Mining the reviews of movie trailers on YouTube and comments on Yahoo Movies

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1 Mining the reviews of movie trailers on YouTube and comments on Yahoo Movies Li-Chen Cheng* Chi Lun Huang Department of Computer Science and Information Management, Soochow University, Taipei, Taiwan, ROC Abstract Online reviewing is a useful and important information resource for individuals and companies. Recently several studies have focused on analyzing the reviews on Yahoo Movies where users post their comments after seeing the movies. To the best of our knowledge, there has as yet been no systematic analysis of the reviews of movie trailers on YouTube for the purpose of understanding what the consumers feelings are. To address this challenge, we construct a framework for the summarizing and evaluating the reviews from different social media websites. Experimental evaluation shows the proposed approach has greater potential for the industry. Keywords: opinion mining; text mining; sentiment detection T 1. INTRODUCTION he rapid growth of user-generated content on the internet has helped to make online reviewing an ever more useful and important information resource for both individuals and companies. There are several well-known web sites, such as Amazon.com and Yahoo Movies, which encourage people to post reviews detailing their experience with a product or their feelings and opinions about the movies they have watched. Recently, there have been several systematic studies of sentiment analysis and opinion mining from online review postings [1, 2]. Online product reviews have the potential to be a valuable tool for firms and manufacturers who can use them to gather feedback from their customers to further improve their products and adapt their marketing strategies [3]. Naturally, positive opinions will encourage potential consumers to adopt a product whereas negative opinions will discourage them [12]. The summarizing of customer reviews can help people to objectively evaluate their purchase decisions [4, 5, 6]. Opinion mining and summarization strategies have thus attracted increasing research attention. In most studies the focus has been on collecting the consumer s feedback after watching movies or using the products. Several commonly used web sites, such as This research was supported in part by the Ministry of Science and Technology Taiwan (Republic of China) under grant number NSC H MY3. Li-Chen Cheng is associate professor at Department of Computer Science and Information Management, Soochow University, Taipei, Taiwan, ROC (corresponding author to lijen.cheng@gmail.com).. Amazon.com and Yahoo! Movies have designed functions to allow users to vote and rank products or movies and to filter out unhelpful reviews for readers. People tend to talk more about movies immediately after watching them and less as time goes by. Such information gathering mechanisms only work after the consumer watches a movie and then provides their feedback. Liu (2006) proved that the explanatory power of such information is somewhat dependent on the volume of online reviews. Previous studies have also found that the amount of prerelease buzz can be used as a proxy for early sales [3]. Word of mouth (WoM) also has a strong influence on people s movie selection. This study proposes a framework that will help interested firms to monitor consumer attitudes based on the analysis of reviews collected after a movie s release. The movie makers and distributors can adapt their marketing strategies and distribution tactics based on this information. Recently, one of the most popular web sites, YouTube, has begun to provide social tools for community interaction, including the possibility of commenting on published videos and rating the comments made by other users [7]. User feedback is collected, for example from those who have watched movie trailers on YouTube which is of interest to many organizations [8]. Previous studies have also proven that online blog postings can successfully predict the ranking of book sales [9]. Gathering information on how people perceive newly released products can be helpful in the design of marketing and advertising campaigns. Movie producers spend a lot of effort and money publicizing their movies through different mediums. This study focuses on the influence of WoM and the effect of pre-release opinions. To the best of our knowledge, there has been no study analyzing the comments on movie trailers that appear on YouTube and observing the users behavior. This study aims to fill this gap through analysis of a large sample of text comments on Movie Trailers from YouTube. 2. RELATED WORK The basic idea behind opinion mining is that it can be used to identify product features and to determine trends in public opinion. There are three steps in this process: (1) extracting customer comments and opinions about product features [1,2]; (2) identifying the opinion sentence in each review and deciding whether each opinion sentence is positive or negative [3,4]; (3) summarizing the results [10]. Some

