Movie Rating Research Articles

Personalized movie rating prediction based on the data of short video users and videos information

This study presents a sophisticated personalized movie rating prediction model that incorporates user-generated content from Douyin, a prominent video-based social media platform. The proposed model effectively leverages user demographic data, movie metadata, historical rating records, and sentiment analysis of user comments to precisely predict a user's rating score for films. This study employs lexicon-integrated two-channel CNN-LSTM family models for conducting sentiment analysis. Furthermore, the study explores a variety of regression models, such as linear regression, ridge regression, LASSO regression, and Elastic Net regression, to determine the most suitable model for the dataset being analyzed. This analysis aims to enhance the accuracy and effectiveness of the sentiment analysis performed on the given dataset. Furthermore, we contemplate the application of collaborative filtering algorithms, such as Alternating Least Squares (ALS), in developing our personalized movie rating prediction model. By incorporating user-generated content from Douyin, our methodology enhances the accuracy of movie rating predictions and underscores the significance of personalized recommendations in the era of social media.

A collaborative filtering recommendation framework utilizing social networks

Collaborative filtering is a widely used technique for providing personalized recommendations to users. However, traditional collaborative filtering methods fail to consider the social connections between users. The current study proposes a collaborative filtering recommendation framework that employs social networks to generate more precise and pertinent recommendations. The framework is based on a modified version of the user-based collaborative filtering algorithm, which computes user similarity based on their ratings and social connections. The similarity measure is determined by a weighted combination of these two factors, with the weights learned through an optimization process. The framework is evaluated using a dataset of movie ratings and social connections between users. The findings reveal that the proposed approach outperforms traditional collaborative filtering methods regarding recommendation accuracy and relevance. Moreover, the framework can offer more diverse recommendations compared to traditional methods. In summary, the proposed framework integrates social networks to enhance the accuracy and relevance of collaborative filtering recommendations. The approach has various applications, including e-commerce, music, and movie recommendation, and can potentially address the issues of cold-start and sparsity in collaborative filtering.

Movie Recommender System Using Decision Tree Method

In this modern era, many things that can be done online, one of which is watching movies. When the number of movies increases, people often find it difficult to decide which movie to watch next. To solve this problem, a useful recommendation system was developed to find movies that one might like based on movies that have been watched before. This research develops a movie recommendation system using Collaborative Filtering (CF) with the Decision Tree algorithm. In this study, the data used were movie data and ratings obtained from the grouplens.org website. Then the movielens dataset is filtered and only saves movies with a rating of more than 50 that are used in the recommendation system. In this study, Mean Absolute Error (MAE) is used as a method to assess the accuracy of the movie recommendation system. Based on the research that has been done, Decision Tree gets better results with an MAE value of 0,942 compared to Collaborative Filtering with an MAE value of 1,242.

A movie recommendation method based on knowledge graph and time series

Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model.

Predicting movie ratings using Korean reviews with multinomial inverse regression

Predicting movie ratings using Korean reviews with multinomial inverse regression

Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System

Movies are an important part of our daily entertainment, with the global movie industry experiencing significant growth and capturing the attention of people of all ages. However, only a few movies achieve success, leading to pressure on movie production stakeholders. Therefore, researchers and moviemakers require expert systems that can accurately predict the probability of a movie's success prior to its production. Most research on predicting movie popularity has focused on post-production stages, but it's essential to predict a movie's success at an early stage to enable necessary changes to be made. To this end, a content-based movie recommendation system (RS) has been proposed that uses preliminary movie features such as genre, cast, director, keywords, and movie description. This RS output and movie rating and voting information are then used to create a new feature set, which is input into a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. The study also proposes a system to predict the popularity of the upcoming movie among different age groups, dividing the audience into four categories: junior, teenage, mid-age, and senior. The study uses publicly available data from the Internet Movie Database (IMDb) and The Movie Database (TMDb). The multiclass classification model implemented in this study achieved 96.8% accuracy, outperforming all benchmark models. Overall, this study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decision- making.

Building a Timeline for LGBTQ+ Global Cinemas (1910–2019): Movie Production Trends from a Collaborative Internet Cinema Database

Building a Timeline for LGBTQ+ Global Cinemas (1910–2019): Movie Production Trends from a Collaborative Internet Cinema Database

The Influence of Douban Reviews and Ratings on Chinese Audiences' Movie-watching Choices

In the process of word-of-mouth, recurring social media ratings and strong reviews can influence viewers' perceptions of a film and result in changes in viewing choices. As the most authoritative movie review website in China, Douban's high score and popular movie reviews are a household name, and even the rating of a movie on Douban and whether the review is good or not become the reason for people to choose whether to watch the movie or not. This paper examines the influence of Douban's movie reviews and ratings on Chinese movie-watching choices through the use of questionnaires in the context of the media environment and media psychology, based on Douban's unique media culture. The results of the questionnaire show that in a film market surrounded by all kinds of information, Douban movies largely satisfy the needs and psychology of users' viewing guides, and in the eyes of a large proportion of people Douban's ratings and reviews have become the main measure of whether viewers will watch them or not.

