Over a Decade of Social Opinion Mining: A Systematic Review
Keith Cortis,Brian Davis +1 more
TLDR
Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple media formats, like text, image, video and audio.Abstract:
Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.read more
Citations
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Journal ArticleDOI
A survey on sentiment analysis methods, applications, and challenges
TL;DR: Sentiment analysis is the process of gathering and analyzing people's opinions, thoughts, and impressions regarding various topics, products, subjects, and services as mentioned in this paper , which can be beneficial to corporations, governments and individuals for collecting information and making decisions based on opinion.
Journal ArticleDOI
A survey on sentiment analysis methods, applications, and challenges
TL;DR: Sentiment analysis is the process of gathering and analyzing people's opinions, thoughts, and impressions regarding various topics, products, subjects, and services as mentioned in this paper , which can be beneficial to corporations, governments and individuals for collecting information and making decisions based on opinion.
Journal ArticleDOI
An enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysis
Manju Venugopalan,Deepa Gupta +1 more
TL;DR: This paper proposed an unsupervised approach for aspect term extraction, a guided Latent Dirichlet Allocation (LDA) model that uses minimal aspect seed words from each aspect category to guide the model in identifying the hidden topics of interest to the user.
Proceedings ArticleDOI
Sentimental Analysis Using Supervised Learning Algorithms
TL;DR: Sentimental analysis is the process of collection of data and analyzing one's studies, ideas, also extremities about colorful motifs, products, motifs and services as discussed by the authors , which can be useful to companies, governmental organizations, and individualities by gathering data and practicing vision-grounded opinions.
Journal ArticleDOI
Customer sentiment analysis and prediction of halal restaurants using machine learning approaches
TL;DR: In this article , the authors analyzed and predicted customer reviews of halal restaurants using machine learning (ML) approaches and found that most of the customer reviews toward halal restaurant were positive.
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