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Emotion Detection with Neural Personal Discrimination
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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There have been a recent line of works to automatically predict the emotions of posts in social media. Existing approaches consider the posts individually and predict their emotions independently. ...
In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words. ...
Finally, we employ attention mechanisms to aggregate the representation of informative attributes-aware words into a vector for the emotion prediction. ...
doi:10.18653/v1/d19-1552
dblp:conf/emnlp/ZhouWLZZ19
fatcat:bwlmodsgengbrb3kirlqwm6jzu
Emotion Detection with Neural Personal Discrimination
[article]
2019
arXiv
pre-print
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There have been a recent line of works to automatically predict the emotions of posts in social media. Existing approaches consider the posts individually and predict their emotions independently. ...
In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words. ...
2019 2017), both discrete and neural models have been used to predict the emotions of posts in social media. ...
arXiv:1908.10703v1
fatcat:amjvaghzlzgsrehvrr3y4t46t4
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
[article]
2020
arXiv
pre-print
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Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. ...
Current suicidal ideation detection methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature ...
[16] predicted suicide attempt and mental health with neural models under the framework of multi-task learning by predicting the gender of users as auxiliary task. Gaur et al. ...
arXiv:1910.12611v2
fatcat:63z4uvh5zrgyzb2bawtlbuo34m
An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief
[chapter]
2016
Studies in Computational Intelligence
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Recent years, on the other hand, have witnessed the advent of social networking websites, microblogs, wikis and Web applications and consequently, an unprecedented growth in user-generated data is poised ...
Sentiment analysis of disaster related posts in social media in could help to detect posts that contribute to the situational awareness and better understand the dynamics of the network including users ...
Subjectivity features could be used to assess the amount of emotion a user expressed in her tweets for situational awareness and information extraction. ...
doi:10.1007/978-3-319-30319-2_13
fatcat:aajzoeahsngtpg4ybvqm32hwem
Towards Deep Learning Prospects: Insights for Social Media Analytics
2019
IEEE Access
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Nevertheless, instead of the technical description, this paper emphasis on describing the SMA-oriented problems with the DL-based solutions. ...
He has taken part in many research projects, and is the Principal Investigator (PI) of two projects. ...
In addition, Liu and Zhu [42] used microblog data to predict users' behavior by proposing an unsupervised drawing of the Linguistic Representation Feature Vector (LRFV). ...
doi:10.1109/access.2019.2905101
fatcat:65mxyey3frdrfngvbfnfss3gpa
PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. ...
Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. ...
Correspondingly, Deng et al. (2015) explored user emotion in microblogs for music recommendation and Dhahri, Matsumoto, and Hoashi (2018) built a personalized mood-aware song map according to implicit ...
doi:10.1609/aaai.v34i01.5352
fatcat:jh5uvn5ynfbyzadjffdafjwipe
Emotion Based Hate Speech Detection using Multimodal Learning
[article]
2022
arXiv
pre-print
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Our results demonstrate that incorporating emotional attributes leads to significant improvement over text-based models in detecting hateful multimedia content. ...
Our preliminary study concluded that the most essential features in classifying hate speech would be the speaker's emotional state and its influence on the spoken words, therefore limiting our current ...
Parthasarathy and Busso [33] , in their research, claim that the emotional attributes are interrelated and hence predicting them with a unified learning framework will give us better results. ...
arXiv:2202.06218v1
fatcat:5iwxp4wfonclvlt7hxaetlnf5q
Automatic Rumor Detection on Microblogs: A Survey
[article]
2018
arXiv
pre-print
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In this survey, we introduce a formal definition of rumor in comparison with other definitions used in literatures. ...
We give our suggestions for future rumors detection on microblogs as a conclusion. ...
