Aspects Of Movie Research Articles

Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’ dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve as a memory to process dependent sentences to infer implicit aspects. It can identify implicit aspects from four types of sentences, including independent and three types of dependent sentences. The study is evaluated on a large movie reviews dataset with 50k examples. The experimental results showed that the explicit aspect identification method achieved 89% F1-score and implicit aspect extraction methods achieved 76% F1-score. In addition, the proposed approach also performs better than the state-of-the-art techniques (NMFIAD and ML-KB+) on the product review dataset, where it achieved 93% precision, 92% recall, and 93% F1-score.

Evaluation of Wadjda: A Comparative Discourse Analysis of Western and Saudi Movie Reviews

Since people are showing more interest recently in movies and as movie reviews of the same movie could vary in the degree of their objectivity, this paper seeks to answer why movie reviews may differ in their evaluation of the same work. To this end, a critical discourse analysis using Fairclough’s three-dimensional framework and Martin and White’s attitude framework is carried out to analyze two reviews of the Saudi movie Wadjda, one written in English by a western critic, and the other is Arabic written by a Saudi critic. The textual analysis of attitudinal expressions shows that the English text maintains an even distribution between the negative and positive expressions, and most of the negative expressions do not assess the aspects of the movie but rather the Saudi society. In the Arabic text, however, the negative expressions outnumber the positive ones, and they are mostly related to the movie and the director. Using Fairclough’s framework, the author explains the findings of the textual analysis of the English review and the critic’s avoidance of negative evaluation as a result of Eurocentrism and the way the western media represent Arabs and Muslims. However, the negative appraisal of several movie aspects found in the Arabic review could be seen as a result of the critic being part of the society and, thus, not distracted by the social issues.

A Multi-Criteria Movie Recommendation System based on User Preferences and Movie Features

In this research, we develop a multi-criteria movie recommendation system that provides personalised recommendations by taking into consideration both user preferences and movie aspects. To get over each method's specific drawbacks, the suggested system takes a hybrid approach that combines collaborative filtering with content-based techniques. The system uses collaborative filtering to capture user preferences based on historical ratings, while content-based methods analyze movie features such as genre, director, actors, and keywords to enhance the recommendation process. Additionally, we integrate various external data sources like movie reviews, social media sentiment, and box office performance to enrich the movie feature set. The system employs a weighted aggregation method to combine these criteria and generate a comprehensive recommendation score. The effectiveness of the proposed system is evaluated utilizing standard metrics including recall, precision, and F1-score on a publicly available dataset. The results demonstrate that our multi-criteria recommendation system effectively captures user preferences and provides more accurate and diverse recommendations compared to traditional single-criterion approaches.

Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis techniques have been applied in several application domains. From the last two decades, these techniques have been developed mostly for product and service application domains. However, very few aspect-based sentiment techniques have been proposed for the movie application domain. Moreover, these techniques only mine specific aspects (Script, Director, and Actor) of a movie application domain, nevertheless, the movie application domain is more complex than the product and service application domain. Since, it contains NER (Named Entity Recognition) problem and it cannot be ignored, since there is an opinion often associated with it. Consequently, in this paper MAIM (Movie Aspect Identification Model) is proposed that can extract not only movie specific aspects, also identifies NEs (Named Entities) such as Person Name and Movie Title. The three main contributions are 1) the identification of infrequent aspects, 2) the identification of NE (named entity) in movie application domain, 3) identifying N-gram opinion words as an entity. MAIM incorporates the BiLSTM-CRF hybrid technique and is implemented on the movie application domain having precision 89.9%, recall 88.9% and f1-measure 89.4%. The experimental results show that MAIM performs better than baseline models CRF and LSTM-CRF.

