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1 – 10 of over 1000
Article
Publication date: 29 October 2018

Shrawan Kumar Trivedi and Shubhamoy Dey

To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be…

Abstract

Purpose

To be sustainable and competitive in the current business environment, it is useful to understand users’ sentiment towards products and services. This critical task can be achieved via natural language processing and machine learning classifiers. This paper aims to propose a novel probabilistic committee selection classifier (PCC) to analyse and classify the sentiment polarities of movie reviews.

Design/methodology/approach

An Indian movie review corpus is assembled for this study. Another publicly available movie review polarity corpus is also involved with regard to validating the results. The greedy stepwise search method is used to extract the features/words of the reviews. The performance of the proposed classifier is measured using different metrics, such as F-measure, false positive rate, receiver operating characteristic (ROC) curve and training time. Further, the proposed classifier is compared with other popular machine-learning classifiers, such as Bayesian, Naïve Bayes, Decision Tree (J48), Support Vector Machine and Random Forest.

Findings

The results of this study show that the proposed classifier is good at predicting the positive or negative polarity of movie reviews. Its performance accuracy and the value of the ROC curve of the PCC is found to be the most suitable of all other classifiers tested in this study. This classifier is also found to be efficient at identifying positive sentiments of reviews, where it gives low false positive rates for both the Indian Movie Review and Review Polarity corpora used in this study. The training time of the proposed classifier is found to be slightly higher than that of Bayesian, Naïve Bayes and J48.

Research limitations/implications

Only movie review sentiments written in English are considered. In addition, the proposed committee selection classifier is prepared only using the committee of probabilistic classifiers; however, other classifier committees can also be built, tested and compared with the present experiment scenario.

Practical implications

In this paper, a novel probabilistic approach is proposed and used for classifying movie reviews, and is found to be highly effective in comparison with other state-of-the-art classifiers. This classifier may be tested for different applications and may provide new insights for developers and researchers.

Social implications

The proposed PCC may be used to classify different product reviews, and hence may be beneficial to organizations to justify users’ reviews about specific products or services. By using authentic positive and negative sentiments of users, the credibility of the specific product, service or event may be enhanced. PCC may also be applied to other applications, such as spam detection, blog mining, news mining and various other data-mining applications.

Originality/value

The constructed PCC is novel and was tested on Indian movie review data.

Article
Publication date: 25 October 2018

Shrawan Kumar Trivedi, Shubhamoy Dey and Anil Kumar

Sentiment analysis and opinion mining are emerging areas of research for analyzing Web data and capturing users’ sentiments. This research aims to present sentiment analysis of an…

Abstract

Purpose

Sentiment analysis and opinion mining are emerging areas of research for analyzing Web data and capturing users’ sentiments. This research aims to present sentiment analysis of an Indian movie review corpus using natural language processing and various machine learning classifiers.

Design/methodology/approach

In this paper, a comparative study between three machine learning classifiers (Bayesian, naïve Bayesian and support vector machine [SVM]) was performed. All the classifiers were trained on the words/features of the corpus extracted, using five different feature selection algorithms (Chi-square, info-gain, gain ratio, one-R and relief-F [RF] attributes), and a comparative study was performed between them. The classifiers and feature selection approaches were evaluated using different metrics (F-value, false-positive [FP] rate and training time).

Findings

The results of this study show that, for the maximum number of features, the RF feature selection approach was found to be the best, with better F-values, a low FP rate and less time needed to train the classifiers, whereas for the least number of features, one-R was better than RF. When the evaluation was performed for machine learning classifiers, SVM was found to be superior, although the Bayesian classifier was comparable with SVM.

Originality/value

This is a novel research where Indian review data were collected and then a classification model for sentiment polarity (positive/negative) was constructed.

