Relevant bibliographies by topics / Movie Review

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Movie Review'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 June 2025

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Journal articles on the topic "Movie Review"

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Wilson, Kris. "Movie Review." Science Communication 26, no. 2 (2004): 227–29. http://dx.doi.org/10.1177/1075547004271520.

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Woolfolk, Tara N. "MOVIE REVIEW." Journal of Feminist Family Therapy 19, no. 3 (2007): 83–88. http://dx.doi.org/10.1300/j086v19n03_07.

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Bennett, Katherine J., and Thomas W. Satre. "Movie Review." Prison Journal 80, no. 2 (2000): 228–32. http://dx.doi.org/10.1177/0032885500080002008.

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Dutton, Thomas A. "Movie Review." Journal of Architectural Education 43, no. 3 (1990): 57–59. http://dx.doi.org/10.1080/10464883.1990.10758579.

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Madhavan, Lakshmi. "Movie Review." Institutionalised Children Explorations and Beyond 7, no. 1 (2020): 94–96. http://dx.doi.org/10.1177/2349300320908734.

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Madhavan, Lakshmi. "Movie Review." Institutionalised Children Explorations and Beyond 7, no. 2 (2020): 233–35. http://dx.doi.org/10.1177/2349300320934681.

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Senjo, Scott. "Movie review." Contemporary Justice Review 5, no. 4 (2002): 393–94. http://dx.doi.org/10.1080/10282580214780.

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Hu, Ya-Han, Wen-Ming Shiau, Sheng-Pao Shih, and Cho-Ju Chen. "Considering online consumer reviews to predict movie box-office performance between the years 2009 and 2014 in the US." Electronic Library 36, no. 6 (2018): 1010–26. http://dx.doi.org/10.1108/el-02-2018-0040.

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Purpose The purpose of this paper is to combine basic movie information factors, external factors and review factors, to predict box-office performance and identify the most crucial factor of influence for box-office performance. Design/methodology/approach Five movie genres and first-week movie reviews found on IMDb were collected. The movie reviews were quantified using sentiment analysis tools SentiStrength and Stanford CoreNLP, in which quantified data were combined with basic movie information and external environment factors to predict movie box-office performance. A movie box-office performance prediction model was then developed using data mining (DM) technologies with M5 model trees (M5P), linear regression (LR) and support vector regression (SVR), after which movie box-office performance predictions were made. Findings The results of this paper showed that the inclusion of movie reviews generated more accurate prediction results. Concerning movie review-related factors, the one that exhibited the greatest effect on box-office performance was the number of movie reviews made, whereas movie review content only displayed an effect on box-office performance for specific movie genres. Research limitations/implications Because this paper collected movie data from the IMDb, the data were limited and primarily consisted of movies released in the USA; data pertaining to less popular movies or those released outside of the USA were, thus, insufficient. Practical implications This paper helps to verify whether the consideration of the features extracted from movie reviews can improve the performance of movie box-office. Originality/value Through various DM technologies, this paper shows that movie reviews enhanced the accuracy of box-office performance predictions and the content of movie reviews has an effect on box-office performance.
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Lee, Sangjae, and Joon Yeon Choeh. "Movie Production Efficiency Moderating between Online Word-of-Mouth and Subsequent Box Office Revenue." Sustainability 12, no. 16 (2020): 6602. http://dx.doi.org/10.3390/su12166602.

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The studies are almost nonexistent regarding production efficiency of movies which is determined based on the relationship between movie resources powers (powers of actors, directors, distributors, and production companies) and box office. Our study attempts to examine how efficiency moderates the relationship between eWOM (online word-of-mouth) and revenue, and to show the difference in prediction performance between efficient and inefficient movies. Using data envelopment analysis to suggest efficiency of movies, movie efficiency negatively moderates the effects of review depth and volume on subsequent box office revenue compensating negative effects of smaller box office in previous period while efficiency exert a positive moderating effect on the influences of review rating and the number of positive reviews on revenue. This shows that review depth and volume are affected by the slack of movie resources powers for inefficient movies, and high rating and positive response for efficient movies to affect revenue. The results of decision trees, k-nearest-neighbors, and linear regression analysis based on ensemble methods using eWOM or movie variables indicate that the movies with the inefficient movie resources powers are providing greater prediction performance than movies with efficient movie resources powers. This show that diverse variation in the efficiency of movie resources powers contributes to prediction performance.
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Sturtevant, William C. "Movie Review: Pocahontas." AnthroNotes : National Museum of Natural History bulletin for teachers 17, no. 3 (2014): 7. http://dx.doi.org/10.5479/10088/22347.

