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Hot Topic Discovery across Social Networks Based on Improved LDA Model

2021 KSII Transactions on Internet and Information Systems  
Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation  ...  Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet.  ...  [11] took users' social relations into account and proposed a "person-viewpoint-topic" (POT) model which could detect social groups and analyze their emotions. Iwata et al.  ... 
doi:10.3837/tiis.2021.11.004 fatcat:e2jx26wd7vavpkghybck4j5p6i

Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey [article]

Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li, Liang Zhao
2018 arXiv   pre-print
Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents.  ...  Also, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.  ...  Nanjing Science and Technology Development Plan Project (201805036).  ... 
arXiv:1711.04305v2 fatcat:jzsx6owjyjfo3gkbohrc2ggkzq

Behavior analysis in social networks: Challenges, technologies, and trends

Meng Wang, Ee-Peng Lim, Lei Li, Mehmet Orgun
2016 Neurocomputing  
Their professional evaluations and constructive comments are vital for securing the high quality of the special issue.  ...  The second two papers, "Multi-Label Maximum Entropy Model for Social Emotion Classification over Short Text" and "Detecting Influenza States based on Hybrid Model with Personal Emotional Factors from Social  ...  In the first paper "Predicting the Popularity of Viral Topics Based on Time Series Forecasting", Hu et al. demonstrate the high correlation of the short-term popularity of viral topics, and present a method  ... 
doi:10.1016/j.neucom.2016.06.008 fatcat:x5mumxc3orduxewwvwsdxdar54

People Opinion Topic Model

Hongxu Chen, Hongzhi Yin, Xue Li, Meng Wang, Weitong Chen, Tong Chen
2017 Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion  
In this paper, we are focusing on the problem of finding opinion variations based on different groups of people and introducing the concept of opinion based community detection.  ...  Mining various hot discussed topics and corresponding opinions from different groups of people in social media (e.g., Twitter) is very useful.  ...  Acknowledgements This work is partially supported by ARC Discovery Early Career Researcher Award (Grant No. DE160100308) and ARC Discovery Project (Grant No. DP160104075 and Grant No. DP170103954)".  ... 
doi:10.1145/3041021.3051159 dblp:conf/www/ChenYLWCC17 fatcat:2abmkdto5zdgbajil4rbnvfdny

Personalized Recommendations Based on Sentimental Interest Community Detection

Jianxing Zheng, Yanjie Wang
2018 Scientific Programming  
Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations.  ...  The semantics of topics reflect users' implicit interests. Sentiments on topics imply users' sentimental tendency. People with common sentiments can form resonant communities of interest.  ...  Acknowledgments This work was partially supported by Youth Science Fund Project of the National Natural Science Foundation of China (no. 61603229) and the project of the Natural Science Foundation of Shanxi  ... 
doi:10.1155/2018/8503452 fatcat:5a5mizfl6bg4hkcz5ic5n2l5pm

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

Guillem Aguado, Vicente Julián, Ana García-Fornes, Agustín Espinosa
2020 Applied Sciences  
Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can  ...  For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed.  ...  The three topics are user state detection, risk prevention, and recommendation. • User state detection: refers to the automatic detection of an aspect of the user state by the system.  ... 
doi:10.3390/app10196746 fatcat:m2gqf3utabgtrcvhtbh53hksfq

Classifying Short Text in Social Media: Twitter as Case Study

Faris Kateb, Jugal Kalita
2015 International Journal of Computer Applications  
Topics of interest include micro-blog summarization, breaking news detection, opinion mining and discovering trending topics.  ...  Short messages lead to less accurate results. This has motivated investigation of efficient algorithms to overcome problems that arise due to the short and often informal text of tweets.  ...  Classification is also affected by the huge number of short tweets spread out over many different topics, and hence, with many features. Static data is useful and easy to analyze.  ... 
doi:10.5120/19563-1321 fatcat:dzus5l7hhnf7hhtkuvckyxivs4

Unsupervised Topic Discovery in User Comments [article]

Christoph Stanik, Tim Pietz, Walid Maalej
2021 arXiv   pre-print
We introduce an approach for automatically discovering topics composed of semantically similar user comments based on deep bidirectional natural language processing algorithms.  ...  On social media platforms like Twitter, users regularly share their opinions and comments with software vendors and service providers.  ...  However, such information is in particular crucial when working with short texts such as tweets or user comments in general. Approach and Rationale.  ... 
arXiv:2108.08543v1 fatcat:7et3xoidojhxtbgmy37vsy3lz4