Details

The Electronic Library, vol. 36 no. 4
Type: Research Article
ISSN: 0264-0473

Keywords

Article
Publication date: 3 February 2020

Nikola Nikolić, Olivera Grljević and Aleksandar Kovačević

Student recruitment and retention are important issues for all higher education institutions. Constant monitoring of student satisfaction levels is therefore crucial…

Abstract

Purpose

Student recruitment and retention are important issues for all higher education institutions. Constant monitoring of student satisfaction levels is therefore crucial. Traditionally, students voice their opinions through official surveys organized by the universities. In addition to that, nowadays, social media and review websites such as “Rate my professors” are rich sources of opinions that should not be ignored. Automated mining of students’ opinions can be realized via aspect-based sentiment analysis (ABSA). ABSA s is a sub-discipline of natural language processing (NLP) that focusses on the identification of sentiments (negative, neutral, positive) and aspects (sentiment targets) in a sentence. The purpose of this paper is to introduce a system for ABSA of free text reviews expressed in student opinion surveys in the Serbian language. Sentiment analysis was carried out at the finest level of text granularity – the level of sentence segment (phrase and clause).

Design/methodology/approach

The presented system relies on NLP techniques, machine learning models, rules and dictionaries. The corpora collected and annotated for system development and evaluation comprise students’ reviews of teaching staff at the Faculty of Technical Sciences, University of Novi Sad, Serbia, and a corpus of publicly available reviews from the Serbian equivalent of the “Rate my professors” website.

Findings

The research results indicate that positive sentiment can successfully be identified with the F-measure of 0.83, while negative sentiment can be detected with the F-measure of 0.94. While the F-measure for the aspect’s range is between 0.49 and 0.89, depending on their frequency in the corpus. Furthermore, the authors have concluded that the quality of ABSA depends on the source of the reviews (official students’ surveys vs review websites).

Practical implications

The system for ABSA presented in this paper could improve the quality of service provided by the Serbian higher education institutions through a more effective search and summary of students’ opinions. For example, a particular educational institution could very easily find out which aspects of their service the students are not satisfied with and to which aspects of their service more attention should be directed.

Originality/value

To the best of the authors’ knowledge, this is the first study of ABSA carried out at the level of sentence segment for the Serbian language. The methodology and findings presented in this paper provide a much-needed bases for further work on sentiment analysis for the Serbian language that is well under-resourced and under-researched in this area.

Book part
Publication date: 8 December 2023

Rajeev Kamineni and Ruth Rentschler

Despite almost 50% of the Indian population being women, there is a significant gap between the genders in movie production. Although there might be several reasons attributed to…

Abstract

Despite almost 50% of the Indian population being women, there is a significant gap between the genders in movie production. Although there might be several reasons attributed to the underrepresentation of women in the role of a movie entrepreneur, it is a fact that female movie entrepreneurs are few and far between. Most of the female movie producers in Indian movie industry tend to be spouses or children of leading male actors who have taken up the mantle to assist their husbands or fathers. This chapter, using interviews and life history analysis, examines reasons for low numbers of female entrepreneurs in the Indian movie industry, a domain that has largely been overlooked.

Book part
Publication date: 12 November 2018

Marissa Joanna Doshi

This study reports on a four-month ethnographic project conducted among young Catholic women in Mumbai, India. Here, the author examines how the media consumption of participants…

Abstract

This study reports on a four-month ethnographic project conducted among young Catholic women in Mumbai, India. Here, the author examines how the media consumption of participants is implicated in reconstituting Indian national identity. Because Hinduism is closely tied to conceptualizations of Indianness and because women continue to be marginalized in Indian society, Catholic women in India are viewed as second-class citizens or “not Indian enough” or “appropriately Indian” by virtue of their gender and religious affiliation. However, through media consumption that emphasizes hybridity, participants destabilize narrow definitions of Indian identity. Specifically, participants cultivate hybridity as central to an Indian identity that is viable in an increasingly global society. Within this formulation of hybridity, markers of their marginalization are reframed as markers of distinction. By centering hybridity in their media consumption, young, middle-class Catholic women (re)imagine their national identity in translocal cosmopolitan terms that subverts marginalization experienced by virtue of their religion and leverages privileges they enjoy by virtue of their middle-class status. Importantly, this version of Indian identity remains elitist in that it remains inaccessible to poor women, including poor women of minority groups.