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Dissertations / Theses on the topic "Movie Review"

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Wrenn, Alex. "Differences in Seasonality Based on Movie Quality." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2029.

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In recent years, the entertainment industry has begun to announce the release dates of many of their movies years in advance. This leads one to believe that movie studios are not taking into account the quality of a movie when a studio decides its release date. This paper will be an analysis in whether there is a difference in seasonality between different qualities of movies. If a studio announces the release date before filming even begins, it is clear that they do not know, and therefore cannot properly consider, the quality of the movie when they make its release date public. I will use films that make over a million dollars at the box office from 2000-2016 to examine the seasonality of good, average, and bad movies. My models will control for variables that were found to be significant in previous research. These include budget, MPAA rating, genre, and Oscar nominations. I will prove that there is a difference in seasonality between all three of these qualities groups. This will show that the Hollywood is now dismissing a key component in the difficult decision process that is movie release dates.
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El, Aouad Sara. "Personalized, Aspect-based Summarization of Movie Reviews." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS019.pdf.

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Les sites web de critiques en ligne aident les utilisateurs à décider quoi acheter ou quels hôtels choisir. Ces plateformes permettent aux utilisateurs d’exprimer leurs opinions à l’aide d’évaluations numériques et de commentaires textuels. Les notes numériques donnent une idée approximative du service. D'autre part, les commentaires textuels donnent des détails complets, ce qui est fastidieux à lire. Dans cette thèse, nous développons de nouvelles méthodes et algorithmes pour générer des résumés personnalisés de critiques de films, basés sur les aspects, pour un utilisateur donné. Le premier problème que nous abordons consiste à extraire un ensemble de mots liés à un aspect des critiques de films. Notre évaluation montre que notre méthode est capable d'extraire même des termes impopulaires qui représentent un aspect, tels que des termes composés ou des abréviations. Nous étudions ensuite le problème de l'annotation des phrases avec des aspects et proposons une nouvelle méthode qui annote les phrases en se basant sur une similitude entre la signature d'aspect et les termes de la phrase. Le troisième problème que nous abordons est la génération de résumés personnalisés, basés sur les aspects. Nous proposons un algorithme d'optimisation pour maximiser la couverture des aspects qui intéressent l'utilisateur et la représentativité des phrases dans le résumé sous réserve de contraintes de longueur et de similarité. Enfin, nous réalisons trois études d’utilisateur qui montrent que l’approche que nous proposons est plus performante que la méthode de pointe en matière de génération de résumés
Online reviewing websites help users decide what to buy or places to go. These platforms allow users to express their opinions using numerical ratings as well as textual comments. The numerical ratings give a coarse idea of the service. On the other hand, textual comments give full details which is tedious for users to read. In this dissertation, we develop novel methods and algorithms to generate personalized, aspect-based summaries of movie reviews for a given user. The first problem we tackle is extracting a set of related words to an aspect from movie reviews. Our evaluation shows that our method is able to extract even unpopular terms that represent an aspect, such as compound terms or abbreviations, as opposed to the methods from the related work. We then study the problem of annotating sentences with aspects, and propose a new method that annotates sentences based on a similarity between the aspect signature and the terms in the sentence. The third problem we tackle is the generation of personalized, aspect-based summaries. We propose an optimization algorithm to maximize the coverage of the aspects the user is interested in and the representativeness of sentences in the summary subject to a length and similarity constraints. Finally, we perform three user studies that show that the approach we propose outperforms the state of art method for generating summaries
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Zhang, Jun. "Sentiment analysis of movie reviews in Chinese." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412670.

