What movie will I watch today? The role of online review ratings, reviewers’ comments, and user’s gratification style

Browsing online ratings and viewers’ comments is an integral part of the experience of choosing and watching a movie. Current theories have broadened the concept of entertainment beyond amusement (hedonic motives) to include experiences of meaning, value, and self-development (eudaimonic motives). With a between-subjects design, we examined the role of the reviewer’s rating (medium rating vs high rating), comments (hedonic vs. eudaimonic), and participant’s gratification style on their interest in watching a movie. Results showed that participants (N = 383) reported a higher preference for the high rating movie. Results also revealed a match between comment type and individual gratification style, with participants with hedonic motives reporting more interest in the movie with hedonic comments, and those reporting eudaimonic motives for the movie with eudaimonic comments.info:eu-repo/semantics/acceptedVersio

Predicting self‐declared movie watching behavior using Facebook data and information‐fusion sensitivity analysis

The main purpose of this paper is to evaluate the feasibility of predicting whether yes or no a Facebook user has self-reported to have watched a given movie genre. Therefore, we apply a data analytical framework that (1) builds and evaluates several predictive models explaining self-declared movie watching behavior, and (2) provides insight into the importance of the predictors and their relationship with self-reported movie watching behavior. For the first outcome, we benchmark several algorithms (logistic regression, random forest, adaptive boosting, rotation forest, and naive Bayes) and evaluate their performance using the area under the receiver operating characteristic curve. For the second outcome, we evaluate variable importance and build partial dependence plots using information-fusion sensitivity analysis for different movie genres. To gather the data, we developed a custom native Facebook app. We resampled our dataset to make it representative of the general Facebook population with respect to age and gender. The results indicate that adaptive boosting outperforms all other algorithms. Time- and frequency-based variables related to media (movies, videos, and music) consumption constitute the list of top variables. To the best of our knowledge, this study is the first to fit predictive models of self-reported movie watching behavior and provide insights into the relationships that govern these models. Our models can be used as a decision tool for movie producers to target potential movie-watchers and market their movies more efficiently

Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media

[EN] In recent years, social media have become ubiquitous and important for social networking and content sharing. Moreover, the content generated by these websites remains largely untapped. Some researchers proved that social media have been a valuable source to predict the future outcomes of some events such as box-office movie revenues or political elections. Social media are also used by companies to measure the sentiment of customers about their brand and products. This work proposes a new social media based model to measure how users perceive new products from a technical point of view. This model relies on the analysis of advantages and drawbacks of products, which are both important aspects evaluated by consumers during the buying decision process. This model is based on a lexicon developed in a related work (Chiarello et. al, 2017) to analyse patents and detect advantages and drawbacks connected to a certain technology. The results show that when a product has a certain technological complexity and fuels a more technical debate, advantages and drawbacks analysis is more efficient than sentiment analysis in producing technical-functional judgements.Chiarello, F.; Bonaccorsi, A.; Fantoni, G.; Ossola, G.; Cimino, A.; Dell'orletta, F. (2018). Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 145-156. https://doi.org/10.4995/CARMA2018.2018.8336OCS14515

Peran Brand Reputation dalam Mempengaruhi Movie Consumers’ Willingness dengan Release Strategy Sebagai Moderator

Sejarah industri perfilman Indonesia yang sempat mengalami naik-turun memberikan keunikan tersendiri bagi memori pelaku industri perfilman. Sebuah rumah produksi bernama MD Pictures pada tahun 2018 memberikan perubahan berarti dengan mencatatkan saham mereka di Bursa Efek Indonesia, di mana untuk pertama kalinya industri perfilman terbuka kepada masyarakat. Menjadi pelopor dengan reputasi baik seharusnya menjadikan masyarakat lebih rela mengonsumsi produk buatan mereka. Namun ternyata, data menunjukkan bahwa MD Pictures belum bisa mengalahkan salah satu pesaingnya untuk menduduki peringkat pertama box office. Tujuan dari penelitian ini adalah untuk menganalisis pengaruh brand reputation terhadap consumer willingness secara langsung dan juga pengaruh brand reputation terhadap consumer willingness jika dimoderasi oleh release strategy. Penelitian dilakukan dengan cara menyebarkan kuesioner daring kepada 183 sampel responden konsumen yang pernah menonton film produksi MD Pictures di bioskop. Metode kuantitatif yang digunakan dalam penelitian ini terdiri atas uji validitas dan reliabilitas, uji regresi sederhana, serta uji regresi moderasi yang diolah menggunakan program IBM SPSS 25. Hasil dari pengujian tersebut menunjukkan bahwa brand reputation memberikan pengaruh negatif terhadap consumer willingness secara langsung, namun berpengaruh positif sebesar 47,5% jika dimoderasi oleh release strategy