Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Khan, Atif; Gul, Muhammad Adnan; Uddin, Imad; Shah, Syed Atif Ali; Ahmad, Shafiq; Firdausi, Muhammad; Zaindin, Mazen

doi:10.1155/2020/5812715

Cited by 18 publications

(14 citation statements)

References 50 publications

(64 reference statements)

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“…Moreover, they were able to increase the accuracy to 93.80% using a hybrid naïve Bayes/GA classifier. Khan et al [13] presented a method for the classification and summarization of movie reviews. To improve classification accuracy, their method employs unigrams, bigrams, and trigrams.…”

Section: Hirschberg and Manningmentioning

confidence: 99%

Sentiment Analysis of Online Movie Reviews using Machine Learning

Steinke¹,

Wier²,

Simon³

et al. 2022

IJACSA

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Many websites encourage their users to write reviews for a wide variety of products and services. In particular, movie reviews may influence the decisions of potential viewers. However, users face the arduous tasks of summarizing the information in multiple reviews and determining the useful and relevant reviews among a very large number of reviews. Therefore, we developed machine learning (ML) models to classify whether an online movie review has positive or negative sentiment. We utilized the Stanford Large Movie Review Dataset to build models using decision trees, random forests, and support vector machines (SVMs). Further, we compiled a new dataset comprising reviews from IMDb posted in 2019 and 2020 to assess whether sentiment changed owing to the coronavirus disease 2019 (COVID-19) pandemic. Our results show that the random forests and SVM models provide the best classification accuracies of 85.27% and 86.18%, respectively. Further, we find that movie reviews became more negative in 2020. However, statistical tests show that this change in sentiment cannot be discerned from our model predictions.

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Section: Hirschberg and Manningmentioning

confidence: 99%

Sentiment Analysis of Online Movie Reviews using Machine Learning

Steinke¹,

Wier²,

Simon³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…With so many texts and documents, it takes people a long time to read and summarize the essential information. Thus, text summarization may be accomplished through the use of machine learning models that have been trained on a large amount of labeled data (Khan et al, 2020). TextRank, which was proposed by Mihalcea and Tarau (2004), involves the exploration of massive collections of text and the conversion of unstructured text data to structured data.…”

Section: State-of-the-art Text Mining Approachesmentioning

confidence: 99%

Achieving Eco-Innovative Smart Glass Design with the Integration of Opinion Mining, QFD and TRIZ

Lee

Tsang

Chong

et al. 2022

Preprint

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As technology advances, the release of new smart devices accelerates, and thus the disposal of electronic devices has increased exponentially, worsening the gravity of electronic waste all over the world. It is eager to consider eco-friendly product design in order to sustain global supply chains. When addressing customer requirements in new product design, sustainable design thinking should thus be incorporated to create not only innovative, but also sustainable products. The purpose of this research is to build a systematic approach to achieve sustainable new product development. To examine the system effectiveness, smart glasses which are rapidly developed in the era of the metaverse is considered in a case study. By utilizing opinion mining approaches, including wordcloud and TextRank algorithm, to decipher customer needs, quality function deployment (QFD) is used to investigate the existing state of smart glasses, as well as translate customers’ needs into technical requirements that serve as the basis for sustainable new product design. Subsequently, the theory of inventive problem solving (TRIZ) is applied to formulate the contradiction matrix between improvement and preservation areas in order to reach the eco-innovation in a systematic manner. Beyond the theoretical investigation, this study presents the application of the proposed system for the sustainable design of smart glasses, so as to support the next generation of smart glasses.

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“…Even though most Artificial Intelligence (AI) and Machine Learning (ML) algorithms and their enabling platforms for performing BDA are free, they require a new skill set that is uncommon to most practitioners in Big Data Mining and Analytics, June 2022, 5 (2): [81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97] this field and organisations' IT departments [3] . Hence, integrating these tools and platforms seamlessly into an organisation's internal and external data on a common platform is a challenge.…”

Section: Introductionmentioning

confidence: 99%

A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

Nti

Quarcoo

Aning

et al. 2022

Big Data Min. Anal.

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The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA.The related applications fields, challenges, and most importantly the openings for future research, are detailed.

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Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Cited by 18 publications

References 50 publications

Sentiment Analysis of Online Movie Reviews using Machine Learning

Sentiment Analysis of Online Movie Reviews using Machine Learning

Achieving Eco-Innovative Smart Glass Design with the Integration of Opinion Mining, QFD and TRIZ

A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

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