2 approaches utilize linguistic methods to discover the semantic orientations of words and sentences, in order to classify the sentiment. Other approaches extract the explicit product features based on a priori algorithm. In 2004, Hu [11] carried out a pioneering work on feature-based opinion summarization. Turney (2002) and Pang (2002) applied different methods for detecting the polarity in product reviews and movie reviews respectively. 3. THE PROPOSED FRAMEWORK This study proposes a model for the analysis of movie comments; the architecture is diagramed in Fig.1. As demonstrated below, the comments on movie trailers from YouTube and the comments from Yahoo! Movie are gathered and then enter the pre-processing module. The data preprocessing phase is comprised of two parts: Chinese Knowledge and Information Processing (CKIP) auto tagging, and extraction of the feature opinion module. We invited several experts to score the filtered opinions to build the opinion score database. In the opinion decision module, an aggregate prelease score for each movie is compiled from the comments on movie trailers on YouTube for the opinion score database. In the same way, we accumulate a score from the Yahoo! Movie comment for the opinion score database. 3.1 Collecting data In the first step, the data for users comments are gathered from YouTube and Yahoo! Movie. The reviews and some metadata can also be collected from each user s review document, such as the author, the like value (i.e., the number of readers who like this comment), the dislike value (i.e., the number of readers who dislike this comment) and so on. For each movie, we collect all of the comments and the metadata before the opening weekend as well as the income from each movie during the same time period. After watching the movie trailer on YouTube, users are asked to express their opinion as to whether they would be willing to watch the movie in the theater. This study aims to discover the effect that the comments on these movie trailers and metadata have on sales during the opening weekend. Fig. 1. An overview of the proposed framework 3.2 Pre-processing module After the preprocessing step, the processed results for each movie are summarized to obtain a picture of the users opinions. The steps are described as follows: (1) CKIP Auto tag Generally speaking, the preprocessing includes parser, part-of-speech, and feature candidate extraction functions. First, some pre-processing of words is performed including removal of stop words, stemming and so on. Next, we adopt the CKIP System to perform the Part-of-speech (POS) tagging process to produce the available datasets [24]. This process involves assigning a part-of speech (like noun, verb, pronoun, adverb, and adjective) or other lexical class marker to each word in a sentence. This process involves tokenizing every sentence into every word phrase. Each review sentence is parsed and tagged and the processing results are stored in the database. (2) Extraction of feature opinion pairs This step identifies the product features about which many people have expressed their opinions. Hu and Liu (2004)

3 established a good framework for extracting the feature opinion pairs. A feature-opinion pair consists of a feature and a relevant opinion. Product features are usually nouns or noun phrases in review sentences. First, we extract the frequent features that appear explicitly as nouns or noun phrases in the reviews. Next, we identify opinion words which are expressed as subjective opinions based on the frequent features. This study uses adjectives as opinion words. We also limit the opinion word extraction to those sentences that contain one or more product features, as we are only interested in customers opinions about these movie features. Word features [8], opinion dictionaries [9], and syntactic structures [10-12] are used for opinion analysis. 3.3 Opinion decision module Several domain experts were consulted to determine the scores for each opinion word which have been extracted from the previous steps. The scores are from 1 to 5. Score 5 means most consumers used this opinion word to express their satisfactions. Fig.2 is part of the opinion score database. algorithm, one aggregate score for each movie was obtained from comments on movie trailers from YouTube and another was determined from reviews on Yahoo Movie. The comments for five movies for each genre were summarized; the results are illustrated in Figures 3-8. Most of the aggregated scores from the YouTube comments seem to be lower than the scores generated from reviews on Yahoo Movie. The aggregated scores only have a value of one as can be seen in Fig.3. For marketing purposes, the trailers always contain the most exciting scenes in order to attract people to come and see the movies. After many people watched the trailer of THOR, the expect value was very high shown in Fig.4. However, it was found that most viewers tended to use simple words to express their feeling about action movies meaning the comments were too short for the proposed algorithm to predict a score which would convey the true feeling of the users. Fig.3 Results for action movies. Fig.2 part of the opinion score database. The proposed algorithm considers both positive and negative opinions in the aggregation of a fair final score. After analyzing each review, the proposed algorithm can determine a final score. 4. EXPERIMENTAL RESULTS AND ANALYSIS We used the customer reviews of a few movies from the Yahoo Movies message board ( There are several reasons Yahoo Movies serves as a good source of movie WoM. In this study, we selected 104 movies that appeared in theaters from September 2013 to January We also gathered the comments on trailers for those 104 movies from YouTube. The number of comments obtained from YouTube was 4859 and the number gathered from Yahoo Movies was There were four movie genres included: action, comedy, drama and thrillers. From the Fig.4 The number of users watched trailers of action movies and their Examination of Fig.5 shows an interesting fact, that some of the aggregated scores based on trailers seen on YouTube are the same as the reviews from Yahoo Movie. It can be seen that, especially when the movies are popular, the users comments contain more sentences. This makes it easier for the proposed algorithm to predict a score which is close to the thinking of users after seeing a movie. Fig. 6 is illustrated an interesting fact that the Zone Pro Site has a very good reputation in Taiwan. The proposed algorithm can produce a precise aggregate of the score.