An Attention-Based Unsupervised Adversarial Model for Movie Review Spam Detection

With the prevalence of the Internet, online reviews have become a valuable information resource for people. However, the authenticity of online reviews remains a concern, and deceptive reviews have become one of the most urgent network security problems to be solved. Review spams will mislead users into making suboptimal choices and inflict their trust in online reviews. Most existing research manually extracted features and labeled training samples, which are usually complicated and time-consuming. This paper focuses primarily on a neglected emerging domain - movie review, and develops a novel unsupervised spam detection model with an attention mechanism. By extracting the statistical features of reviews, it is revealed that users will express their sentiments on different aspects of movies in reviews. An attention mechanism is introduced in the review embedding, and the conditional generative adversarial network is exploited to learn users' review style for different genres of movies. The proposed model is evaluated on movie reviews crawled from Douban, a Chinese online community where people could express their feelings about movies. The experimental results demonstrate the superior performance of the proposed approach.

Investigating the Effect of eWOM in Movie Box Office Success Through an Aspect-Based Approach

This study examines the influence of Electronic Word of Mouth (eWOM) on the box office revenue generation of movies in the U.S domestic market using the technique of Aspect-Based Sentiment Analysis (ABSA) and aspect identification. The analysis was conducted on the sentiment score and frequency of five movie aspects from the user reviews collected from high grossing 2014 movies. This study revealed a significant dependence on the aspect-based sentiment frequency of the movie's Story aspect. Surprisingly, the data also showed a strong dependence of movie success on the negative sentiment frequency on the Casting aspect. The findings of the study suggest that the eWOM present in online movie reviews can be used to predict the performance of a movie at the box office by monitoring the aspect's frequency of sentiment, which can be referred to as a metric of the online “buzz” of the movie.

Movie aspects, tweet metrics, and movie revenues: The influence of iOS vs. Android

Microblogging word of mouth (MWOM) using Twitter has been found to impact the success of experiential products such as movies. However, the influence of the type of device or platform used for tweeting (iOS or Android) on the relationship between well-established tweet metrics - valence, volume, and time period of tweeting - and movie performance is not yet known. Furthermore, it is not known if users of these platforms differ in the aspects of movies they discuss and how that may influence tweet metrics. In this study, we investigated these gaps by analyzing more than four million tweets for 29 movies from both iOS and Android users and conducted a robustness check on another 8 movies. Results from mixed model estimations show that valence of tweets on Android before a movie's release and volume of tweets on iOS after the release significantly influence the revenues of a movie. Results also show that mentions of director and script are more important in the case of Android users whereas mentions of production and music are more important in the case of iOS users. Finally, results show that it may be more productive for movie studios and advertisers to reach the more prolific Twitter users on Android but relatively newer Twitter users on iOS. These findings have significant implications for movie studios as well as mobile advertisers to target their promotions to these platform users accordingly.

<심청전>의 영화화 양상과 문화사적 의미

<심청전>의 영화화 양상과 문화사적 의미

Comparison of Film Language and Historical Fact

Film language is an important aspect of analyzing film. Film language is the term used to break down the aspects of movies to develop the deeper meanings being communicated to the audience. The following essay studies three areas of film language in these historical movies: 12 Years a Slave, Nanking, and Frida. These three films will be examined through their use of dialogue, cinematography, and metaphor in the characters' actions. The essay compares the similarities and differences in the three films and offers a deep analysis of each film individually. Furthermore, a historical understanding is developed from the messages conveyed in the films. It is concluded that the movies share a similarity in their use of dialogue to convey character development. However, the difference in the three films is portrayed in their use of metaphor in the characters' actions to communicate various messages to the viewers. Through the study of the three historical films, we develop a greater understanding of the historical messages they express and a deep appreciation of the intricacy of the film industry.

Sentiment analysis of movie reviews: finding most important movie aspects using driving factors

The opinion conveyed by the user towards the movie can be understood by sentiment analysis of the movie review. In the current work we focus on finding the aspects of a movie review which direct its polarity the most. This is achieved using certain driving factors, which are scores given to the various movie aspects. Generally its found that aspects with high driving factors affect the review polarity the most.

Exposure to Movie Reckless Driving in Early Adolescence Predicts Reckless, but Not Inattentive Driving.

