Thousands Of Movies Research Articles

Internet Movie Database Data Analysis Using Python

The film industry is highly competitive and dynamic, with thousands of movies released every year. To succeed in this industry, it's crucial for filmmakers, studios, and investors to make informed decisions. IMDB, a popular source for movie-related data, provides a wealth of information on movies, including details about genres, budgets, revenues, critical reception, and more. The problem statement aims to leverage this data to gain valuable insights and inform key decisions within the film industry.

Some Remarks on the Perso-Arabic Nominal Lexica in Bollywood Movie Song Lyrics

The Indian film industry releases thousands of movies every year, with those in the Hindi language forming a significant part of that number. These movies, and the songs used in them, have been the object of research within the scope of various scientific disciplines, including linguistics. The author of the present paper, however, believes that there are still numerous language phenomena to study within the lyrics of the Bollywood songs. The present text is dedicated to research of the Perso-Arabic lexica in these songs over the period of 80 years (1940–2020). It also analyses the frequency of the Perso-Arabic forms as opposed to native Indo-Aryan ones. Particular attention is paid to the forms of the highest frequency, especially the Persian word dil.

Content Based Movie Scene Retrieval using Spatio Temporal Features

Thousands of movies along with TV shows, documentaries are being produced each year around the world with different genres and languages. Making a movie scene impactful as well as original is challenging task for the director. On the other hand, users demands to retrieve similar scenes from their queries is also challenging task as there is no proper maintenance of database of movie scene videos with proper semantic tags associated with it. So to fulfill the requirement of these two (but not the least) application areas there is a need of content based retrieval system for movie scenes. Content based video retrieval is a problem of retrieving most similar videos to a given query video by analyzing the visual contents of videos. Traditional video level features based on key frame level hand engineered features which does not exploit rich dynamics present in the video. In this paper we propose a Content based Movie Scene Retrieval (CB-MSR) framework using spatio-temporal features learned by deep learning. Specifically deep CNN along with LSTM is deploy to learn spatio-temporal representations of video. On the basis of these learned features similar movie scenes can be retrieve from the collection of movies. Hollywood2 dataset is used to test the proposed system. Two types of features: spatial and spatio-temporal features are used to evaluate the proposed framework.

The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books.

Our analysis of thousands of movies and books reveals how these cultural products weave stereotypical gender roles into morality tales and perpetuate gender inequality through storytelling. Using the word embedding techniques, we reveal the constructed emotional dependency of female characters on male characters in stories. We call this narrative structure "Cinderella complex", which assumes that women depend on men in the pursuit of a happy, fulfilling life. Our analysis covers a substantial portion of narratives that shape the modern collective memory, including 7,226 books, 6,087 movie synopses, and 1,109 movie scripts. The "Cinderella complex" is observed to exist widely across periods and contexts, reminding how gender stereotypes are deeply rooted in our society. Our analysis of the words surrounding female and male characters shows that the lives of males are adventure-oriented, whereas the lives of females are romantic-relationship oriented. Finally, we demonstrate the social endorsement of gender stereotypes by showing that gender-stereotypical movies are voted more frequently and rated higher.

Comparative Analysis for Prediction of Success of Bollywood Movie

In terms of Film Production, Indian Cinema is one of the largest Film Industry where more than thousands of movies released each year. The large part of Indian Cinema is Bollywood which is multi-million dollar industry. So, this Paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Data Mining techniques (classification and prediction) will be applied. Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems which come in business and helps to predict the forthcoming trends. The main difference between classification and prediction is that the classification predicts the definitive categorical labels and prediction techniques predicts the continuous values. Every big business is engaging a large amount of data so after analyzing them the company can define different strategies to achieve business aims and profits. For these, there are various techniques available with different data visualization tools such as Microsoft Power BI, Tableau, Logi Analytics, Orange, etc. To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. All the techniques related to classification and Prediction such as Decision Tree, Naive Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. These Algorithms would be applied in Orange. The orange tool is Open-Source software which incorporates Data Mining, Machine Learning and Data Visualization which makes our work easy and everything in one place. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, Evaluate. This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the Best possible model for predicting the movie Success. This Prediction can help Production Houses for Advertisement Propaganda and they can plan their costs and by assuring these factors they can make the movie more profitable. By this, they can also decide as which is the best time to release the movie according to the predicted success rate defined by mode to gain higher benefits.

Access Nollywood

Access Nollywood

How to Measure the Power of Actors and Film Directors?

Star power is considered to be one of the key drivers of motion picture success. The power of movie directors is another possible, yet controversial, determinant of movie success. However, measuring the power of actors and directors objectively is not an easy task. In this article, we give a detailed description of a fast method which is based on previous box office performance of movies with particular actors and directors. Our approach allows calculating movie star ratings of thousands of movies using a spreadsheet within several minutes. It is also shown empirically that the resulting measures are significant determinants of box office sales. The percentage contribution of each determinant is estimated using the Shapley value decomposition.

Factors of Gratification Contributing in Continuance Intention to Watch Movies on YouTube

YouTube has leveraged their native sharing properties and making all content digitally available to everyone under the Creative Commons license (ford.digitalsnippets.com, 2010). Thousands of movies have been uploaded to YouTube for free viewings and has affected broadcasting and cinema industry greatly (Brody, 2012). This paper is to share sets of predicted elements in YouTube said to be responsible in gratifying its multi-purposes viewers and instill continuance intentions to use the UGC for movie watching among Malaysians in Klang Valley. This was done by integrating the uses and gratification framework with several disconfirmations of motivations (content, social, process, and technology).

Exploring an opinion network for taste prediction: An empirical study

We develop a simple statistical method to find affinity relations in a large opinion network which is represented by a very sparse matrix. These relations allow us to predict missing matrix elements. We test our method on the Eachmovie data of thousands of movies and viewers. We found that significant prediction precision can be achieved and it is rather stable. There is an intrinsic limit to further improve the prediction precision by collecting more data, implying perfect prediction can never obtain via statistical means.