Geospatial Event Detection by Grouping Emotion Contagion in Social Media

Lwowski, Brandon; Rad, Paul; Choo, Kim‐Kwang Raymond

doi:10.1109/tbdata.2018.2876405

Cited by 16 publications

(5 citation statements)

References 40 publications

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“…Whereas RS is divided into three main state-of-the-art categories such as CBF, CF, and hybrid mode. The CBF comprises of some algorithms named term frequency-inverse domain frequency ( Majid et al., 2013 ; Jiang et al, 2016 ; Wen et al, 2017 ; Psyllidis, Yang & Bozzon, 2018 ) topic modeling ( Wang et al., 2014 ; Lwowski, Rad & Choo, 2018 ), latent Dirichlet allocation ( Wang et al., 2014 ; Fang et al., 2014 ; Pyo & Kim, 2014 ; Jiang et al., 2015 ; Xu, Chen & Chen, 2015 ; Shi et al, 2017 ; Sang, Yan & Xu, 2018 ; Cui et al, 2018 ; Psyllidis, Yang & Bozzon, 2018 ; Nguyen & Cho, 2020 ; Ge et al, 2020 ) feature extraction ( Yu et al, 2016 ), word2vec ( Zhao et al, 2018 ), and natural language processing ( Psyllidis, Yang & Bozzon, 2018 ). On the other hand, CF uses user-item matrix ( Pyo & Kim, 2014 ; Jiang et al., 2015 ; Moro, Rita & Vala, 2016 ; Iqbal et al, 2019 ; Manca, Boratto & Carta, 2018 ; Zhang et al, 2019 ; Ju, Wang & Xu, 2019 ; Margaris, Vassilakis & Spiliotopoulos, 2020 ; Shahbaznezhad, Dolan & Rashidirad, 2021 ), friend-matching graph ( Wang et al., 2014 ), social network analysis ( Wu et al, 2015 ; Wu et al, 2019 ), matrix factorization ( Zhao, Qian & Xie, 2016 ; Yu et al, 2016 ; Zhao et al, 2018 ; Xu, 2018 ), classification ( Yang & Jiang, 2018 ), and graph theory ( Alduaiji, Datta & Li, 2018 ; Ahmadian et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Data mining-based recommendation system using social networks—an analytical study

Ajmal

Awais

Khurshid

et al. 2023

PeerJ Computer Science

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In the current age, social media is commonly used and shares enormous data. However, a huge amount of data makes it difficult to deal with. It requires a lot of storage and processing time. The content produced by social media needs to be stored efficiently by using data mining methods for providing suitable recommendations. The goal of the study is to perform a systematic literature review (SLR) which finds, analyzes, and evaluates studies that relate to data mining-based recommendation systems using social networks (DRSN) from 2011 to 2021 and open up a path for scientific investigations to enhance the development of recommendation systems in a social network. The SLR follows Kitchenhem’s methodology for planning, guiding, and reporting the review. A systematic study selection procedure results in 42 studies that are analyzed in this article. The selected articles are examined on the base of four research questions. The research questions focus on publication venues, and chronological, and geographical distribution in DRSN. It also deals with approaches used to formulate DRSN, along with the dataset, size of the dataset, and evaluation metrics that validate the result of the selected study. Lastly, the limitations of the 42 studies are discussed. As a result, most articles published in 2018 acquired 21% of 42 articles, Whereas, China contributes 40% in this domain by comparing to other countries. Furthermore, 61% of articles are published in IEEE. Moreover, approximately 21% (nine out of 42 studies) use collaborative filtering for providing recommendations. Furthermore, the Twitter data set is common in that 19% of all other data sets are used, and precision and recall both cover 28% of selected articles for providing recommendations in social networks. The limitations show a need for a hybrid model that concatenates different algorithms and methods for providing recommendations. The study concludes that hybrid models may help to provide suitable recommendations on social media using data mining rules.

