Search results

Sentiment analysis is the subfield of data mining, which is profusely used for studying the opinions of the users by analyzing their suggestions on the Web platform. It plays an important role in the daily decision-making process, and every decision has a great impact on daily life. Various techniques including machine learning algorithms have been proposed for sentiment analysis, but still, they are inefficient for extracting the sentiment features from the given text. Although the improvement in sentiment analysis approaches, there are several problems, which make the analysis inefficient and inaccurate. This paper aims to develop the sentiment analysis scheme on movie reviews by proposing a novel classifier.

For the analysis, the movie reviews are collected and subjected to pre-processing. From the pre-processed review, a total of nine sentiment related features are extracted and provided to the proposed exponential-salp swarm algorithm based actor-critic neural network (ESSA-ACNN) classifier for the sentiment classification. The ESSA algorithm is developed by integrating the exponentially weighted moving average (EWMA) and SSA for selecting the optimal weight of ACNN. Finally, the proposed classifier classifies the reviews into positive or negative class.

The performance of the ESSA-ACNN classifier is analyzed by considering the reviews present in the movie review database. From, the simulation results, it is evident that the proposed ESSA-ACNN classifier has improved performance than the existing works by having the performance of 0.7417, 0.8807 and 0.8119, for sensitivity, specificity and accuracy, respectively.

The proposed classifier can be applicable for real-world problems, such as business, political activities and so on.

The purpose of this study is to detect these reviews’ complex emotions, visualize and analyze them. Movie reviewers’ moviescores and reviews can be analyzed with respect to their emotion content, aggregated and projected onto a movie, resulting in an emotion map for a movie. It is then possible for a moviegoer to choose a movie, not only on the basis of movie scores and reviews, but also on the basis of aggregated emotional outcome of a movie as reflected by its emotion map displaying certain emotion map patterns desirable for the moviegoer.

The authors use the hourglass of emotion model to find the emotional scores of words of a review, then they use singular value decomposition to reduce the data dimension into singular scores. Once, they have the emotional scores of reviews, the authors cluster them by using k-means algorithm to find similar emotional levels of movies. Finally, the authors use heat maps to visualize four dimensions in a figure.

The authors are able to find the emotional levels of movie reviews, represent them in single scores and visualize them. The authors look the similarities and dissimilarities of movies based on their genre, ranking and emotional statuses. They also find the closest emotion levels of movies to a given movie.

The authors detect complex emotions from the text and simply visualize them.

In recent years, e-commerce (EC) sites dealing in various goods and services have increased along with internet popularity. Now, very few EC recommendation systems present a concrete reason for their recommendations. Therefore, because user preferences strongly influence outcomes, evaluation and selection are difficult for items, such as books, movies and luxury goods. The purpose of this paper is evoking interest by showing the review as a reason for a user’s decision-making factor. This paper aims to presents the development and introduction of a recommendation system that presents a review adapted to user preference.

The system presents a review to the user, which indicates the reason for matching the item contents and user preferences. Thereby, this system enables the creation of personalized reasons for recommendations.

Recommendation sentences conforming to user preferences are effective for item selection. Even with a simple method, in this paper, it was possible to present a review which is an item selection factor sufficient for the user.

This system can show a recommendation sentence that conforms to a user’s preferences merely from a user profile with the tag data of a product. This paper dealt in movies, but it can easily be applied even for other items.

Computer‐based video facilitates the creation of ‘movies’ recording actions on a computer screen complete with a voice‐over by the tutor. This paper describes the application of computer‐based video technology for point of need instruction on database searching. The Lotus ScreenCam software was used, being both inexpensive and readily available as part of the Lotus SmartSuite bundle. Initially, eight short movie clips were created, covering the techniques for searching PsycLIT on CD‐ROM and the ISI citation indexes via the BIDS gateway. The movie clips were made available on library PCs where students search these databases. The paper first examines educational theory to identify the role of computer‐based video within the educational framework. The movies created at Cardiff University are then described and the issues in design and implementation discussed. Finally, the effectiveness of this method of database searching instruction is explored and compared with more traditional point‐of‐need instruction techniques such as the handout, computer‐based tutorials and staff assistance.

The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India.

This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average.

In this study the authors found results of movie performance at the box office, which had been based on fuzzy interface system algorithm for prediction. The fuzzy interface system contains two factors, namely, sentiment score and actor rating to get the accurate result. By calculation of opening weekend collection, the authors found that that the predicted values were approximately same as the actual values. For the movie Singham Returns over method of prediction gave a box office collection as 84 crores and the actual collection turned out to be 88 crores.

The current study suffers from the limitation of not having enough computing resources to crawl the data. For predicting box office collection, there is no correct availability of ticket price information, total number of seats per screen and total number of shows per day on all screens. In the future work the authors can add several other inputs like budget of movie, Central Board of Film Certification rating, movie genre, target audience that will improve the accuracy and quality of the prediction.

The authors used different factors for predicting box office movie performance which had not been used in previous literature. This work is valuable for promoting of product and services of the firms.

This paper aims to research as to how Twitter is influential as an electronic word-of-mouth (e-WOM) communication tool and thereby affecting movie market. In present days, social media is playing an important role in connecting people around the globe. The technology has provided a platform in the social media space for people to share their experiences through text, photos and videos. Twitter is one such online social networking media that enables its users to send and read text-based messages of up to 140 characters, known as “tweets”. Twitter has nearly 200 million users and billions of such tweets are generated by users every other day. Social media micro-blogging broadcasting networks such as Twitter are transforming the way e-WOM is disseminated and consumed in the digital world. Twitter social behaviour for the Hollywood movies has been assessed across seven countries to validate the two basic blocks of the honeycomb model – sharing and conversation. Twitter behaviour was studied for 27 movies in 22 different cities of seven countries and for six genres with a total tweets of 9.28 million. The difference of Twitter social media behaviour was compared across countries, and “sharing” and “conversation” as two building blocks of the honeycomb model were studied. t-Test results revealed that the behaviour is different across countries and across genres.

