SMERT: Single-stream Multimodal BERT for Sentiment Analysis and Emotion Detection

2021 ◽  
Vol 48 (10) ◽  
pp. 1122-1131
Author(s):  
Kyeonghun Kim ◽  
Jinuk Park ◽  
Jieun Lee ◽  
Sanghyun Park
IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 181074-181090
Author(s):  
Ali Shariq Imran ◽  
Sher Muhammad Daudpota ◽  
Zenun Kastrati ◽  
Rakhi Batra

2014 ◽  
Vol 7 (1) ◽  
Author(s):  
Samuel Cruz-Lara ◽  
Alexandre Denis ◽  
Nadia Bellalem

Within a globalized world, the need for linguistic support is increasing every day. Linguistic information, and in particular multilingual textual information, plays a significant role for describing digital content: information describing pictures or video sequences, general information presented to the user graphically or via a text-to-speech processor, menus in interactive multimedia or TV, subtitles, dialogue prompts, or implicit data appearing on an image such as captions, or tags. It is obviously crucial to associate digital content to multilingual textual information in a non-intrusive way: the user must decide, whether or not, he wants to display the textual information related to the digital content he is dealing with in any particular language.In this paper we will present a general review on linguistic and multilingual issues related to virtual worlds and serious games. The expression “linguistic and multilingual issues” will consider not only any kind of linguistic support (such as syntactic and semantic analysis) based on textual information, but also any kind of multilingual and monolingual topics (such as localization or automatic translation), and their association to virtual worlds and serious games. We will focus on our ongoing research activities, particularly in the framework of sentiment analysis and emotion detection. Note that we will also dedicate special attention to standardization issues because they grant interoperability, stability, and durability.The review will essentially be based on our own experience but some interesting international research projects and applications will be also mentioned, in particular, research projects and applications related to sentiment analysis and emotion detection.


Author(s):  
Neha V. Thakare

Abstract: Sentiment Analysis is that the most ordinarily used approach to research knowledge that is within the form of text and to identify sentiment content from the text. Opinion Mining is another name for sentiment analysis. a good vary of text data is getting generated within the form of suggestions, feedback, tweets, and comments. E-Commerce portals area unit generating tons of data. Every day within the form of customer reviews. Analyzing E-Commerce data can facilitate on-line retailers to grasp customer expectations, offer an improved searching expertise, and to extend sales. Sentiment Analysis can be used to identify positive, negative, and neutral information from the customer reviews. Researchers have developed a lot of techniques in Sentiment Analysis. Keywords: Sentiment analysis, Sentiment classification, Feature selection, Emotion detection, Customer Reviews;


Author(s):  
Ria Ambrocio Sagum

The research focus on the issue of accuracy for sentiment analysis. The researcher experimented on emotion detection result to be used in sentiment analysis. The emotions that were included in this research are happiness, sadness, anger, and fear. Once emotion was detected the system will then use it to know the sentiment of the person on a particular movie. This paper aims to measure the accuracy in sentiment analysis enhanced by emotion detection and to know whether emotion detection plays a key role in reading sentiment analysis.


Author(s):  
Emily Öhman ◽  
Marc Pàmies ◽  
Kaisla Kajava ◽  
Jörg Tiedemann

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