Rotten Tomatoes Research Articles

Bad Apples on Rotten Tomatoes: Critics, Crowds, and Gender Bias in Product Ratings

This paper shows that movie crowd reviewers provide evaluations that are biased against movies with a stronger female presence relative to professional critics.

Allusion as a means of implementing intertextuality in English-language online film review

The article discusses ways to actualize allusion as an intertextual element. Intertextual inclusions in the texts of the film review are designed to illustrate the degree of professional competence of the reviewer and his interpretative qualities using the comparative analytical method. Allusion, being a stylistic device for the realization of intertextuality, contains a certain pretext. This pretext performs the function of a precedent phenomenon associated with some collective invariant component of linguistic consciousness. The purpose of this article is to consider in film review the ways in which allusion functions as an intertextual reference. The empirical material of this study was a corpus of 30 movie reviews from 2021–2023, taken from the review aggregator website Rotten Tomatoes. Direct or indirect reference to a specific cinematic phenomenon not only increases the general erudition and awareness of the reader, but also serves as a means of providing a perlocative effect when reading this film review. Allusion, being a means of realizing the author’s pragmatic intentions, performs a comparative function by building intertextual references. The referents in film reviews are the names of films, directors, actors and other precedent units of the world of cinema.

Primary Screening of Fungi with Aflatoxin-Production Potential in Rotten Tomato sold at Shasha Market Akure

The present study aimed to isolate, characterise, and evaluate aflatoxin-producing fungus from rotting tomatoes sold in Shasha market, Akure. Rotten tomato fruits from the Shasha market in Akure were cultured on potato dextrose and malt extract agar that was made selective for fungi by adding chloramphenicol to inhibit bacteria. Results showed that fungal isolates have 1.40 to 3.30 x 103 sfu/g of total fungus. Penicillium spp., Rhizopus stolonifer, Mucor mucedo, and Phytophthora spp. had the lowest proportion of occurrence (4.76%), followed by Aspergillus flavus (33.33%) and Aspergillus niger (23.81%). Hepatoma, or liver cancer, and severe hepatitis are caused by aflatoxin, especially in underdeveloped nations like Nigeria. This study informs people that eating rotten tomatoes is harmful. To avoid aflatoxins-producing fungus contamination of tomato fruits, farmers and marketers must implement pre- and post-harvest procedures.

Do Androids Dream of Bad TV?: Un/originality in Neil Burger’s Voyagers

Critics did not take kindly to Neil Burger’s Voyager (2021). On Rotten Tomatoes, the film scored a dismal 25%, and the consensus is that it’s a trip best not taken: “It has a game cast and a premise ripe with potential, but Voyagers drifts in familiar orbit rather than fully exploring its intriguing themes.” This article seeks neither to reclaim the film as an unjustly neglected cinematic masterpiece nor to assert its importance in the canon of dystopian works. Rather, it treats Voyagers as a test case for exploring our own critical investment in the genre. Our aims are twofold. First, we argue that the film speaks to the dystopian genre’s fundamental distrust of future generations to make the best decisions. It effectively exposes the central irony that we presume to know best though we had signally failed to do right by our planet in the first place. Secondly, we reveal how unoriginal work can still point to new ways forward. By the end of the film, the Humanitas mission is back on course, following Zac’s (Fionn Whitehead) demise. Sela (Lily-Rose Depp) is surely right to wonder, to Christopher (Tye Sheridan), how they should ensure that mutiny doesn’t recur. The short answer? They can’t—and they probably shouldn’t. The capacity for events, like those we witness in Voyagers, to recur—and many times over too—is at least partially responsible for the dystopian genre’s appeal and it contributes to the genre’s persistent ethical ruminations. This essay advances scholarship by suggesting that even the most derivative of cinema can offer profound insights into our world, in this case, how democracies work.

Sentiment analysis of movie review classifications using deep learning approaches

Movie reviews reflect how the public feels about a movie they have watched. However, because many reviews are posted on various websites, it is practically impossible to read each one. Summarizing all movie reviews can help people make informed decisions without reading through all of them. Previous studies have used different machine learning and deep learning techniques for sentiment analysis (SA), but few have combined comprehensive hyperparameter tuning and novel datasets for better performance. This paper presents an SA approach using deep learning models with optimized hyperparameters and a novel Rotten Tomatoes (RT) dataset to help viewers make better movie choices. SA, or opinion mining, is a computational technique to extract and analyze opinions and emotions expressed in text. We explore deep learning models such as Long Short-Term Memory (LSTM), XLNet, Convolutional Neural Networks-LSTM (CNN-LSTM), and Bidirectional Encoder Representations from Transformers (BERT). These models are known for capturing complex language patterns and context from raw text data. XLNet, a pre-trained model, effectively understands context by considering all possible permutations of the input sequence, BERT excels at using bidirectional context to understand text, LSTM retains information about long-term patterns in sequential data, and CNN-LSTM combines local and global context for reliable feature extraction. The RT dataset was pre-processed with data cleaning, spelling correction, lemmatization, and handling of informal words to improve the results. Our experiments show that XLNet performed better than other models on the Rotten Tomatoes dataset. The study demonstrates that SA of movie reviews provides insights into emotions and attitudes, allowing us to estimate a movie’s performance based on its overall sentiment.

