (IEIRTN). It is modeled with information from sentences (i.e., reviews) and aspects simultaneously. First, IEIRTN extract all aspects of the sentence. After obtaining the aspects, it utilize all data in the source domain and the target domain for training Multiview Light Semi Supervised Convolution Neural Network (MLSSCNN) classifier. To understand the predictive performance of this approach several performance metrics are used. The experimental result shows that the MLSSCNN offers a superior predictive effect than other classifier.

Do Spoilers Really Spoil? Using Topic Modeling to Measure the Effect of Spoiler Reviews on Box Office Revenue

A sizable portion of online movie reviews contain spoilers, defined as information that prematurely resolves plot uncertainty. In this research, the authors study the consequences of spoiler reviews using data on box office revenue and online word of mouth for movies released in the United States. To capture the degree of information in spoiler review text that reduces plot uncertainty, the authors propose a spoiler intensity metric and measure it using a correlated topic model. Using a dynamic panel model with movie fixed effects and instrumental variables, the authors find a significant and positive relationship between spoiler intensity and box office revenue with an elasticity of .06. The positive effect of spoiler intensity is greater for movies with a limited release, smaller advertising spending, and moderate user ratings, and is stronger in the earlier days after the movie’s release. Using an event study and online experiments, the authors provide further evidence that spoiler reviews can help consumers reduce their uncertainty about the quality of movies, consequently encouraging theater visits. Thus, movie studios may benefit from consumers’ access to plot-intense reviews and should actively monitor the content of spoiler reviews to better forecast box office performance.

Do Spoilers Really Spoil? Using Topic Modeling to Measure the Effect of Spoiler Reviews on Box Office Revenue:

A sizable portion of online movie reviews contain spoilers, defined as information that prematurely resolves plot uncertainty. In this research, the authors study the consequences of spoiler review...

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.

The moderating effect of movie production efficiency on the relationship between eWOM and review helpfulness

Purpose This paper aims to intend to study the effect of movie production efficiency on eWOM and the moderating effect of efficiency on the relationship between eWOM and review helpfulness for movies. Design/methodology/approach Production efficiency is suggested by comparing the power of movie resources (e.g. the power of actors, directors, distributors, production companies) against box-office revenue through a data envelopment analysis (DEA). Findings The study results present that the number of reviews, the number of reviews by reviewers and review extremity are greater in an efficient subsample than in an inefficient subsample. For efficient movies, the review depth and the strength of the sentiments in the reviews are more positively related to review helpfulness. The prediction results for review helpfulness using the k-nearest neighbor method and automatic neural networks show that the efficient subsample provides a significantly lower prediction error rate than the inefficient subsample. The study results can support the effective facilitation of helpful online movie reviews. Originality/value As the numbers of online reviews are increasingly used to provide purchase decision support, it becomes crucial to understand which attributes represent average helpful reviews for movies. While previous studies have examined eWOM (online word-of-mouth) variables as predictors of helpfulness on movie websites, the role of the production efficiency of movies has not been examined considering the relationship between eWOM and review helpfulness for movies.

Investigating the effects of textual reviews from consumers and critics on movie sales

PurposeThe purpose of this paper is to investigate the sales impact of different types of online word-of-mouth based on their source (user vs critic) and form (structured vs unstructured).Design/methodology/approachThe paper proposed a model by adopting the heuristic-systematic perspective of information processing and tested it using online movie reviews collected from Rotten Tomatoes. A unique dataset was constructed, which matched critic reviews and user reviews with metadata such as box-office sales and advertisement spending for 90 movies. Sentiment information from the textual contents of both user and critic reviews were text-mined and extracted. Data analyses were used to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings and critic text reviews.FindingsCritic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives.Practical implicationsThis study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to maximize the economic value of online user-generated information.Originality/valueThe major contribution of this study is to extend the current understanding of the sales impact of online reviews to their textual aspect, as well as investigate how these textual narratives play different roles when offered by critics and users.

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Information is exploding on the web at exponential pace, and online movie review over the web is a substantial source of information for online users. However, users write millions of movie reviews on regular basis, and it is not possible for users to condense the reviews. Classification and summarization of reviews is a difficult task in computational linguistics. Hence, an automatic method is demanded to summarize the vast amount of movie reviews, and this method will permit the users to speedily distinguish between positive and negative features of a movie. This work has proposed a classification and summarization method for movie reviews. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents and represent the review documents as a vector. Next, the Na¨ıve Bayes algorithm is employed to categorize the movie reviews (signified as a feature vector) into negative and positive reviews. For the task of movie review summarization, word2vec model is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are employed to compute the salience score of all review sentences in clusters. Finally, the best-ranked review sentences are picked based on top salience scores to form a summary of movie reviews. Empirical results indicate that the suggested machine learning approach performed better than benchmark summarization approaches.

