Chinese micro-blog sentiment analysis based on semantic features and PAD model

Gao, Fei; Sun, Xiao; Wang, Kunxia; Ren, Fuji

doi:10.1109/icis.2016.7550903

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…As a CME, PAD can distinguish different emotional states effectively (Russell, 1980 ; Gao et al, 2016 ) and break from the traditional tag-description method. As one of the relatively mature emotional models (Mehrabian and Russell, 1974 ; Mehrabian, 1996a ; Gunes et al, 2011 ; Jia et al, 2011 ; Chen and Long, 2013 ; Gao et al, 2016 ; Osuna et al, 2020 ; Wang et al, 2020 ), the PAD model measures the mapping relationship between emotional states and typical emotions by “distance” to some extent, thus transforming the analytical studies of discrete emotional voices into quantitative studies of emotional voices (Mehrabian and Russell, 1974 ; Mehrabian, 1996a ; Gunes et al, 2011 ; Jia et al, 2011 ; Chen and Long, 2013 ; Gao et al, 2016 ; Osuna et al, 2020 ; Wang et al, 2020 ). It has been extensively applied in information processing, emotional computing, and man–machine interaction (Dai et al, 2015 ; Weiguo and Hongman, 2019 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the one hand, voice signals contain the verbal content to be transmitted. On the other hand, rhythms in the vocalizations contain rich emotional indicators (Murray and Arnott, 1993 ; Gao et al, 2016 ; Noroozi et al, 2018 ; Skerry-Ryan et al, 2018 ). Each emotional state has unique acoustic features (Scherer et al, 1991 ; Weninger et al, 2013 ; Liu et al, 2018 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Affective Voice Interaction and Artificial Intelligence: A Research Study on the Acoustic Features of Gender and the Emotional States of the PAD Model

2021

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New types of artificial intelligence products are gradually transferring to voice interaction modes with the demand for intelligent products expanding from communication to recognizing users' emotions and instantaneous feedback. At present, affective acoustic models are constructed through deep learning and abstracted into a mathematical model, making computers learn from data and equipping them with prediction abilities. Although this method can result in accurate predictions, it has a limitation in that it lacks explanatory capability; there is an urgent need for an empirical study of the connection between acoustic features and psychology as the theoretical basis for the adjustment of model parameters. Accordingly, this study focuses on exploring the differences between seven major “acoustic features” and their physical characteristics during voice interaction with the recognition and expression of “gender” and “emotional states of the pleasure-arousal-dominance (PAD) model.” In this study, 31 females and 31 males aged between 21 and 60 were invited using the stratified random sampling method for the audio recording of different emotions. Subsequently, parameter values of acoustic features were extracted using Praat voice software. Finally, parameter values were analyzed using a Two-way ANOVA, mixed-design analysis in SPSS software. Results show that gender and emotional states of the PAD model vary among seven major acoustic features. Moreover, their difference values and rankings also vary. The research conclusions lay a theoretical foundation for AI emotional voice interaction and solve deep learning's current dilemma in emotional recognition and parameter optimization of the emotional synthesis model due to the lack of explanatory power.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Affective Voice Interaction and Artificial Intelligence: A Research Study on the Acoustic Features of Gender and the Emotional States of the PAD Model

2021

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“…Further to the quoted lexicons, 49 studies used lexicons that they created as part of their work. Some studies composed their lexicons from emoticons/emojis that were extracted from a dataset [474,48,423,343,345,312,391,444,430,407], combined publicly available emoticon lexicons/lists [495] or mapped emoticons to their corresponding polarity [481], and others [424,499,389,390,414,444,430,503] used seed/feeling/emotional words to establish a microblog typical emotional dictionary. Additionally, some authors constructed or used sentiment lexicons [195,123,417,316,215,124,320,322,328,496,361,363,439,492,397,398,91,401,403] some of which are domain or language specific [478,317,516,347,206,100,…”

Section: Hybrid (Hy)mentioning

confidence: 99%

“…Chinese 53 [474,289,150,286,122,415,95,199,546,90,218,57,339,424,496,347,200,106,107,355,312,439,364,139,174,207,14,134,98,135,73,520,166,74,176,510,492,386,389,78,390,493,392,394,189,396,397,470,414,398,501,…”

Section: Language Total Studiesmentioning

confidence: 99%

Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. 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. Therefore, 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 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, natural language processing and other

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“… Abubakar and Ilkan (2016) pointed out that online reviews of tourist destinations affect tourists’ trust and stimulate their purchase demand. Gao et al (2016) achieved affective polarity classification of text by building semantic features and binary models for sentiment analysis of microblog comment data. In addition, data mining and applications of sentiment analysis in tourism have been gradually developed.…”

Section: Introductionmentioning

confidence: 99%

Big Data Recommendation Research Based on Travel Consumer Sentiment Analysis

Zhu

2022

Front. Psychol.

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More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine (SVM) algorithm based on travel consumer sentiment analysis and builds an Hadoop Distributed File System (HDFS) system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification.

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Chinese micro-blog sentiment analysis based on semantic features and PAD model

Cited by 8 publications

References 8 publications

Affective Voice Interaction and Artificial Intelligence: A Research Study on the Acoustic Features of Gender and the Emotional States of the PAD Model

Affective Voice Interaction and Artificial Intelligence: A Research Study on the Acoustic Features of Gender and the Emotional States of the PAD Model

Over a Decade of Social Opinion Mining: A Systematic Review

Big Data Recommendation Research Based on Travel Consumer Sentiment Analysis

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