Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis

Öztürk, Nazan; Ayvaz, Serkan

doi:10.1016/j.tele.2017.10.006

Cited by 230 publications

(113 citation statements)

References 32 publications

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“…With the advent of social media, information related to various issues started going viral. Dealing with this flow has become an indispensable societal daily routine [163]. Moreover, social media creates new ways for people from various communities to engage with each other [164].…”

Section: Social Mediamentioning

confidence: 99%

“…Similarly, many other researchers use the information available on Twitter to make stock market predictions [167][168][169][170][171][172][173][174][175]. Öztürk and Ayvaz [163] studied Turkish and English tweets for evaluating their sentiments towards the Syrian refugee crisis and found that Turkish tweets are remarkably different from English tweets [163]. A study on the Arabic Twitter feed is proposed by Alkhatib et al [176] with the objective of offering a novel framework for events and incidents management in smart [176].…”

Section: Twittermentioning

confidence: 99%

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Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Social Mediamentioning

confidence: 99%

Section: Twittermentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

View full text Add to dashboard Cite

show abstract

“…Sentiment analysis is to extract and quantify subjective information including the status of attitudes, emotions and opinions from a variety of contents such as texts, images and audios [47]. Sentiment analysis has been drawing great attentions because of its wide applications in business and government intelligence, political science, sociology and psychology [2,3,13,33]. From a technical perspective, textual sentiment analysis is first explored by researchers as an NLP task.…”

Section: Related Work 21 Sentiment Analysismentioning

confidence: 99%

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Chen

Yuan

You

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

134

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Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.

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“…In the context of leveraging the information found online for HADR emergencies, approaches for languages other than English have been limited. Most of which are done by manually constructing resources for a particular language (e.g., in tweets [31]- [33] and in disaster-related news coverage [34]), or by applying cross-language text categorization to build languagespecific models [32], [35].…”

Section: Previous Workmentioning

confidence: 99%

A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPA's LORELEI project

Martínez¹,

Ramakrishna²,

Chiu³

et al. 2019

2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)

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During the course of a Humanitarian Assistance-Disaster Relief (HADR) crisis, that can happen anywhere in the world, real-time information is often posted online by the people in need of help which, in turn, can be used by different stakeholders involved with management of the crisis. Automated processing of such posts can considerably improve the effectiveness of such efforts; for example, understanding the aggregated emotion from affected populations in specific areas may help inform decisionmakers on how to best allocate resources for an effective disaster response. However, these efforts may be severely limited by the availability of resources for the local language. The ongoing DARPA project Low Resource Languages for Emergent Incidents (LORELEI) aims to further language processing technologies for low resource languages in the context of such a humanitarian crisis. In this work, we describe our submission for the 2019 Sentiment, Emotion and Cognitive state (SEC) pilot task of the LORELEI project. We describe a collection of sentiment analysis systems included in our submission along with the features extracted. Our fielded systems obtained the best results in both English and Spanish language evaluations of the SEC pilot task.

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Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis

Cited by 230 publications

References 32 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPA's LORELEI project

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