Details

Media and Power in International Contexts: Perspectives on Agency and Identity
Type: Book
ISBN: 978-1-78769-455-2

Keywords

Article
Publication date: 1 November 2019

Shrawan Kumar Trivedi and Shubhamoy Dey

Email is a rapid and cheapest medium of sharing information, whereas unsolicited email (spam) is constant trouble in the email communication. The rapid growth of the spam creates…

Abstract

Purpose

Email is a rapid and cheapest medium of sharing information, whereas unsolicited email (spam) is constant trouble in the email communication. The rapid growth of the spam creates a necessity to build a reliable and robust spam classifier. This paper aims to presents a study of evolutionary classifiers (genetic algorithm [GA] and genetic programming [GP]) without/with the help of an ensemble of classifiers method. In this research, the classifiers ensemble has been developed with adaptive boosting technique.

Design/methodology/approach

Text mining methods are applied for classifying spam emails and legitimate emails. Two data sets (Enron and SpamAssassin) are taken to test the concerned classifiers. Initially, pre-processing is performed to extract the features/words from email files. Informative feature subset is selected from greedy stepwise feature subset search method. With the help of informative features, a comparative study is performed initially within the evolutionary classifiers and then with other popular machine learning classifiers (Bayesian, naive Bayes and support vector machine).

Findings

This study reveals the fact that evolutionary algorithms are promising in classification and prediction applications where genetic programing with adaptive boosting is turned out not only an accurate classifier but also a sensitive classifier. Results show that initially GA performs better than GP but after an ensemble of classifiers (a large number of iterations), GP overshoots GA with significantly higher accuracy. Amongst all classifiers, boosted GP turns out to be not only good regarding classification accuracy but also low false positive (FP) rates, which is considered to be the important criteria in email spam classification. Also, greedy stepwise feature search is found to be an effective method for feature selection in this application domain.

Research limitations/implications

The research implication of this research consists of the reduction in cost incurred because of spam/unsolicited bulk email. Email is a fundamental necessity to share information within a number of units of the organizations to be competitive with the business rivals. In addition, it is continually a hurdle for internet service providers to provide the best emailing services to their customers. Although, the organizations and the internet service providers are continuously adopting novel spam filtering approaches to reduce the number of unwanted emails, the desired effect could not be significantly seen because of the cost of installation, customizable ability and the threat of misclassification of important emails. This research deals with all the issues and challenges faced by internet service providers and organizations.

Practical implications

In this research, the proposed models have not only provided excellent performance accuracy, sensitivity with low FP rate, customizable capability but also worked on reducing the cost of spam. The same models may be used for other applications of text mining also such as sentiment analysis, blog mining, news mining or other text mining research.

Originality/value

A comparison between GP and GAs has been shown with/without ensemble in spam classification application domain.

Article
Publication date: 11 October 2011

Sonal Kureshi and Vandana Sood

The purpose of this paper is to understand the growing phenomenon of brand placements in the Indian movie industry. The study goes further to compare the incidence and the nature…

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Abstract

Purpose

The purpose of this paper is to understand the growing phenomenon of brand placements in the Indian movie industry. The study goes further to compare the incidence and the nature of brands placed within movies for the same time period.

Design/methodology/approach

A content analysis of 106 successful Bollywood movies between 1997 and 1999 was conducted and the incidence of brand placements within them and the execution style adopted were documented. Analysis of the brand appearances in 110 Hollywood movies was carried out and the volume of placements, kind of brands placed and the movie genre in which they were found was noted.

Findings

In‐film placements of entertainment and automobile brands were found to be highly prevalent in Indian movies. Showing the usage of the brand was the most common style of execution. The volume of in‐film placements in Hollywood movies was found to be far higher than that in Indian movies.