ObjectiveWe examine the association between exposure to depictions of reckless driving in movies and unsafe driving, modeling inattentive and reckless driving as separate outcomes.MethodsData were obtained by telephone from 1,630 US adolescents aged 10 to 14 years at baseline who were drivers at a survey 6 years later. Exposure to movie reckless driving was measured based on movies seen from a randomly selected list of 50 movie titles that had been content coded for reckless driving among characters. Associations were tested with inattentive and reckless driving behaviors in the subsequent survey–controlling for baseline age, sex, socioeconomic status, parental education, school performance, extracurricular activities, daily television and video/computer game exposure, number of movies watched per week, self-regulation and sensation seeking.ResultsExposure to movie reckless driving was common, with approximately 10% of movie characters having driven recklessly. Confirmatory factor analysis revealed a significant distinction between items tapping reckless and inattentive driving at the 6th wave. Age and exposure to movie reckless driving at baseline were directly associated with wave-6 reckless (but not inattentive) driving. Additionally, growth in sensation seeking mediated a prospective relation between the total number of movies watched per week at baseline and reckless driving, independent of exposure to movie reckless driving. Males and high sensation seekers reported lower seatbelt usage and more reckless driving, whereas lower self-regulation predicted inattentive driving.DiscussionIn this study, exposure to movie reckless driving during early adolescence predicted adolescents’ reckless driving, suggesting a direct modeling effect. Other aspects of movies were also associated with reckless driving, with that association mediated through growth in sensation seeking. Predictors of reckless driving were different from predictors of inattentive driving, with lower self-regulation associated with the latter outcome. Making a clear distinction between interventions for reckless or inattentive driving seems crucial for accident prevention.

Aspect-based sentiment analysis of movie reviews on discussion boards

In this article, a method for automatic sentiment analysis of movie reviews is proposed, implemented and evaluated. In contrast to most studies that focus on determining only sentiment orientation (positive versus negative), the proposed method performs fine-grained analysis to determine both the sentiment orientation and sentiment strength of the reviewer towards various aspects of a movie. Sentences in review documents contain independent clauses that express different sentiments toward different aspects of a movie. The method adopts a linguistic approach of computing the sentiment of a clause from the prior sentiment scores assigned to individual words, taking into consideration the grammatical dependency structure of the clause. The prior sentiment scores of about 32,000 individual words are derived from SentiWordNet with the help of a subjectivity lexicon. Negation is delicately handled. The output sentiment scores can be used to identify the most positive and negative clauses or sentences with respect to particular movie aspects.

Comparing sentiment expression in movie reviews from four online genres

PurposeThis paper aims to investigate the characteristics and differences in sentiment expression in movie review documents from four online opinion genres – blog postings, discussion board threads, user reviews, and critic reviews.Design/methodology/approachA collection of movie review documents was harvested from the four types of web sources, and a sample of 520 movie reviews were analysed to compare the content and textual characteristics across the four genres. The analysis focused on document and sentence length, part‐of‐speech distribution, vocabulary, aspects of movies discussed, star ratings used and multimedia content in the reviews. The study also identified frequently occurring positive and negative terms in the different genres, as well as the pattern of responses in discussion threads.FindingsCritic reviews and blog postings are longer than user reviews and discussion threads, and contain longer sentences. Critic reviews and blogs contain more nouns and prepositions, whereas discussion board and user reviews have more verbs and adverbs. Critic reviews have the largest vocabulary and also the highest proportion of unique terms not found in the other genres. The most informative sentiment words in each genre are provided in the paper. With regard to content, critic reviews are more comprehensive in coverage, and discuss the movie director much more often than the other genres. User reviews discuss the scene aspects (including action and visual effects) more often than the other genres, while blogs tend to talk about the cast, and discuss the music and sound slightly more often.Research limitations/implicationsThe study only analysed movie review documents. Similar content and text analysis studies can be carried out in other domains, such as commercial product reviews, celebrity reviews, company reviews and political opinions to compare the results.Originality/valueThe main contribution of the study is the sentiment content analysis results across genres, which show the similarities and differences in content and textual characteristics in the four online opinion genres. The insights will be useful in designing automatic sentiment summarisation methods for multiple online genres.