Identifying and Tracking Sentiments and Topics from Social Media
            Texts during Natural Disasters

Yang, Min; Mei, Jincheng; Ji, Heng; Zhao, Wei; Zhao, Zhou; Chen, Xiaojun

doi:10.18653/v1/d17-1055

Cited by 16 publications

(8 citation statements)

References 23 publications

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“…Some approaches such as dynamic joint sentiment-topic model [33], neighbourhood based influence propagation model, and location-based dynamic sentiment-topic models [34] have been proposed to capture the evolution of the sentiment of topics. Sentiment time series have been used to track sentiments of public topics and make predictions [8], for example, work in the literature proposed a location sentiment evolution model to track the sentiment of topics in natural disasters [34], while Si et al [8] proposed a continuous Dirichlet process mixture model to learn the daily topic set and build sentiment time series for each topic, and then used it to predict the stock market. Some systems such as [3], [14] have been proposed to visualise the t is dependent on the sentiment at the previous timepoint, X t −1 .…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

2020

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Sentiment analysis is one of the key tasks of natural language understanding. Sentiment Evolution models the dynamics of sentiment orientation over time. It can help people have a more profound and deep understanding of opinion and sentiment implied in user generated content. Existing work mainly focuses on sentiment classification, while the analysis of how the sentiment orientation of a topic has been influenced by other topics or the dynamic interaction of topics from the aspect of sentiment has been ignored. In this paper, we propose to construct a Gaussian Process Dynamic Bayesian Network to model the dynamics and interactions of the sentiment of topics on social media such as Twitter. We use Dynamic Bayesian Networks to model time series of the sentiment of related topics and learn relationships between them. The network model itself applies Gaussian Process Regression to model the sentiment at a given time point based on related topics at previous time. We conducted experiments on a real world dataset that was crawled from Twitter with 9.72 million tweets. The experiment demonstrates a case study of analysing the sentiment dynamics of topics related to the event Brexit. INDEX TERMS Sentiment analysis, sentiment evolution, dynamic Bayesian network, social media, temporal dynamics.

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Section: Related Workmentioning

confidence: 99%

A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

2020

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“…Yang et al [31] suggest a location-based dynamic sentiment-topic model (LDST) to make use of the geographic information of Tweets directly in the topic model. They apply the LDST to a dataset of approx.…”

Section: Lda For Disaster Detection In Tweetsmentioning

confidence: 99%

Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping

et al. 2020

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In the event of a natural disaster, geo-tagged Tweets are an immediate source of information for locating casualties and damages, and for supporting disaster management. Topic modeling can help in detecting disaster-related Tweets in the noisy Twitter stream in an unsupervised manner. However, the results of topic models are difficult to interpret and require manual identification of one or more “disaster topics”. Immediate disaster response would benefit from a fully automated process for interpreting the modeled topics and extracting disaster relevant information. Initializing the topic model with a set of seed words already allows to directly identify the corresponding disaster topic. In order to enable an automated end-to-end process, we automatically generate seed words using older Tweets from the same geographic area. The results of two past events (Napa Valley earthquake 2014 and hurricane Harvey 2017) show that the geospatial distribution of Tweets identified as disaster related conforms with the officially released disaster footprints. The suggested approach is applicable when there is a single topic of interest and comparative data available.

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“…Giachanou et al [19] attempted to utilize conventional time series analysis techniques, such as frequency analysis, outlier detection and time series decomposition, to track sentiment in social media. Yang et al [20] designed a location-based dynamic sentiment-topic model which can jointly model topic, sentiment, time and geolocation information, with an attempt to track sentiment shifts in different geographical regions. Giachanou et al [21] proposed to leverage time series outlier detection technique for sentiment spike identification, and combine LDA and relative entropy to extract the topics and compute their contribution to the sentiment spikes.…”

Section: A Sentiment Evolution Analysismentioning

confidence: 99%

Exploiting Long-Term Dependency for Topic Sentiment Analysis

Huang

Yuan

et al. 2020

IEEE Access

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Most existing unsupervised approaches to detect topic sentiment in social texts consider only the text sequences in corpus and put aside social dynamics, as leads to algorithm's disability to discover true sentiment of social users. To address the issue, a probabilistic graphical model LDTSM (Long-term Dependence Topic-Sentiment Mixture) is proposed, which introduces dependency distance and uses the dynamics of social media to achieve the perfect combination of inheriting historical topic sentiment and fitting topic sentiment distribution underlying in current social texts. Extensive experiments on real-world SinaWeibo datasets show that LDTSM significantly outperforms JST, TUS-LDA and dNJST in terms of sentiment classification accuracy, with better inference convergence, and topic and sentiment evolution analysis results demonstrate that our approach is promising.

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Identifying and Tracking Sentiments and Topics from Social Media Texts during Natural Disasters

Cited by 16 publications

References 23 publications

A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping

Exploiting Long-Term Dependency for Topic Sentiment Analysis

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