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Section: Resultsmentioning

confidence: 99%

Data mining-based recommendation system using social networks—an analytical study

Ajmal

Awais

Khurshid

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

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“…Data is extracted from popular micro-blogging platform Twitter via the link https://apps.twitter.com/ using Tweepy API [35] credentials, python web scrapping techniques and libraries without any search keywords or specific topics of interest. In this study, around 40k raw microblog without any user information are extracted into a spreadsheet and processed for data analysis.…”

Section: A Dataset and Pre-processingmentioning

confidence: 99%

T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT

Palani,

Rajagopal,

Pancholi

2021

Preprint

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Sentiment analysis (SA) has become an extensive research area in recent years impacting diverse fields including ecommerce, consumer business, and politics, driven by increasing adoption and usage of social media platforms. It is challenging to extract topics and sentiments from unsupervised short texts emerging in such contexts, as they may contain figurative words, strident data, and co-existence of many possible meanings for a single word or phrase, all contributing to obtaining incorrect topics. Most prior research is based on a specific theme/rhetoric/focused-content on a clean dataset. In the work reported here, the effectiveness of BERT(Bidirectional Encoder Representations from Transformers) in sentiment classification tasks from a raw live dataset taken from a popular microblogging platform is demonstrated. A novel T-BERT framework is proposed to show the enhanced performance obtainable by combining latent topics with contextual BERT embeddings. Numerical experiments were conducted on an ensemble with about 42000 datasets using NimbleBox.ai platform with a hardware configuration consisting of Nvidia Tesla K80(CUDA), 4 core CPU, 15GB RAM running on an isolated Google Cloud Platform instance. The empirical results show that the model improves in performance while adding topics to BERT and an accuracy rate of 90.81% on sentiment classification using BERT with the proposed approach.

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“…Following the long history of well-studied event detection, lots of various techniques have been proposed to extract/detect events from contexts, articles and transcripts, a significant amount of which have investigated datasets from social media. For example, Lwowski et al [16] uses emotions presented in Tweets for a given city merely using text-based contents. Similar projects are presented across various social media platforms and online information resources such as Facebook, Google Plus [17], Twitter [18] as well as news articles [19] to name but a few.…”

Section: A Event Detection Across All Domainsmentioning

confidence: 99%

Implicit Life Event Discovery From Call Transcripts Using Temporal Input Transformation Network

et al. 2019

Self Cite

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Customer-agent conversations (i.e. call transcripts) are invaluable source for companies as they convey direct information from their customers implicit and explicit behaviour. Identifying customerrelated events is an important task in customer services which is possible from the call transcripts. However, call centers produces a vast amount of transcripts which makes the manual or semi-manual processing of such raw datasets quite challenging. Furthermore, customer-agent call transcripts tend not to explicitly denote events that might be beneficial to customer services. Albeit being highly researched across multiple domains in the literature, event detection, especially implicit life event detection have not been well examined from call transcripts due to a lack of proper large-scale dataset. In this research, we propose a novel deep learning approach based on latent topic modeling and deep recurrent neural networks with memory units to automatically detect implicit events from a customer's history of call transcripts. These implicit events are detected prior to the report date of that event thereby not containing any explicit topic/feature. We provide a case study on a real-life, large-scale data of more than 800K call transcripts from a large financial services company in the U.S. to examine the practical features and challenges of this problem. The evaluation results demonstrate the potential applicability of our implicit life event detection as it achieves a macro-recall score of 53 (macro-f1 of 47.5) on a highly imbalanced test set, negative samples are 95% of the data. Our model beats the the state-of-the-art text classification benchmarks by macro-f1 score of 5.6 and macro-recall of 8.8 on average, and performs better than the ensemble of all single-document and sequential classification benchmarks albeit being significantly less complex. The comparison results show the importance as well as our model's capability of capturing the mutual information of a sequence of call transcripts in detecting the implicit life events. INDEX TERMS Implicit event discovery, call transcripts, deep learning, recurrent neural network, machine learning, natural language processing, text classification, topic modeling, event detection.

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Geospatial Event Detection by Grouping Emotion Contagion in Social Media

Cited by 16 publications

References 40 publications

Data mining-based recommendation system using social networks—an analytical study

Data mining-based recommendation system using social networks—an analytical study

T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT

Implicit Life Event Discovery From Call Transcripts Using Temporal Input Transformation Network

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