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Sentiment analysis aims at figuring out the opinions of the users towards a certain service or product. In this research, the aim is at classifying the sentiments of users based on the comments they have posed on Douban movie website. In this thesis, I try two different ways to classify the sentiments: with the first one classifying comments into five classes of ratings from 1 to 5, and with the second one classifying comments into three classes of ratings: negative, neutral and positive. For the latter, the ratings of 1 and 2 are grouped as negative, the ratings of 3 neutral and the ratings of 4 and 5 positive. First, Term Frequency Inverse Document Frequency (TF-IDF) is used as the feature extraction technique for machine learning algorithms. Chi Square and Mutual Information are used for feature selection. The selected features are fed into different machine learning methods: Logistic Regression, Linear SVC, SGD classifier and Multinomial Naive Bayes. The performance of models with feature selection will be compared with the performance of models without feature selection for 5-class classification as well as 3-class classification. Also, fastText and Skip-Gram are used as embedding methods for deep learning algorithms LSTM and BILSTM. FastText will also be used for both embedding as well as being a classifier. The aim is to compare different machine learning and deep learning algorithms using different vectorization methods to see which model performs the best regarding both 5-class and 3-class classification. The two classification strategies will be compared with each other in terms of error analysis. The aim is to figure out the similarities and differences of misclassifications made by two different classification strategies.
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Luce, Maurício Seibel. "O ensino de língua estrangeira por tarefas : um projeto com críticas de filmes." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/21488.

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Um dos principais objetivos da pesquisa em aquisição da linguagem tem sido a compreensão da forma como os processos de aprendizado ou aquisição ocorrem, assim como a elaboração de alternativas metodológicas que consigam promover a comunicação autêntica em sala de aula, habilitando os aprendizes a participar das mais diversas situações comunicativas encontradas no cotidiano. Dentre os enfoques comunicativos, o ensino de línguas por tarefas tem sido fortemente aceito como sendo uma abordagem que permite o desenvolvimento de uma desejável habilidade comunicativa, através da prática autêntica e do foco nos significados, e que, concomitantemente, abre espaço para a ocorrência de momentos ou etapas durante a tarefa em que a atenção do aluno seja temporariamente dirigida a aspectos formais da língua-alvo, sem que haja uma inversão de prioridades no aprendizado, isto é, sem que o foco principal das tarefas seja preterido em relação ao foco excessivo em aspectos estruturais. A presente pesquisa investigou um projeto de elaboração de um guia de críticas de filmes. O projeto ocorreu em um curso livre de idiomas, no qual 22 alunos adultos de inglês como língua estrangeira foram divididos em três grupos e realizaram atividades que visavam a aumentar sua atenção sobre elementos relevantes ao gênero textual crítica de filme, assim como sobre aspectos gramaticais e lexicais, durante os encontros que ocorreram em meio à produção escrita de 3 críticas por cada participante. Os procedimentos adotados visaram a verificar de que maneira os aprendizes obtiveram ganhos de produção escrita no que se refere aos elementos de gênero e aos aspectos gramaticais. A análise dos dados obtidos demonstrou que as atividades de aumento de atenção e consciência trouxeram resultados bastante positivos. Além disso, o projeto demonstrou que os dois enfoques metodológicos tomados como base para os procedimentos adotados – o Ensino de Línguas por Tarefas e a proposta de Sequências Didáticas com gêneros textuais - são bastante compatíveis e que podem ser muito produtivos para aplicações futuras no ensino de língua estrangeira.
One of the main goals of the research in language acquisition has been to understand in which ways the learning or acquisition processes occur, and to propose methodological alternatives which are able to promote authentic communication in classroom, enabling learners to take part in a range of communicative situations found in daily life. Among the communicative approaches, the task based-language teaching has strongly been regarded as an option that permits both the development of desirable communicative skills, through authentic practice and a focus on meaning, and which also provides room for moments or steps during the task when the learner’s attention be temporarily focused on formal aspects of the target language, without any kind of inversion on the learning priorities, that is, keeping the main focus on the tasks, instead of an excessively strong focus on structural aspects. The present research investigated a project involving the production of a movie review guide book. The project took place in a free language course, where 22 adult participants, learners of English as a foreign language were split into three groups, and were assigned activities that intended to enhance their attention to both text genre-relevant elements and to grammar and lexical aspects during the meetings which happened in midst of the production of 3 reviews by each participant. The chosen procedures sought to investigate in which ways learners had gains in their writing production in relation to those elements and aspects. The data analysis shows that the attention and consciousness-raising activities brought about significantly positive results. In addition, the project has demonstrated that both methodological approaches used to design the task sequence adopted in the research – the task based-language teaching and the use of text genres in didactic sequences – are quite compatible and that they can be very productive in similar future applications in foreign language teaching.
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Kposowa, Kaitor. "The Financial Success of Franchise Film Sequels: An Exploration of the Relationship of Budget, Personnel Factors, and Reviews with Sequel Return on Investment." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1429187596.