A Smart Movie Suitability Rating System Based on Subtitle

With the enormous growth rate in the number of movies coming into our lives, it can be very challenging to decide whether a movie is suitable for a family or not. Almost every country has a Movie Rating System that determines movies’ suitability age. But these current movie rating systems require watching the full movie with a professional. In this paper, we developed a model which can determine the rating level of the movie by only using its subtitle without any professional interfere. To convert the text data to numbers, we use TF-IDF vectorizer, WIDF vectorizer and Glasgow Weighting Scheme. We utilized random forest, support vector machine, k-nearest neighbor and multinomial naive bayes to find the best combination that achieves the highest results. We achieved an accuracy of 85%. The result of our classification approach is promising and can be used by the movie rating committee for pre-evaluation.
 Cautionary Note: In some chapters of this paper may contain some words that many will find offensive or inappropriateness; however, this cannot be avoided owing to the nature of the work

Near majority of adults favor R ratings for films with smoking

Near majority of adults favor R ratings for films with smoking

Prediction of movie success based on machine learning and twitter sentiment analysis using internet movie database data

<span lang="EN-US">Nowadays, predicting the success of a new movie is a crucial task. In this work, the hybrid approach considers the movie features as well as sentiment expressed in the movie review to predict the success rate of a movie. Multiple movie features such as title, director, star cast, and writer. Are considered for prediction. The related raw data is collected from the internet movie database (IMDb) website and after pre-processing, the collected data is used to generate the supervised machine learning model. Different supervised learning models are compared and the one with the best results is used further. The mean squared error, root mean squared error and r2 score of the models generated are comparable with existing models. Further, sentiment analysis of the movie-related tweets is performed. The accuracy of best sentiment analysis model is 88.47%. Finally, the two models are combined to give the success prediction rating of new movies and the results of the hybrid model are encouraging. The proposed model may be used to find the top-rated movies of a particular calendar year.</span>

Movie Recommendation System Based on Traditional Recommendation Algorithm and CNN Model

As streaming services have expanded in recent years, an excellent recommendation algorithm, as one of the core technologies in movie service, brings huge benefits. Although the current application research of recommendation algorithms is mature, most systems or products usually rely on only one main algorithm. This makes it difficult for the system to overcome the shortcomings of various algorithms and cannot benefit from the combination of multiple recommendation algorithms. In addition, the widely used recommendation models are found to be unable to extract the finer features of users and movies, and the calculation time is very long. Meanwhile, the recommendation results are inaccurate, and they are not user-friendly. In this research, we design and construct a system to recommend movies which is consisted by a model based on a convolutional neural network consisting. By extracting the features of users and movies, we can calculate the direct similarity of different users' movies and then predict movie ratings to give recommendations. When extracting features of users and movies, we refer to traditional algorithms based on content and content. Through the "cosine similarity" and "user movie score matrix", the Item-Based Collaborative Filtering and User-Based Collaborative Filtering can be well implemented. To sum up, our movie recommendation system is based on Convolutional Neural Network (CNN) model and the performance of the system is improved by adopting multiple recommendation algorithms such as content-based recommendation and system filtering. We efficiently trained the neural network and finally built a movie recommendation system with a faster operation speed, although some parts are not perfect.

Effective Community Search on Large Attributed Bipartite Graphs

Community search over bipartite graphs has attracted significant interest recently. In many applications such as the user–item bipartite graph in e-commerce and customer–movie bipartite graph in movie rating website, nodes tend to have attributes. However, the previous community search algorithms on bipartite graphs ignore attributes, thus making them to return results with poor cohesion with respect to their node attributes. In this paper, we study the community search problem on attributed bipartite graphs. Given a query vertex [Formula: see text], we aim to find the attributed [Formula: see text]-communities of [Formula: see text], where the structure cohesiveness of the community is described by the [Formula: see text]-core model, and the attribute similarity of two groups of nodes in the subgraph is maximized. In order to retrieve attributed communities from bipartite graphs, we first propose a basic algorithm composed of two steps: the generation and verification of candidate keyword sets, and then two improved query algorithms Inc and Dec are proposed. Inc is proposed considering the anti-monotonicity property of attributed bipartite graphs, then we adopt different generating methods and verify the order of candidate keyword sets and propose the Dec algorithm. After evaluating our solutions on eight large graphs, the experimental results demonstrate that our methods are effective and efficient in querying the attributed communities on bipartite graphs.

Decision Support System for Movie Recommendations Based on Multi User Preferences Using the Simple Additive Weighting Method

At this time advances in technology and information have experienced rapid progress, one of which is in the field ofentertainment, both audio and visual. And one of the entertainments is movies. With the increasing number of movies,there are several classifications of movie genres to assist users in finding and selecting movies to watch, but the genreclassification itself is still very general. Due to the above factors, especially in genre, subgenre, rating, movie durationwhich always develops over time according to a certain pattern and also audiences who have different moviepreferences, the researcher sees that there is a need for an application that can recommend movies with preferencesthat can be set according to the wishes of movie lovers. From the problems that arise, this research was built using theSimple Additive Weighting (SAW) method which aims to make it easier for users to determine which movie to choose.This system produces a web-based information system using several parameters, namely the main genre of a movie,subgenre, movie rating, movie duration, and year of making.