The objective of the paper is to analyse Twitter messages on an entertainment product (movies) across different regions of the world. Hollywood movies are released across different parts of the world, and Twitter users are also in different parts of the world. The objective is to hence validate “conversation” and “sharing” building blocks of the honeycomb model. The research is confined to analysing Twitter data related to a few Hollywood movies. The tweets were collected across nine different cities spanning four different countries where English language is prominent. To understand the Twitter social media behaviour, a crawler application using Python and Java was developed to collect tweets of Hollywood movies from the Twitter database. The application has incorporated Twitter application programming interfaces (APIs) to access the Twitter database to extract tweets according to movies search queries across different parts of the world. The searching, collecting and analysing of the tweets is a rather challenging task because of various reasons. The tweets are stored in a Twitter corpus and can be accessed by the public using APIs. To understand whether tweets vary from one country to another, the analysis of variance test was conducted. To assess whether Twitter behaviour is different, and to compare the behaviour across countries, t-tests were conducted taking two countries at a time. The comparisons were made across all the six genres. In this way, an attempt was made to obtain a microscopic view of the Twitter behaviour for each of the seven countries and the six genres.

The findings show that the people use social media across the world. Nearly 9.28 million tweets were from seven countries, namely, USA, UK, Canada, South Africa, Australia, India and New Zealand for 27 Hollywood movies. This is indicative of the fact that today, people are exchanging information across different countries, that people are conversing about a product on social media and people are sharing information about a product on social media and, thus, proving the hypothesis. Further, the results indicate that the users in USA, Canada and UK, tweet more than the other countries, USA and UK being the highest in tweets followed by the Canada. On the other hand, the number of tweets in Australia, India and South Africa are low with New Zealand being the lowest of all the countries. This indicates that different countries’ users have different social media behaviour. Some countries use social media to communicate about their experience more than in some other country. However, consumers from all over the world are using Twitter to express their views openly and freely.

This research is useful to scholars and enterprises to understand opinions on Twitter social media and predict their impact. The study can be extended to any products which can lead to better customer relationship management. Companies can use the Internet and social media to promote and get feedback on their products and services across different parts of the world. Governments can inform the public about their new policies, benefits of governmental programmes to people and ways to improve the Internet reach to more people and also for creating awareness about health, hygiene, natural calamities and safety.

The purpose of this study is to suggest suitable movies for children among the various multimedia selections available these days. Multimedia have a significant impact on the social and psychological development of children who are often explored to inappropriate materials, including movies that are either accessible online or through other multimedia channels. Even though not all movies are bad, there are negative effects of offensive languages, violence and sexuality as exhibited in movies. Parents and guidance of children need all the help they can get to promote the healthy use of movies these days.

To offer parents appropriate movies of interest to their youths, the authors have developed MovRec, a personalized movie recommender for children, which is designed to provide educational and suitable entertaining opportunities for children. MovRec determines the appealingness of a movie for a particular user based on its children-appropriate score computed by using the backpropagation model, pre-defined category using latent Dirichlet allocation, its predicted rating using matrix factorization and sentiments based on its users’ reviews, which along with its like/dislike count and genres, yield the features considered by MovRec. MovRec combines these features by using the CombMNZ model to rank and recommend movies.

The performance evaluation of MovRec clearly demonstrates its effectiveness and its recommended movies are highly regarded by its users.

Unlike Amazon and other online movie recommendation systems, such as Common Sense Media, Internet Movie Database and TasteKid, MovRec is unique, as to the best of the authors’ knowledge, MovRec is the first personalized children movie recommender.

The purpose of this paper is automatic classification of TV series reviews based on generic categories.

What the authors mainly applied is using surrogate instead of specific roles or actors’ name in reviews to make reviews more generic. Besides, feature selection techniques and different kinds of classifiers are incorporated.

With roles’ and actors’ names replaced by generic tags, the experimental result showed that it can generalize well to agnostic TV series as compared with reviews keeping the original names.

The model presented in this paper must be built on top of an already existed knowledge base like Baidu Encyclopedia. Such database takes lots of work.

Like in digital information supply chain, if reviews are part of the information to be transported or exchanged, then the model presented in this paper can help automatically identify individual review according to different requirements and help the information sharing.

One originality is that the authors proposed the surrogate-based approach to make reviews more generic. Besides, they also built a review data set of hot Chinese TV series, which includes eight generic category labels for each review.

We examine the role of financial and nonfinancial performance measures in managing revenues derived from life cycles of a type of intellectual property products − motion pictures.

Our study focuses on the first two markets in which audiences can watch a motion picture – the upstream theatrical market and the downstream home video market. We combine data collected from numerous public and proprietary sources and form a final sample of 654 motion pictures. Then we perform regression analysis on the data.

First, three measures of a movie’s performance in the theatrical market, opening box office revenue, peak rank, and weeks at the peak rank, have positive effects on subsequent revenues in the home video market. Second, the same set of performance measures also predicts the motion picture’s life span in the theatrical market. Third, when the actual life span of a motion picture in the theatrical market deviates from its predicted value, the total return on investment in the motion picture decreases.

We do not have data on other downstream markets related to motion pictures, such as pay-per-view and online video streaming.

This study suggests that the public and proprietary data can be used to inform managerial decisions regarding intellectual property product life cycles.

This is the first accounting study that directly examines life cycle revenues of intellectual property products. We also extend literature on revenue driver and revenue management research to the product level.