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Sarika, Pawan Kumar. "Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213.

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Today, we are living in a data-driven world. Due to a surge in data generation, there is a need for efficient and accurate techniques to analyze data. One such kind of data which is needed to be analyzed are text reviews given for movies. Rather than classifying the reviews as positive or negative, we will classify the sentiment of the reviews on the scale of one to ten. In doing so, we will compare two recurrent neural network algorithms Long short term memory(LSTM) and Gated recurrent unit(GRU). The main objective of this study is to compare the accuracies of LSTM and GRU models. For training models, we collected data from two different sources. For filtering data, we used porter stemming and stop words. We coupled LSTM and GRU with the convolutional neural networks to increase the performance. After conducting experiments, we have observed that LSTM performed better in predicting border values. Whereas, GRU predicted every class equally. Overall GRU was able to predict multiclass text data of movie reviews slightly better than LSTM. GRU was computationally expansive when compared to LSTM.
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Tsang, Kwong-ping Loretta. "Offshore office : a strategic move : a post-implementation review of Cathay Pacific Airways Sydney Data Centre move /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18836732.

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Haddi, Emma. "Sentiment analysis : text, pre-processing, reader views and cross domains." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/11196.

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Sentiment analysis has emerged as a field that has attracted a significant amount of attention since it has a wide variety of applications that could benefit from its results, such as news analytics, marketing, question answering, knowledge management and so on. This area, however, is still early in its development where urgent improvements are required on many issues, particularly on the performance of sentiment classification. In this thesis, three key challenging issues affecting sentiment classification are outlined and innovative ways of addressing these issues are presented. First, text pre-processing has been found crucial on the sentiment classification performance. Consequently, a combination of several existing preprocessing methods is proposed for the sentiment classification process. Second, text properties of financial news are utilised to build models to predict sentiment. Two different models are proposed, one that uses financial events to predict financial news sentiment, and the other uses a new interesting perspective that considers the opinion reader view, as opposed to the classic approach that examines the opinion holder view. A new method to capture the reader sentiment is suggested. Third, one characteristic of financial news is that it stretches over a number of domains, and it is very challenging to infer sentiment between different domains. Various approaches for cross-domain sentiment analysis have been proposed and critically evaluated.
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Tsang, Kwong-ping Loretta, and 曾廣萍. "Offshore office: a strategic move : a post-implementation review of Cathay Pacific Airways Sydney Data Centremove." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B3126833X.

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Sommar, Fredrik, and Milosz Wielondek. "Combining Lexicon- and Learning-based Approaches for Improved Performance and Convenience in Sentiment Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166430.