텍스트 내용과 언어 스타일이 온라인 영화 리뷰 유용성에 미치는

텍스트 내용과 언어 스타일이 온라인 영화 리뷰 유용성에 미치는

Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm.

With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.

Sentiment Cognition From Words Shortlisted by Fuzzy Entropy

Sentiment analysis (SA) is the process of evaluating human emotions, opinions, and reviews expressed in text to detect the writer's mental outlook toward a particular event, topic, product, service, etc., and assign a relevant sentiment. In SA, to highlight the correct words which contribute toward sentiment cognition is very difficult. Simulating this task of shortlisting of words by human observers is challenging due to complexity of human mind's processing. The use of fuzzy entropy is proposed in this article as an innovative step to tap sentiment quotients of online movie reviews. We have proposed a novel approach of shortlisting of words that help in sentiment cognition using a combination of fuzzy entropy, k -means clustering, and sentiment lexicon SentiWordNet. We have addressed this challenging task of simulating the human cognition of words by developing a model that recognizes sentiment based on fuzzy scores derived from SentiWordNet in an automatic manner. Experiments on two benchmark movie review data sets-IMDB and polarity data sets by Pang and Lee-with training by long short-term memory neural networks, yields high accuracy for our approach as compared to other state-of-the-art-methods of SA.

A Novel Deep Learning Based Sentiment Analysis of Movie using Hybrid CNN_SVM Algorithm

Data flow in web is becoming high and vast, extracting useful and meaningful information from the same is especially significant. The extracted information can be utilized for enhanced decision making. The information provided by the end-users is normally in the form of comments with respect to different products and services. Sentiment analysis is effectively carried out in these kinds of compact review to give away the people’s opinion of any products. This analyzed data will be efficient to improve the business strategy. In our work the collected online movie reviews are analyzed by using machine learning sentiment classification models like Random Forest, Naive Bayes, KNN and SVM. The work has been extended with CNN and hybrid CNN-SVM deep learning models to achieve higher performance. Comparing the workings of all the above classification models for sentiment analysis based upon various performance metrics is the main objective of the paper.

Movie Reviews Classification Using Sentiment Analysis

Background/objectives: Movie reviews have become an essential factor when it comes to describing the success or failure of a movie. Nowaday’s movie reviews influence people’s choice whether to watch a movie or not. Methods/findings: We have studied the various algorithms of machine learning for classifying the movie review comments by semantic means. People have become very specific with their choices, and they would prefer not to squander their time and cash on a motion picture having terrible reviews. Improvements/applications: This study provides a comparison between various machine learning algorithms which can be used for classifying the online movie reviews by semantic meaning. The principle target of the study is to find the best algorithm based on its efficiency and accuracy.Keywords: Movie Reviews, Sentiment Analysis, NLP, Machine Learning

Investigating the Effects of Textual Reviews From Consumers and Critics on Movie Sales

In this study, we investigate the sales impact of different types of online word-of-mouth (WOM) based on their source (user versus critic) and form (structured versus unstructured). We propose a model by adopting the heuristic-systematic perspective and empirically test it using online movie reviews collected from Rotten Tomatoes, a review-aggregation website for film and television. We construct a unique dataset that matches critic reviews and user reviews, with metadata such as weekly box-office sales and weekly advertisement spending for 90 movies released in the U.S in 2014. In addition, we use a text-mining method to extract valuable sentiment information from the textual contents of both user and critic reviews. This dataset allows us to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings, and critic text reviews. The results reveal that critic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives. Our study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to lift sales.

Improving sentiment analysis in Arabic: A combined approach

Sentiment analysis SA is concerned with determining users’ opinions from text reviews. However, mining these opinions from massive amounts of unstructured and lengthy documents is a challenging task. In this paper, we propose methods that extract valuable opinions and values from online movie reviews to improve SA in Arabic. First, we propose a method that explores the role of n-gram and skip-n-gram models in opinion classification. Second, we study a method that exploits subjective words such as adjectives and nouns by applying Part-Of Speech tagging. Both of the methods are combined with a feature reduction technique to enhance SA results. Third, we present a method that seeks to extract relevant opinions such as review summaries and conclusion opinions. Then, a combined approach is proposed to augment opinion classification results. Forth, we introduce a method for analyzing customers’ opinions by determining factors impacting their attitudes based on the costumer value model. Experimental results conducted on two datasets prove that our proposed methods are effective and provide better scores than baseline sentiment classifiers. The best obtained classification results reached 96% in F-Measure. These results indicate also that the aesthetic factor is the most influent factor in Arabic movie reviews.

