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Opinion Mining and Sentiment Analysis in Social Media: Challenges and Applications

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It is a widely accepted truth there are great values embedded in the opinion and sentiment expressed by users on social media platforms. Nowadays, it is quite common for researchers or engineers to adopt opinion mining and sentiment analysis techniques to extract enriched emotional information from online text content. However, given the characteristics of social media, such as dynamic, short, informal and context dependent, applying general opinion mining and sentiment analysis techniques originally designed for static long text corpora would lead to serious bias. In many applications, even research that not specialized in opinion mining and sentiment analysis, this problem is ignored unintentionally or unintentionally. Such ignorance may contribute the failure of some designs or unexplainable results. In this paper, we summarized these challenges in social media sentiment analysis. Some potential solutions for these challenges are also discussed. Finally, we also introduced several state-of-the-art techniques in social media sentiment analysis.

Publication information

Original languageEnglish
Title of host publicationHCI in Business, Government, and Organizations : 5th International Conference, HCIBGO 2018 Held as Part of HCI International 2018 Las Vegas, NV, USA, July 15–20, 2018 Proceedings
EditorsFiona Fui-Hoon Nah, Bo Sophia Xiao
Number of pages13
Place of PublicationCham
PublisherSpringer
Publication date2018
Pages536-548
ISBN (Print)9783319917153
ISBN (Electronic)9783319917160
DOIs
StatePublished - 2018
Event5th International Conference on HCI in Business, Government and Organizations. HCIBGO 2018 - Las Vegas, United States
Duration: 15 Jul 201820 Jul 2018
Conference number: 5
http://2018.hci.international/hcibgo

Conference

Conference5th International Conference on HCI in Business, Government and Organizations. HCIBGO 2018
Nummer5
LandUnited States
ByLas Vegas
Periode15/07/201820/07/2018
AndetHeld as Part of HCI International 2018
Internetadresse
SeriesLecture Notes in Computer Science
Volume10923
ISSN0302-9743

    Research areas

  • Opinion mining and sentiment analysis, Social media, Business applications

ID: 56462665