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Sentiment classification is the process of categorizing data into categories based on its polarity with a wide array of applications across several industries. This report examines a combination of two prominent approaches to sentiment classification using a lexicon of weighted words and machine learning respectively. These approaches are compared with the combined hybrid approach in order to give an account of their relative strengths and weaknesses. When run on a set of IMDb movie reviews the results indicate that the hybrid model performs better than the lexicon-based approach, in turn being outperformed by the learning-based approach. However, the gain in convenience brought on by eliminating the need for training data makes the hybrid model an appealing alternative to the other approaches with a slight trade-off in performance.
Att klassificera text i kategorier baserat på känslan de uttrycker är ett aktuellt område idag och kan tillämpas inom många industrier. Rapporten undersöker en kombination av de två framstående tillvägagångssätten till denna typ av klassificering baserade på ett lexikon med definerade ordvikter respektive maskininlärning. Denna hybridlösning jämförs mot de två andra tillvägagångssätten för att framlägga deras relativa styrkor och svagheter. På ett dataset med filmrecensioner från IMDb får maskininlärningsklassificeraren bäst resultat, följt av hybridlösningen och sist den lexikonbaserade lösningen. Trots det kan hybridlösningen vara att föredra i situationer där det är ogenomförbart eller oskäligt att förbereda träningsdata för maskininlärningsklassificeraren, dock med ett visst avkall på prestanda.
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Books on the topic "Movie Review"

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Cooper, Stuart. Regulation - the movie: A semiotic study of the periodic review of UK regulated industry. Aston Business School, Aston University, 1998.

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Ebert, Roger. Your Movie Sucks. Andrews McMeel Publishing, 2007.

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Ebert, Roger. Your movie sucks. Andrews McMeel Publishing, LLC, 2007.

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Nebbadoon's movie reviews. Nebbadoon Press, 1997.

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Sumner, Don. Horror movie freak. Krause Publications, 2010.

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Sumner, Don. Horror movie freak. Krause Publications, 2010.

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Martin, Mick. Video movie guide, 1986. Edited by Porter Marsha and Remitz Ed ed. Ballantine Books, 1986.

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(Firm), Zagat Survey. Zagat 2008 movie guide. Zagat Survey, 2007.

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(Firm), Zagat Survey. Zagat 2007 movie guide. Zagat Survey, 2006.

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Nelson, Michael J. Mike Nelson's movie megacheese. HarperEntertainment, 2000.

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Book chapters on the topic "Movie Review"

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Haughton, Dominique, Mark-David McLaughlin, Kevin Mentzer, and Changan Zhang. "Can We Predict Oscars from Twitter and Movie Review Data?" In Movie Analytics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09426-7_6.

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Jain, Nikita, Achuth Kandikuntta, Deepak Jannarapu, Nallagatla Manikanta, Tella Tarun Kumar, and Dhaval Patel. "ME $$^2$$ 2 : Movie Review Exploration Engine." In Applications of Cognitive Computing Systems and IBM Watson. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6418-0_3.

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Alladi, Krishnaswami. "Review of the Movie on the Mathematical Genius Ramanujan." In Ramanujan's Place in the World of Mathematics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6241-9_25.

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Kumb, Florian. "Literature Review on the Motion Picture Industry." In Local Movie Supply in the German Motion Picture Industry. Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-20685-7_3.

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Badugu, Srinivasu. "Telugu Movie Review Sentiment Analysis Using Natural Language Processing Approach." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1097-7_57.

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Patel, Priya, Devkishan Patel, and Chandani Naik. "Sentiment Analysis on Movie Review Using Deep Learning RNN Method." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5679-1_15.

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Singh, Jagbir, Hitesh Sharma, Rishabh Mishra, Sourab Hazra, and Namrata Sukhija. "Movie Review Sentimental Analysis Based on Human Frame of Reference." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4367-2_27.

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Schuller, Björn, and Tobias Knaup. "Learning and Knowledge-Based Sentiment Analysis in Movie Review Key Excerpts." In Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces. Theoretical and Practical Issues. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18184-9_39.

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Rabbimov, Ilyos, Iosif Mporas, Vasiliki Simaki, and Sami Kobilov. "Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments." In Speech and Computer. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60276-5_42.

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Jain, Achin, and Vanita Jain. "Effect of Activation Functions on Deep Learning Algorithms Performance for IMDB Movie Review Analysis." In Proceedings of International Conference on Artificial Intelligence and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4992-2_46.

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Conference papers on the topic "Movie Review"

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Topal, Kamil, and Gultekin Ozsoyoglu. "Movie review analysis: Emotion analysis of IMDb movie reviews." In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752387.

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"“Yeah! The Movie”." In ACM SIGGRAPH 2003 video review. ACM Press, 2003. http://dx.doi.org/10.1145/1006091.1006110.