Classification Techniques on Twitter Data: A Review

S. Shafina Banu, K. Syed Kousar Niasi, E. Kannan
2019 Asian Journal of Computer Science and Technology  
Sentiment analysis is the method of defining the emotional tone behind a sequence of words, used to gain an accepting of the attitudes, opinions and emotions conveyed within an online mention.  ...  Sentiment analysis is tremendously useful in social media observing as it allows us to gain a synopsis of the broader public opinion behind definite topics.  ...  Discovery sources of information and observing their progress on the web is a very difficult task due to the huge number of different sources and the huge volume of texts; each with their individual opinions  ... 
doi:10.51983/ajcst-2019.8.s2.2022 fatcat:ylpffpp3bfaznd344wflsy4p7y


Shivangi Chawla
2018 International Journal of Advanced Research in Computer Science  
This paper presents a unique study to focus on the outcome of research on Textual Emotion Mining instead on the process of emotion mining.  ...  It classifies the output of TEM by presenting an emotion-output model.  ...  User data in the form of text can potentially be extracted for emotions to benefit mankind in several commercial and social spheres like market analysis, security and crisis management, stress detection  ... 
doi:10.26483/ijarcs.v9i1.5472 fatcat:hrqsgyvjmnd7zcq3ib4uo4rk2i

Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities

Bo Dao, Thin Nguyen, Svetha Venkatesh, Dinh Phung
2017 International Journal of Data Science and Analytics  
We analyse sentiment-based, psycholinguistics-based and topicbased features from blog posts made by members of these online communities.  ...  The visualization of the discovered meta-communities in their use of latent topics shows a difference between the groups.  ...  Acknowledgements This work is partially supported by the Telstra-Deakin Centre of Excellence in Big Data and Machine Learning.  ... 
doi:10.1007/s41060-017-0073-y dblp:journals/ijdsa/DaoNVP17 fatcat:japndqqvjvhutpudrahaoaipxe

Social Media Analytics for Behavioral Health

Rose Yesha Aryya, Gangopadhyay
2015 International Journal of Emergency Mental Health  
More users are choosing to share their thoughts and emotions that encompass their daily lives.  ...  Furthermore, topic modeling analyzes how these themes relate to one another, and how they differ over time (Blei, 2012) .  ... 
doi:10.4172/1522-4821.1000255 fatcat:jrercczhovfmhkyumc7dwdixiu

Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media

Sudha Subramani, Manjula O'Connor
2018 EAI Endorsed Transactions on Scalable Information Systems  
Thus provides actionable knowledge by monitoring and analysing continuous and rich user generated content. disclose their sufferings, and there is a need to collect data in a private space and in the absence  ...  Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions, and health care.  ...  [39] infers the problem of topic detection as probabilistic distribution. It represents the document as a distribution over topics and a topic as distribution over words.  ... 
doi:10.4108/eai.29-5-2018.154807 fatcat:kvgaqlzvxnc7xou4upc5tmrehq

Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks [article]

Shaoxiong Ji, Xue Li, Zi Huang, Erik Cambria
2021 arXiv   pre-print
This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators  ...  Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention.  ...  Acknowledgments The authors would like to thank Philip Resnik for providing the UMD Reddit Suicidality Dataset and Mark Dredze for providing the dataset in the CLPsych 2015 shared task.  ... 
arXiv:2004.07601v3 fatcat:amgogcdh75hshhg6klv25lqzdu

Survey of Generative Methods for Social Media Analysis [article]

Stan Matwin, Aristides Milios, Paweł Prałat, Amilcar Soares, François Théberge
2021 arXiv   pre-print
We included two important aspects that currently gain importance in mining and modeling social media: dynamics and networks.  ...  Networks, on the other hand, may capture various complex relationships providing additional insight and identifying important patterns that would otherwise go unnoticed.  ...  However, short documents (social media posts) create much sparser co-occurence statistics, making topic discovery more challenging.  ... 
arXiv:2112.07041v1 fatcat:xgmduwctpbddfo67y6ack5s2um
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