4 Fig.5 Results for comedies. Fig.8 The number of users watched trailers of action movies and their 5. CONCLUSIONS Fig.6 The number of users watched trailers of action movies and their Two sets aggregated scores are compiled, one based on trailers from YouTube and the same for reviews from Yahoo Movie, as shown in Fig.7. It is noted that after seeing a drama, viewers may identify with the main character in the movie. They also tend to write many sentences to express their feelings in the social community. For example, Cold Bloom is a well-known Japanese movie featuring a story that happened after the March 11, 2011 earthquake and tsunami. The scenes are familiar enough to young people in Taiwan that they can identify with the characters. The expect value is 5 shown in Fig.8. The reviews attracted by the prelease trailer were extensive and there were more comments about their feelings left on YouTube. The richer the comments are, the more precise the score predicted by the proposed algorithm. For the genre of dramas, the users evaluation of the trailer is the same as the feelings expressed after seeing the movie. Fig.7 Results for dramas and thrillers. In this paper, a novel opinion mining framework is proposed for discovering knowledge from the reviews of movie trailers on YouTube and comments on Yahoo Movies. The objective is to automatically discover the WOM trends for online reviews of newly released products. We can observe the differences between comments on the prelease video and those made after seeing the movies. We conducted extensive experiments to evaluate the effectiveness of the proposed algorithm. We collected comments on several movies from both YouTube and Yahoo! Movie. After analyzing all the comments, we found some interesting facts about the users behavior. The length of users comments was influenced by the genre of the movies. In future, we will conduct more experiments to detect differences in users comments collected from the trailer on YouTube and Yahoo movie. ACKNOWLEDGMENT The authors would like to acknowledge the Ministry of Science and Technology, Taiwan, R.O.C. which provides supports in part under the grant NSC number NSC H MY3. REFERENCES [1] M. Hu, and B. Liu, Mining and summarizing customer reviews, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. Seattle, WA, USA, ACM, 2004, pp [2] Q. Miao, Q. Li, et al, AMAZING: A sentiment mining and retrieval system, Expert Systems with Applications 36(3, Part 2), 2009, pp [3] Y. Liu, Word of mouth for movies: Its dynamics and impact on box office revenue, Journal of Marketing 70(3), 2006, pp [4] M. Hu, and B. Liu, Mining opinion features in customer reviews, Proceedings of the 19th national conference on Artifical intelligence. San Jose, California, AAAI Press, 2004, pp [5] L.C. Cheng, Z.H. Ke, B. M. Shiue, Detecting changes of opinion from customer reviews In proceeding of: Eighth International Conference

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