Detecting life events from twitter based on temporal semantic features

Khodabakhsh, Maryam; Kahani, Mohsen; Bagheri, Ebrahim; Noorian, Zeinab

doi:10.1016/j.knosys.2018.02.021

Cited by 21 publications

(8 citation statements)

References 18 publications

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“…In addition, Khodabakhsh et al [35] analyzed Twitter user behaviors associated with important events that have a temporary nature, offering useful insights for the analysis of these events on social networks. Similarly, to Khodabakhsh et al [35], Zhou et al [36] used sentiment analysis of tweets posted during large social events to identify the feelings related to user actions and communication published on those digital platforms. Following this line of research, Tsolmon et al [37] proposed a new method to study Twitter-based UGC associated with social events.…”

Section: Literature Reviewmentioning

confidence: 99%

Are Black Friday Deals Worth It? Mining Twitter Users’ Sentiment and Behavior Response

Saura

Reyes-Menéndez

Palos-Sánchez

2019

Journal of Open Innovation: Technology, Market, and Complexity

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The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area.

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Section: Literature Reviewmentioning

confidence: 99%

Are Black Friday Deals Worth It? Mining Twitter Users’ Sentiment and Behavior Response

Saura

Reyes-Menéndez

Palos-Sánchez

2019

Journal of Open Innovation: Technology, Market, and Complexity

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“…In this work, we use the ACE dataset 24 as a means to evaluate the proposed framework. We rely on the fact that ACE is currently the most popular and the most cited dataset for the EE task.…”

Section: Dataset and Evaluation Metricsmentioning

confidence: 99%

“…Its aim is to automatically extract specific knowledge of certain incidents identified in texts [4] in the form of who is involved, in what, at when and where [5]. This task can be very beneficial in a variety of domains including question answering [6,7], information retrieval [8], summarization [9][10][11][12], timeline extraction [13,14], news recommendation [15,16], knowledge base construction [9,17], and online monitoring systems such as ones for health, life, disease, cyber-attack, stock markets, accident and robbery [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Joint event extraction along shortest dependency paths using graph convolutional networks

Balali

Asadpour

Campos

et al. 2020

Knowledge-Based Systems

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“…The average number of monthly active users around the world on Twitter is 335 million. Users post tweets in several domains such as daily routine activities, life events, local and global news, tweets about the success of their favourite celebrities, death, and winning of awards [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

Malik

Aslam

et al. 2022

Computational Intelligence and Neuroscience

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People’s lives are influenced by social media. It is an essential source for sharing news, awareness, detecting events, people’s interests, etc. Social media covers a wide range of topics and events to be discussed. Extensive work has been published to capture the interesting events and insights from datasets. Many techniques are presented to detect events from social media networks like Twitter. In text mining, most of the work is done on a specific dataset, and there is the need to present some new datasets to analyse the performance and generic nature of Topic Detection and Tracking methods. Therefore, this paper publishes a dataset of real-life event, the Oscars 2018, gathered from Twitter and makes a comparison of soft frequent pattern mining (SFPM), singular value decomposition and k-means (K-SVD), feature-pivot (Feat-p), document-pivot (Doc-p), and latent Dirichlet allocation (LDA). The dataset contains 2,160,738 tweets collected using some seed words. Only English tweets are considered. All of the methods applied in this paper are unsupervised. This area needs to be explored on different datasets. The Oscars 2018 is evaluated using keyword precision (K-Prec), keyword recall (K-Rec), and topic recall (T-Rec) for detecting events of greater interest. The highest K-Prec, K-Rec, and T-Rec were achieved by SFPM, but they started to decrease as the number of clusters increased. The lowest performance was achieved by Feat-p in terms of all three metrics. Experiments on the Oscars 2018 dataset demonstrated that all the methods are generic in nature and produce meaningful clusters.

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Detecting life events from twitter based on temporal semantic features

Cited by 21 publications

References 18 publications

Are Black Friday Deals Worth It? Mining Twitter Users’ Sentiment and Behavior Response

Are Black Friday Deals Worth It? Mining Twitter Users’ Sentiment and Behavior Response

Joint event extraction along shortest dependency paths using graph convolutional networks

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

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