Challenges in the spread of feminism on social media: A case study on Barbie’s dissemination and promotion on Rotten Tomatoes

This study investigates the challenges of disseminating feminist messages on social media, focusing on the film Barbie as an example. Feminism has gained significant academic attention, intersecting with various social, political, and economic domains. The study utilizes both quantitative and qualitative methods to conduct a comprehensive analysis of social media reactions and feedback horn platforms such as Twitter. Facebook, and Instagram. The findings reveal a complex landscape where Barbie is both praised for its progressive portrayal of female empowerment and criticized for superficial engagement with deeper feminist issues. Utilizing word clouds to identify dominant themes, the research provides a detailed understanding of public perceptions. The study highlights the evolving nature of feminist discourse in the digital age and underscores the critical role of media in shaping public perceptions of gender equality.

That funny feeling: the tragic stylings of Bo Burnham’s Inside

Bo Burnham’s Inside, promoted as a musical comedy special by its platform Netflix, released in June 2021 to almost unquestioning praise, and has now won 3 Emmys. The work is an evocative retrospective of Burnham’s fictionalized experience during the Covid-19 quarantine. Written, performed, filmed, and edited by Burnham himself, the piece reflects his career as a musical comedy performance artist, and also provides an intimate look into his struggles with mental health, internet culture, and artistic ego. Of particular appeal in this piece is the complex pairing of comedy with pathos. Its songs have become ironic backgrounds to TikTok videos and popular sea shanty renditions. Critics remark: ‘What he’s describing is the near-universal feeling of despair in 2020. And that feels good’ (Herman 2021); ‘A tricky work that for all its boundary-crossing remains in the end a comedy in the spirit of neurotic, self-loathing stand-up’ (Zinoman 2021); ‘A claustrophobic masterclass in comedy and introspection, Inside is a beautifully bleak, hilariously hopeful special’ (Rotten Tomatoes). It is this complexity which I seek to investigate. I assert that Bo Burnham’s Inside achieves its poignancy not through its comedic stylings, but through its implementation of a traditional Aristotelian tragic structure. This paper will discuss the ways in which Burnham follows the tragic structure and the monomyth while engaging with, and subverting, benign violation theory to provide a postmodern catharsis in its audience that feels like comedy, but ultimately provides a shared expression of, and outlet for, the audience’s grief.

The Power of Chaos

The Power of Chaos

Do American Critic Reviews Affect Film Consumption Abroad? The Brazilian Case

The Brazilian film market consists mainly of American films; most of these films are released after their release in the United States, while a few are released before or simultaneously. The recent rapid developments in information and communication technology (ICT) changed film-showing technology to a digital model, improving the possibility of simultaneous worldwide releases. ICT evolution also allowed Brazilian moviegoers to access American critic reviews in real time. We explore this scenario to study whether American critic reviews influence film consumption at theaters in Brazil. We employ regression analysis using data from 1,600 films exhibited in the country between 2007 and 2018 collected from Box Office Mojo and Rotten Tomatoes websites. Controlling for endogeneity problems, we documented a prediction effect of expert reviews on gapped film releases but not an influence effect on simultaneous releases, which correspond to the wide film releases in the United States.

Sentiment Analysis on Movie Review from Rotten Tomatoes Using Logistic Regression and Information Gain Feature Selection

The advancement and development of technology today can have a positive influence on the use of the internet and also on the dissemination of information it contains, including information about the world of cinema. With this convenience, there are many movie reviews that can be obtained easily. Movie reviews are very influential in the various ways movies are available. Thanks to the ease of various information on the internet, the number of movie reviews has become diverse. Therefore, it is necessary to do a sentiment analysis. In this research, the classification method used is Logistic Regression. The method was chosen because it has accurate classification accuracy. In this study, Information Gain was also chosen as a feature selection because it is good enough to do a filter approach in classification. Furthermore, for feature extraction, TF-IDF was chosen because it can overcome data imbalance in the dataset. The best model resulting from this research is a model built without using stemming in the preprocessing stage, without using information gain feature selection, and using parameters in Logistic Regression which produces an f1-score of 76.50%.