Sentiment Analysis to Predict Movies Success Rate Based on NLTK Movie Review Corpora Using Machine Learning

With the proliferation of social networks, peoples express their opinions about different things or issues on social media without any hesitation. The rapid growth of textual data on social media are required to develop algorithms and techniques for recognizing people’s opinions towards specific subject. These opinions are helpful in business plans development, marketing trends, political parties’ popularity. The film industry can be an important revenue generating industry of any country. Peoples express their opinion on movie trailer using social media. The effective sentiment analysis of opinions on social media such as Twitter can be helpful to predict movie ratings. This research work focuses on developing a technique to predict movie success rate on the basis of tweets data. We have collected tweets about different movies after their trailer released by using hash tag method. We applied Sentiment analysis approach using Machine learning. In this study we utilized four key algorithms (Naïve Bayes, SVM, Neural Networks, decision tree) on NLTK Movie review corpora.

Aspect-Based Sentiment Analysis of Avatar 2 Movie Reviews on IMDb Using Support Vector Machine

In the digital age, IMDb plays a crucial role in influencing audience movie choices. However, IMDb's movie ratings lack detailed information about specific aspects of films considered important in the industry's evaluation of audience responses. To address this gap, we conducted aspect-based sentiment analysis on 3198 reviews of Avatar 2. We focused on narrative and cinematic elements in the movie reviews, such as character, conflict, location, time, mise-en-scene, cinematography, editing, and sound. After data collection, we labeled the aspects and sentiments, and through TF-IDF weighting and SMOTE balancing, we performed sentiment classification. The Support Vector Machine model with SMOTE proved most effective, highlighting crucial features often discussed by audiences in both positive and negative sentiments. This analysis provides valuable insights for the film industry, aiding in better movie production, marketing, and a deeper understanding of audience preferences. Our research demonstrates the significance of aspect-based sentiment analysis in guiding future film-making endeavors.

Do sex and violence sell internationally? A moderating role of cultural differences in the mediation effect of age ratings on the relationship between films’ content elements and worldwide box office performance

Although the determinants of films’ box office performance have been extensively examined in the past, there is a gap in the literature with regard to how the mediation effect of age ratings for movies on the relationship between content elements and box office performance differs across cultures. Through a moderated mediation analysis of films released in 111 countries in the past 100 years (between 1921 and 2020), this paper finds that when there are wider cultural distances between the home and host countries, content with higher levels of violence and morally loaded behaviors reap greater box office revenues in host countries. However, in total, movies with higher elements of violent content rather than content related to morally loaded behaviors contribute to high box office revenues in the international film market.

Forecasting movie rating using k-nearest neighbor based collaborative filtering

<p><span lang="EN-US">Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.</span></p>

Effects of Industrial Protection Policies on Firms’ Incentives and Consumer Welfare

Industrial protection policies have been widely implemented to protect domestic firms from foreign competition. This project specifically focused on examining the effects of the Domestic Movie Protection Month Policy that started in China in 2014. First, I demonstrated the existence of this policy by measuring the obvious delay of foreign movies during the summer (July and August) after 2014. Second, I compared the quality of domestic movies released during the summer (treatment group) with movies released at other times of the year (control group) to evaluate the effect of the policy. Two proxies for movie quality and consumer welfare were constructed for robustness checks: (1) Douban rating, which is the general rating of the entire population of the audience on Douban website, the biggest movie rating website in China; (2) Welfare Index which is based on the sentiment analysis constructed by the NLP (Natural Language Processing) of around 70,000 consumers’ comments. The final result showed that the Douban rating and Welfare Index were both significantly higher for domestic movies released during the summer after 2014 than before 2014, while the quality of movies in the control group remained unchanged. This finding supports the hypothesis that domestic firms will have more incentives to invest in quality upgrading if the industrial protection policy is implemented. This conclusion also adds to the recent heated discussions on whether policymakers shall continue to implement industrial protection policy not only in the movie industry but also in other industries that face severe foreign competition.

A supervised topic embedding model and its application.

We propose rTopicVec, a supervised topic embedding model that predicts response variables associated with documents by analyzing the text data. Topic modeling leverages document-level word co-occurrence patterns to learn latent topics of each document. While word embedding is a promising text analysis technique in which words are mapped into a low-dimensional continuous semantic space by exploiting the local word co-occurrence patterns within a small context window. Recently developed topic embedding benefits from combining those two approaches by modeling latent topics in a word embedding space. Our proposed rTopicVec and its regularized variant incorporate regression into the topic embedding model to model each document and a numerical label paired with the document jointly. In addition, our models yield topics predictive of the response variables as well as predict response variables for unlabeled documents. We evaluated the effectiveness of our models through experiments on two regression tasks: predicting stock return rates using news articles provided by Thomson Reuters and predicting movie ratings using movie reviews. Results showed that the prediction performance of our models was more accurate in comparison to three baselines with a statistically significant difference.