Proceedings Article•
Blogs as Predictors of Movie Success
20 Mar 2009-
TL;DR: Analysis of a comprehensive set of features extracted from blogs for prediction of movie sales is presented, using correlation, clustering and time-series analysis to study which features are best predictors.
Abstract: Analysis of a comprehensive set of features extracted from blogs for prediction of movie sales is presented. We use correlation, clustering and time-series analysis to study which features are best predictors.
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Book•
01 May 2012TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Abstract: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.
4,515 citations
Book•
01 Jun 2015TL;DR: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes as discussed by the authors, which offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis.
Abstract: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.
587 citations
01 Sep 2016
TL;DR: This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.
531 citations
TL;DR: The extent to which studios could have improved their limited release strategies by identifying the overlap between the actual release markets and the most responsive ones is characterized, implying considerable room for improvement if these were the only metrics to assess those strategies.
Abstract: We measure the effects of pre- and post-release blog volume, blog valence and advertising on the performance of 75 movies in 208 geographic markets of the US We attribute the variation in blog effects across markets to differences in demographic characteristics of markets combined with differences across demographic groups in their access and exposure to blogs as well as their responsiveness conditional on access We study the effects of pre-release factors on opening day box-office performance and of pre- and post-release factors on box-office performance one month after release Our estimation accounts for confounding factors in the measurement of these effects via the use of instrumental variables We find considerable heterogeneity in the effects across consumer and firm generated media and across geographic markets, with gender, income, race and age driving across-market differences Release day performance is impacted most by pre-release blog volume and advertising, whereas post-release performance is influenced by post-release blog valence and advertising Across markets, there is more variance in advertising and blog valence (post-release) elasticities than there is in blog volume (pre-release) elasticities We identify the top 20 markets in terms of their elasticities to each of these 3 instruments Further, we classify markets in terms of their sensitivities across these 3 instruments to identify the most sensitive markets that studios can target with their limited release strategies Finally, we characterize the extent to which studios could have improved their limited release strategies by identifying the overlap between the actual release markets and the most responsive ones At the time of first release, we find that studios cover only 53% of the most responsive advertising markets and 44% of the most responsive markets to pre-release blog volume in their limited release strategies, implying considerable room for improvement if these were the only metrics to assess those strategies
125 citations
Cites background from "Blogs as Predictors of Movie Succes..."
...…WOM (Godes and Mayzlin 2004, Chevalier and Mayzlin 2006, Liu 2006, Mishne and Glance 2006, Liu et al. 2007, Dhar and Chang 2009, Trusov et al. 2009, Sadikov et al. 2009, Chintagunta et al. 2010, Chen et al. 2011, Moe and Trusov 2011, Dewan and Ramaprasad 2012, Onishi and Manchanda 2012, Stephen…...
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TL;DR: This study combines methods from cloud computing, machine learning, and text mining to illustrate how online platform content, such as Twitter, can be effectively used for forecasting, and finds that the information content of Tweets and their timeliness significantly improve forecasting accuracy.
Abstract: Accurate forecasting of sales/consumption is particularly important for marketing because this information can be used to adjust marketing budget allocations and overall marketing strategies. Recen...
124 citations
References
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21 Jul 2004
TL;DR: This paper proposed a machine learning method that applies text-categorization techniques to just the subjective portions of the document, extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.
Abstract: Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.
3,459 citations
21 Aug 2005
TL;DR: First, carefully hand-crafted queries produce matching postings whose volume predicts sales ranks, and even though sales rank motion might be difficult to predict in general, algorithmic predictors can use online postings to successfully predict spikes in sales rank.
Abstract: An increasing fraction of the global discourse is migrating online in the form of blogs, bulletin boards, web pages, wikis, editorials, and a dizzying array of new collaborative technologies. The migration has now proceeded to the point that topics reflecting certain individual products are sufficiently popular to allow targeted online tracking of the ebb and flow of chatter around these topics. Based on an analysis of around half a million sales rank values for 2,340 books over a period of four months, and correlating postings in blogs, media, and web pages, we are able to draw several interesting conclusions.First, carefully hand-crafted queries produce matching postings whose volume predicts sales ranks. Second, these queries can be automatically generated in many cases. And third, even though sales rank motion might be difficult to predict in general, algorithmic predictors can use online postings to successfully predict spikes in sales rank.
432 citations
Proceedings Article•
01 Jan 2006TL;DR: The main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release.
Abstract: The volume of discussion about a product in weblogs has recently been shown to correlate with the product’s financial performance. In this paper, we study whether applying sentiment analysis methods to weblog data results in better correlation than volume only, in the domain of movies. Our main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release. If my film makes one more person miserable, I’ve done my job.
354 citations