The impact of online movie word-of-mouth on consumer choice

PurposeElectronic word-of-mouth (eWOM) is very important for consumer decision making; previous international product diffusion studies have investigated eWOM and cultural factors that influence consumers’ acceptance of new products, but they have not adequately compared the differences in these factors between the USA and China. Therefore, the purpose of this paper is to compare the impact of eWOM on consumer choices in China and the USA. The authors addressed the following questions: What are the cross-cultural differences in consumers’ eWOM behavior between the USA and China: Which genres of Hollywood movies have better cross-culture predictability in terms of box office performance; and What factors affect the success of Hollywood movies in entering the Chinese market?Design/methodology/approachReal eWOM data were collected from two online movie review websites, IMDb.com (the USA) and Douban.com (China), from January 2010 to December 2015. In addition, box office revenue information was collected from BoxOfficeMojo.com. The authors used an independent sample t-test to check whether the differences in consumers’ eWOM behavior between China and the USA and different types of movie lead to cultural discount differences. Furthermore, a log-linear regression model is used to examine which factors influence the commercial success of new movies.FindingsThere are specific similarities and differences between the American and Chinese movie markets. First, the results show that American consumers are more engaged in online review systems and tend to submit extreme reviews, but Chinese consumers tend to submit moderate reviews on movies, and the eWOM variance there is smaller than in the USA. Second, genres are useful variables as indicators of movie content; the genres of comedy and drama are not popular in the Chinese market. Finally, eWOM variance has a positive impact on box office in China, but eWOM variance has no impact on the US box office. In addition, the interactive effect of the average rating and eWOM variance on sales is positively significant in China. Importantly, the one-star reviews have a negative impact on the Chinese box office, but it has no impact on US box office.Practical implicationsUnderstanding how cultural factors influence consumer eWOM communication will help managers to better apply this new marketing communication tool to create more aggressive and targeted promotional plans. Marketers may use eWOM behavior to better respond to and target consumers to overcome barriers to the selection of their products by consumers. Therefore, more effective management of eWOM can improve the acceptance of and preference for products in different cultural consumer groups.Originality/valueThis study expands the existing body of knowledge on eWOM and international marketing literature. Clearly, culture is an important determinant of eWOM’s impact on sales. In addition, it provides strategic direction and practical implications for eWOM communication management in cross-cultural settings.

Characteristics of Chinese Online Movie Reviews and Opinion Leadership Identification

ABSTRACTThis article uses a case study of the reception of popular South Korean films in China to investigate aspects of proactive participation in the Chinese social mediasphere. Based on the big data collected from the popular Douban online review platform, the authors provide an in-depth investigation of the characteristics of online opinion leaders and leadership. First, we statistically analyze user-generated content-related features for both leader and non-leader groups. In addition, we employ a Bayesian optimization-based imbalanced learning algorithm to identify characteristics of the minority opinion leaders. The efficiency of the proposed algorithm is evaluated based on 17,786 reviews collected from 35 popular films covering a variety of genres, and the proposed algorithm is shown to perform accurately by identifying opinion leaders, compared to other state-of-the-art approaches. Our study also reveals some major features associated with opinion leaders, such as vocabulary, viewing habits, and friendship networks. Finally, we show how these elements are contributing to new understandings of the rapidly transforming digital domain of Chinese social media and the roles of such platforms.

Taste Transitivity for Collaborative Filtering: A Stochastic Network Dynamics Approach

ABSTRACTWe develop a stochastic actor‐based network model for online movie reviews and analyze social network dynamics to study the user‐movie taste networks constructed from real movie review datasets. Examining such taste networks provides useful insights into factors underlying the performance of collaborative filtering‐based recommenders. Our results show that similar taste transitivity effect does exist that support collaborative filtering methods to recommend movies based on user taste similarity. We also investigate the role of movie popularity, genre, user gender, age, and geographic location in the taste network evolution. The findings provide insights to improve current movie recommendation systems. The two‐ mode network analyses approach taken in this article will also be broadly useful in obtaining better understanding of factors that drive user appreciations for varied products.

Term Weighting Scheme Effect in Sentiment Analysis of Online Movie Reviews

Sentiment analysis is an evolving field of a study that deals directly with the online expressions posted by the user via the Internet with the main objective to automate the process of mining opinions into valuable information. For online reviews, this analysis deals with the identification of positive and negative reviews to help the consumer and the distributor in the decision-making process. In text analysis tasks, such as text classification and sentiment analysis, the appropriate choice of term weighting schemes will have a huge impact on the effectiveness of the analysis. This paper explores the effect of using term weighting scheme in the sentiment classification of online movie reviews. Specifically, the researchers applied Support Vector Machine (SVM) with linear and non-linear kernels to perform the classification process. The main finding of this study was that LinearSVC when used with TF-IDF improved the classification performance by as much as 87%. Thus, LinearSVC, together with TF-IDF, can serve as an effective technique in the extraction process of online documents.