Sentiment Analysis on Movie Review from Rotten Tomatoes Using Modified Balanced Random Forest Method and Word2Vec

The film industry is one of the impacts of the rapid development of technology. This causes the film industry to increase every year. In addition, technological developments also affect the public to make it easier to access various movies from various websites. With many choices of movies, people need to know the quality of various movies by knowing the reviews of these movies from other people. However, the large number of audience reviews of a movie makes it difficult for people to categorize good movies and bad movies. The solution to the problem is to perform sentiment analysis on movie reviews. In this research, the classification method used is Modified Balanced Random Forest. This method was chosen because it can overcome imbalanced data and can increase accuracy and reduce time complexity. In this research, Word2Vec is also used as feature extraction. This feature extraction was chosen because previous research explained that Word2Vec has the advantage of being able to show the contextual similarity of two words in the resulting vector. The best model produced from this research is a model built without using stemming in the preprocessing stage, using 300 dimensions in Word2Vec, and using the Modified Balanced Random Forest classification method which produces an f1-score of 84.15%.

Sentiment analysis with ensemble and machine learning methods in multi-domain datasets

The first place to get ideas on all the activities considered to occur in everyday life was the comments on the websites. This is an area that deals with these interpretations in the natural language processing, which is a sub-branch of artificial intelligence. Sentiment analysis studies, which is a task of natural language processing are carried out to give people an idea and even guide them with such comments. In this study, sentiment analysis was implemented on public user feedback on websites in two different areas. TripAdvisor dataset includes positive or negative user comments about hotels. And Rotten Tomatoes dataset includes positive (fresh) or negative (rotten) user comments about films. Sentiments analysis on datasets have been carried out by using Word2Vec word embedding model, which learns the vector representations of each word containing the positive or negative meaning of the sentences, and the Term Frequency Inverse Document Frequency text representation model with four machine learning methods (Naïve Bayes-NB, Support Vector Machines-SVM, Logistic Regression-LR, K-Nearest Neighbour-kNN) and two ensemble learning methods (Stacking, Majority Voting-MV). Accuracy and F-measure is used as a performance metric experiments. According to the results, Ensemble learning methods have shown better results than single machine learning algorithms. Among the overall approaches, MV outperformed Stacking.

Method comparison of Naïve Bayes, Logistic Regression, and SVM for Analyzing Movie Reviews

A film can be categorized as a successful film based on the reviews given by the critics. The reviews can range from professional critics to public reviews from the general audience. Due to a large number of reviews and opinions on a film, this study aims to create a sentiment analysis model and compare the methods used to analyze datasets from a movie review. Sentiment Analysis is a method for studying and analyzing opinions, then classifying these opinions into several classes. This research will use the Naïve Bayes method, Logistic Regression, and Support Vector Machine (SVM) to analyze film review data. The film review dataset used is a collection of film reviews taken from the Rotten Tomatoes website and will be pre-processed before implementing the Naïve Bayes, Logistic Regression, and SVM methods. The SVM classifier with 80:20 data splitting has the best performance, with a result of 99.4% accuracy score and 93.5% F1 score.

Credibility Evaluation of User-generated Content using Novel Multinomial Classification Technique

Awareness about the features of the internet, easy access to data using mobile, and affordable data facilities have caused a lot of traffic on the internet. Digitization came with a lot of opportunities and challenges as well. One of the important advantages of digitization is paperless transactions, and transparency in payment, while data privacy, fake news, and cyber-attacks are the evolving challenges. The extensive use of social media networks and e-commerce websites has caused a lot of user-generated information, misinformation, and disinformation on the Internet. The quality of information depends upon various stages (of information) like generation of information, medium of propagation, and consumption of information. Content being user-generated, information needs a quality assessment before consumption. The loss of information is also necessary to be examined by applying the machine learning approach as the volume of content is extremely huge. This research work focuses on novel multinomial classification (based on multinoulli distribution) techniques to determine the quality of the information in the given content. To evaluate the information content a single algorithm with some processing is not sufficient and various approaches are necessary to evaluate the quality of content. We propose a novel approach to calculate the bias, for which the Machine Learning model will be fitted appropriately to classify the content correctly. As an empirical study, rotten tomatoes’ movie review data set is used to apply the classification techniques. The accuracy of the system is evaluated using the ROC curve, confusion matrix, and MAP.