In [11] , emotional marks( question mark and exclamation mark) and emotion icons are counted as textual features. ...
arXiv:1807.03505v1
fatcat:kvwukm7kofhyfd3yjlajagoxce
Content-based Music Recommendation: Evolution, State of the Art, and Challenges
[article]
2021
arXiv
pre-print
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identify six overarching challenges, according to which we organize our main discussion: increasing recommendation diversity and novelty, providing transparency and explanations, accomplishing context-awareness ...
literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated ...
More recently, several works approach the challenge of building an emotion-aware MRS by extracting emotions from user-generated texts, in particular from microblogs [Cai et al. 2007a; Chen et al. 2013a ...
arXiv:2107.11803v1
fatcat:4hz4hqkkmvcapbdr3wvtp2t4iu
The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis
[chapter]
2015
Lecture Notes in Computer Science
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Hitherto, sentiment analysis has been mainly based on algorithms relying on the textual representation of online reviews and microblogging posts. ...
In order to overcome this and many other issues related to sentiment analysis, we propose a novel framework, termed concept-level sentiment analysis (CLSA) model, which takes into account all the natural-language-processing ...
Recursive neural networks (RNN) predict the sentiment class at each node in the parse tree and try to capture the negation and its scope in the entire sentence. ...
doi:10.1007/978-3-319-18117-2_1
fatcat:zlu3xwbijjcbxhq3tngtxqljz4
Learning to embed music and metadata for context-aware music recommendation
2017
World wide web (Bussum)
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In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users' contextual preferences for music. ...
Then it infers and models users' global and contextual preferences for music from their listening records with the learned embeddings. ...
representation with neural networks for community question answering retrieval. ...
doi:10.1007/s11280-017-0521-6
fatcat:mqosqqycbbcsvgiupw3rzcysse
Personalized Microtopic Recommendation on Microblogs
2017
ACM Transactions on Intelligent Systems and Technology
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Microblogging services such as Sina Weibo and Twitter allow users to create tags explicitly indicated by the # symbol. ...
Using two real-world datasets, we evaluate our model with different kinds of content and contextual information. ...
By replacing the topic model with deep learning models such as Stacked Denoising Autoencoders (a feedforward neural network for learning representations of the input data by learning to predict the clean ...
doi:10.1145/2932192
fatcat:vjvznzywlje2fczzufav6e5msu
Advances in Emotion Recognition: Link to Depressive Disorder
[chapter]
2020
Mental Disorders [Working Title]
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However, their algorithms are based on intentionally expressed emotion and are user dependent, which may restrict generalization to other users [5] . (2) The CNS emotion patterns. ...
The emotion recognition algorithms using emotion representation based on emotional labels are intuitive which are ambiguous for computer processing. ...
[32] proposed a joint model of microblog emotion recognition and emotion incentive extraction based on neural network. ...
doi:10.5772/intechopen.92019
fatcat:jmss4llbpnfrxcue6bzebsgmby
Text-based Sentiment Analysis and Music Emotion Recognition
[article]
2018
arXiv
pre-print
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First, deep neural networks need to be fed with data sets that are big in size as well as properly labeled. ...
Sentiment polarity of tweets, blog posts or product reviews has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. ...
At the same time, the rising popularity of microblogs that are rich in user opinions, motivated many researchers to create datasets of emotionally labeled texts. ...
arXiv:1810.03031v1
fatcat:4vj4euwtxbghbjdev2gutcgjny
Introduction to the Special Issue on Language in Social Media: Exploiting Discourse and Other Contextual Information
2018
Computational Linguistics
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., the user's profile, the social network of the user, and their interactions with other users). ...
We conclude with an overview of the papers accepted in this special issue, highlighting what we believe are the future directions in processing social media texts. ...
For example, user profiles like age, gender, and location can be used to enhance subjectivity detection (including sentiment and emotion) (Volkova, Coppersmith, and Van Durme 2014; Volkova and Bachrach ...
doi:10.1162/coli_a_00333
fatcat:mbpjsq3ltfdtnfbmldxlt7wzci
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