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Özdağ, Beste. "Gender Inequality and Space Relationship in Movies: Mustang Movie Review." In 7th International Conference on Gender Studies: Gender, Space, Place & Culture. Eastern Mediterranean University, 2019. http://dx.doi.org/10.33831/gspc19/419-431/27.

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Zhuang, Li, Feng Jing, and Xiao-Yan Zhu. "Movie review mining and summarization." In the 15th ACM international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1183614.1183625.

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Ding, Zhongxiang, Yali Qi, and Deping Lin. "Albert-based sentiment analysis of movie review." In 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2021. http://dx.doi.org/10.1109/aemcse51986.2021.00254.

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Yin, Fulian, Yanyan Wang, Xingyi Pan, and Pei Su. "A Word Vector Based Review Vector Method for Sentiment Analysis of Movie Reviews Exploring the Applicability of the Movie Reviews." In 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2018. http://dx.doi.org/10.1109/iccia.2018.00028.

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Alsaqer, Alaa F., and Sreela Sasi. "Movie review summarization and sentiment analysis using rapidminer." In 2017 International Conference on Networks & Advances in Computational Technologies (NetACT). IEEE, 2017. http://dx.doi.org/10.1109/netact.2017.8076790.

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"Sentiment Analysis of Movie Review Based on LSTM." In 2019 the 9th International Workshop on Computer Science and Engineering. WCSE, 2019. http://dx.doi.org/10.18178/wcse.2019.06.042.

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Cao, Xuezhi, Weiyue Huang, and Yong Yu. "A Complete & Comprehensive Movie Review Dataset (CCMR)." In SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2016. http://dx.doi.org/10.1145/2911451.2914669.

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Azizan, Azilawati, Nurul Najwa SK Abdul Jamal, Mohammad Nasir Abdullah, Masurah Mohamad, and Nurkhairizan Khairudin. "Lexicon-Based Sentiment Analysis for Movie Review Tweets." In 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2019. http://dx.doi.org/10.1109/aidas47888.2019.8970722.

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Reports on the topic "Movie Review"

1

DellaVigna, Stefano, and Johannes Hermle. Does Conflict of Interest Lead to Biased Coverage? Evidence from Movie Reviews. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20661.

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2

Quak, Evert-jan. The Link Between Demography and Labour Markets in sub-Saharan Africa. Institute of Development Studies (IDS), 2020. http://dx.doi.org/10.19088/k4d.2021.011.

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Abstract:
This rapid review synthesises the literature from academic, policy, and knowledge institution sources on how demography affects labour markets (e.g. entrants, including youth and women) and labour market outcomes (e.g. capital-per-worker, life-cycle labour supply, human capital investments) in the context of sub-Saharan Africa. One of the key findings is that the fast-growing population in sub-Saharan Africa is likely to affect the ability to get productive jobs and in turn economic growth. This normally happens when workers move from traditional (low productivity agriculture and household businesses) sectors into higher productivity sectors in manufacturing and services. In theory the literature shows that lower dependency ratios (share of the non-working age population) should increase output per capita if labour force participation rates among the working age population remain unchanged. If output per worker stays constant, then a decline in dependency ratio would lead to a rise in income per capita. Macro simulation models for sub-Saharan Africa estimate that capital per worker will remain low due to consistently low savings for at least the next decades, even in the low fertility scenario. Sub-Saharan African countries seem too poor for a quick rise in savings. As such, it is unlikely that a lower dependency ratio will initiate a dramatic increase in labour productivity. The literature notes the gender implications on labour markets. Most women combine unpaid care for children with informal and low productive work in agriculture or family enterprises. Large family sizes reduce their productive labour years significantly, estimated at a reduction of 1.9 years of productive participation per woman for each child, that complicates their move into more productive work (if available). If the transition from high fertility to low fertility is permanent and can be established in a relatively short-term period, there are long-run effects on female labour participation, and the gains in income per capita will be permanent. As such from the literature it is clear that the effect of higher female wages on female labour participation works to a large extent through reductions in fertility.
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