The Politics of Colonization in Ryan Coogler’s Black Panther

In 2018 preceding the release of Black Panther, amid a sea of positively effusive reviews for Ryan Coogler’s film, a sudden onslaught of extremely negative reviews began to appear on the internet. Negative reviews for a film are not uncommon, yet these were so far out of the norm that they began to draw the concern of film aggregation sites such as Rotten Tomatoes. So perturbed was the site by what it was witnessing in the reviews of Black Panther that Rotten Tomatoes dispatched their security team to “closely monitor” the platform and began to block and delete reviews “as quickly as possible.” In 2018 Black Panther became the target of racists, determined to ruin the ratings of the first Marvel Comic Universe (MCU) film to feature a Black central character and mostly Black cast. This essay will investigate two critical aspects surrounding the release of Coogler’s Black Panther. The first is the nature of the negative reviews surrounding the release of the film in 2018. The second is an exploration of Coogler’s critique of colonization in Black Panther. Lastly, Coogler’s portrayal of the fictitious kingdom of Wakanda is analyzed in concert with Albert Memmi’s The Colonizer and the Colonized (1957, 1991), in order to shine a light on the anticolonial structure present in Black Panther.

AsU-OSum: Aspect-augmented unsupervised opinion summarization

Opinion summarization can facilitate user’s decision-making by mining the salient review information. However, due to the lack of sufficient annotated data, most of the early works are based on extractive methods, which restricts the performance of opinion summarization. In this work, we aim to improve the informativeness of opinion summarization to provide better guidance to users. We consider the setting with only reviews without corresponding summaries, and propose an aspect-augmented model for unsupervised abstractive opinion summarization, denoted as AsU-OSum. We first employ an aspect-based sentiment analysis system to extract opinion phrases from reviews. Then, we construct a heterogeneous graph consisting of reviews and opinion clusters as nodes, which is used to enhance the Transformer-based encoder–decoder framework. Furthermore, we design a novel cascaded attention mechanism to prompt the decoder to pay more attention to the aspects that are more likely to appear in summary. During training, we introduce a sentiment accuracy reward that further enhances the learning ability of our model. We conduct comprehensive experiments on the Yelp, Amazon, and Rotten Tomatoes datasets. Automatic evaluation results show that our model is competitive and performs better than the state-of-the-art (SOTA) models on some ROUGE metrics. Human evaluation results further verify that our model can generate more informative summaries and reduce redundancy.

Towards more trustworthy predictions: A hybrid evidential movie recommender system

Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.

Netflix Movie Recommendation System Using Collaborative Filtering With K-Means Clustering Method on Twitter

Nowadays, the development of technology is very rapid, so watching movies at home has become a means of entertainment. Netflix is one of the platforms for watching movies and provides various movie titles. However, because of the many movie titles, it makes it difficult for users to determine the movie they want to watch. The solution to this problem is to provide a recommendation system that can provide movie recommendations to watch. Collaborative filtering is a method that exists in the recommendation system by providing recommendations based on the ratings given by other users. Collaborative filtering is divided into two, namely based on items (item-based) and based on users (user-based). Twitter is a social media used to write posts called tweets. For this system, tweets serve as data that will be processed into ratings. This research was conducted using k-means clustering with collaborative filtering and collaborative filtering only. By using a dataset obtained from Twitter by crawling data and added with ratings from IMDb, Rotten Tomatoes, and Metacritic. Which resulted in a dataset with 35 users, 785 movie titles, and 6184 reviews. Then preprocessing the data with text processing, polarity, and labeling. And get the dataset that will be used for this experiment. The results of this research test show that k-means clustering with collaborative filtering gets the best results with the best prediction of 2.8466, getting an MAE value of 0.5029, and an RMSE value of 0.6354

Innovation of Environmentally Friendly Solid Electrolyte Biobattery Based on Carrageenan and Rotten Tomatoes

This research aims to create and evaluate the performance of environmentally friendly biobattery based on carrageenan and rotten tomatoes to reduce B3 waste. To make a biobattery based on carrageenan and rotten tomatoes, five ratios of carrageenan and rotten tomatoes mixtures were used with the composition of carrageenan values of 1, 2, 3, 4, and 5% for each type of battery. The parameters observed in this study were the potential difference, current strength, and stability of the biobattery. Carrageenan was added to prevent battery leakage and maintain battery stability. As a result, the biobattery has a potential difference value equivalent to a commercial battery, namely 1.5 V, but the current generated is still low. On the other hand, biobatteries can be applied to alarm clocks. The difference in carrageenan concentration has no significant effect on the value of potential difference, current strength, power, recharging ability, and application on wall clocks, as well as the stability of the biobattery. The experiment was carried out with the hypothesis that husk ash can absorb rotten tomatoes, preventing electrolyte leakage. However, the resulting biobattery is still leaking. After that, we modified it again by using electrolytes made from rotten tomatoes and coconut dregs, but the leakage was still the same. Therefore, the biobattery made from a combination of carrageenan and rotten tomatoes was assumed to keep the battery more stable and prevent leakage. This research was expected to contribute to the development of environmentally friendly batteries to reduce B3 waste, along with the increasing need for batteries in the era of industrial revolution 4.0.

Counting: How We Use Numbers to Decide What Matters

Counting: How We Use Numbers to Decide What Matters