Research limitations/implications

This study being exploratory in nature has the inherent limitation of generalizability of the results.

Practical implications

This paper provides implications for marketing managers and movie producers employing this form of communication.

Originality/value

This study is one of the first to systematically record, analyse and compare the occurrence and the execution of brand placements in Indian movies in a non‐US context and compare and contrast the placement practices of these two movie industries.

Details

Journal of Indian Business Research, vol. 3 no. 4
Type: Research Article
ISSN: 1755-4195

Keywords

Article
Publication date: 28 June 2022

Vijaya Patil, Hema Date, Satish Kumar, Weng Marc Lim and Naveen Donthu

This study explores the making of box-office collection using the Indian film industry, Bollywood, as a case.

Abstract

Purpose

This study explores the making of box-office collection using the Indian film industry, Bollywood, as a case.

Design/methodology/approach

This study conducts in-depth interviews with cinematic experts in the Indian film industry and analyzes the interview transcripts using thematic analysis.

Findings

This study uncovers several noteworthy findings. First, films that drew both general (MASS audience) and niche (CLASS audience) viewers dominate the box office. Second, viewers prefer to see films that are based on true events, and their engagement will be deeper if the subject of the film resonates with them. Third, stakeholder share is variable and changes over time. Fourth, the marketing budget for a film is typically higher than its production budget, and it is determined by the producer's financial resources. Fifth, the dominance of big over small banner films motivates the latter to pursue online rather than cinematic releases. Finally, Internet access creates value and returns on investment through sales of satellite and musical rights, while strategic promotion and distribution reap maximum benefit for box-office collection.

Originality/value

Unlike past studies that rely on secondary data, this study uses primary qualitative data to explore the making of box-office collection. This study also focuses on an alternative film industry, Bollywood, as it is a vast context that remains underexplored.

Details

Marketing Intelligence & Planning, vol. 40 no. 8
Type: Research Article
ISSN: 0263-4503

Keywords

Article
Publication date: 30 October 2018

Shrawan Kumar Trivedi and Prabin Kumar Panigrahi

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy…

Abstract

Purpose

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost).

Design/methodology/approach

Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method.

Findings

Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails.

Research limitations/implications

This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study.

Practical implications

In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers.

Originality/value

A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

Details

Journal of Systems and Information Technology, vol. 20 no. 3
Type: Research Article
ISSN: 1328-7265

Keywords

Article
Publication date: 8 January 2018

Madhumita Nanda, Chinmay Pattnaik and Qiang (Steven) Lu

The purpose of this paper is to examine how movie studios develop an integrated social media strategy to achieve box office success. Departing from prior studies which focus on…

5841

Abstract

Purpose

The purpose of this paper is to examine how movie studios develop an integrated social media strategy to achieve box office success. Departing from prior studies which focus on single social media platforms, this study examines the role of integrated social media promotion strategy using multiple social media platforms on movie success in the Bollywood movie industry.

Design/methodology/approach

This study adopts an in-depth and comprehensive case study approach to examine the promotional strategies adopted through YouTube, Facebook and Twitter throughout the life cycle of the movie and its impact on the box office success of the movie.

Findings

The study provides three major findings. First, the social media promotional strategy was centred on developing appropriate content to match the unique characteristics of the social media platforms. While Facebook was utilised primarily to connect audiences through organising fun events, Twitter was used to retweet the positive word-of-mouth generated from the audiences. Second, emphasis on promotional strategy through social media platforms in the post-release stage of the movie was found to be equally important as the pre-release stage. Finally, the social media platforms were utilised to develop emotional connection with the audience by promoting the content through which the audience identified themselves with the main protagonist of the movie.

Originality/value

This study is among the very few studies which examines the role of integrative social media strategy on the box office success in the movie industry. This study emphasises the way firms can utilise the synergies across different social media platforms to achieve success in the movie industry.

Details

Management Decision, vol. 56 no. 1
Type: Research Article
ISSN: 0025-1747

Keywords

1 – 10 of over 1000