Extensive study of text based methods for opinion mining

Kulkarni, Dhanashree; Rodd, Sunil F.

doi:10.1109/icisc.2018.8399127

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…Another example usage is to provide channel rankings using the video metadata from the dataset. We provide a ranking of top 20 English news channels in YouTube by their monthly views from May 9, 2018 to June 9, 2018 results are exactly the same compared to data from KEDOO 7 downloaded by the YoutubeStat.py module [25].…”

Section: A Metadata Analysismentioning

confidence: 99%

“…With millions of posts and replies uploaded every day on social media such as Facebook, Twitters and YouTube, it is an abundant and informative data source of public opinions; thus, it has attracted lots of attention from both academia and industry to understand people and society [4]- [6]. Most previous text mining-based social media analysis focused on Twitter and Facebook [7]. YouTube, generally considered as a video platform, the values of its text comments below videos have long been underestimated.…”

Section: Introductionmentioning

confidence: 99%

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YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

Lin

Choi

et al. 2018

2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

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With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freelyavailable collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.

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Section: A Metadata Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

Lin

Choi

et al. 2018

2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

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“…The general algorithm that used for opinion mining and sentiment analysis is Naïve Bayes. There are three main components of opinion mining and analysis methods such as pre-processing, feature extraction and classification (Kulkarni & Rodd, 2018. Many research that uses Naïve Bayes as classifier algorithm for instances (Vijay et al, 2020 for product review data from amazon, (Harahap et al, 2019 for predicting purchase, (Ilham Esa Tiffani, 2020 for hotel review, (Pugsee & Chatchaithanawat, 2020 for laptop reviews and (Pugsee et al, 2019 skin care products on twitter, also (Poovaraghan et al, 2019 and(Granik &Mesyura, 2017 for fake news detection applications.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, most of the data from the internet are unstructured. In (Kulkarni & Rodd, 2018 the author states that the accurate identification of content features gathered from the unstructured textual data is major research challenge. Therefore, it is a must to design an efficient technique which will be able to identify such features from the unstructured datasets (Kulkarni & Rodd, 2018.…”

Section: Introductionmentioning

confidence: 99%

“…In (Kulkarni & Rodd, 2018 the author states that the accurate identification of content features gathered from the unstructured textual data is major research challenge. Therefore, it is a must to design an efficient technique which will be able to identify such features from the unstructured datasets (Kulkarni & Rodd, 2018. Therefore, an alternative or at least an optimization of the current algorithm is needed in some applications.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of EM Algorithm in Opinion Mining Movies Review Case Studies

Romadloni¹,

Anugrah²

2021

JDR

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Movies are very familiar to everyone, from children, adolescents to adults, whether just because they want to watch, a hobby, or fill their spare time. Movies that used to be watched only on television and had to wait months after release or directly to the cinema, with the development of technology, of course, it is increasingly easier for everyone to enjoy movies, now they can be watched through paid television services to smartphones. One of the websites that viewers often use to review movies they have watched is IMDb. The data review can be used to get an opinion or opinion mining from the audience, whether the title of the movie being reviewed is good or not. One of the algorithms that are often used is Naïve Bayes, apart from being easy to implement, Naïve Bayes is also known to be very fast and easy to use to predict classes on a test dataset. The purpose of this study is to see how much influence the Expectation-Maximization to increase accuracy on implementation of Expectation-Maximization algorithm in opinion mining movies review case studies. From the results of this study using the Expectation-Maximization method, it was found that the accuracy increased by 4% compared to using only Naïve Bayes.

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Classification of Sentiment Analysis Using Machine Learning

Parikh

Shah

2020

Innovative Data Communication Technologies and Application

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Extensive study of text based methods for opinion mining

Cited by 8 publications

References 8 publications

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

Implementation of EM Algorithm in Opinion Mining Movies Review Case Studies

Classification of Sentiment Analysis Using Machine Learning

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