Localized twitter opinion mining using sentiment analysis

Hridoy, Syed Akib Anwar; Ekram, M. Tahmid; Islam, Mazharul; Ahmed, Faysal; Rahman, Rashedur M.

doi:10.1186/s40165-015-0016-4

Cited by 57 publications

(13 citation statements)

References 9 publications

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“…While surveys play an irreplaceable role, there is another way of collecting public opinion about disease outbreaks: the increasing popularity of social media and the development of text analysis techniques have given rise to mining social media data. Unlike surveys that can mainly collect only a limited volume of data and may not be able to reflect the real thoughts of the public because of some influential factors during the survey process, such as the environmental distraction, subject's psychological pressure, and so on (Hridoy, Ekram, Islam, Ahmed, & Rahman, 2015), social media data, which is the collection of short texts posted online to express one's feelings at any time, are generated on a large scale. Thus, the efficient use of social media data can contribute largely to the research about public opinion.…”

Section: Introductionmentioning

confidence: 99%

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

Xie

Chu

Chiu

et al. 2021

Data and Information Management

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It is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and “self-media,” together contribute to the information spread of positive sentiment.

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Section: Introductionmentioning

confidence: 99%

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

Xie

Chu

Chiu

et al. 2021

Data and Information Management

View full text Add to dashboard Cite

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“…As stated by Anwar Hridoy et al [46], social media data are one of the most effective and accurate indicators for studying public sentiment. After the disaster, Twitter was full of people's opinions and comments, and we analyzed public sentiment based on these data.…”

Section: Discussionmentioning

confidence: 99%

Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan

Zhang

Cheng

2021

IJGI

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The public’s attitudes, emotions, and opinions reflect the state of society to a certain extent. Understanding the state and trends of public sentiment and effectively guiding the direction of sentiment are essential for maintaining social stability during disasters. Social media data have become the most effective resource for studying public sentiment. The TextBlob tool is used to calculate the sentiment value of tweets, and this research analyzed the public’s sentiment state during Typhoon Haiyan, used the biterm topic model (BTM) to classify topics, explored the changing process of public discussion topics at different stages during the disaster, and analyzed the differences in people’s discussion content under different sentiments. We also analyzed the spatial pattern of sentiment and quantitatively explored the influencing factors of the sentiment spatial differences. The results showed that the overall public sentiment during Typhoon Haiyan tended to be positive, that compared with positive tweets, negative tweets contained more serious disaster information and more urgent demand information, and that the number of tweets, population, and the proportion of the young and middle-aged populations were the dominant factors in the sentiment spatial differences.

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“…The classical approaches classify the source of information, identify the main information (twitter, website, etc. ), which is used to infer information [12,13,14,15,16,17]. We extend it with the knowledge graph, which has a great potential in different contexts, for example, for the analysis of textual citizens' contributions, the general attitudes-sentiments (positive, negative or neutral) of the public in the discussion, among other things.…”

Section: Discussionmentioning

confidence: 99%

Opinion mining using a knowledge extraction system from the web

Rodriguez¹,

Aguilar²,

González³

2017

ces

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This paper designs and implements an opinion mining system based on the knowledge extraction from unstructured documents in HTML format. Basically, the system recovers information from the Internet about a subject, and transforms it in a structured and organized knowledge, described semantically in an ontology, using a Semantic Ontology. Therefore, the Semantic Ontology describes the opinion about this subject previously defined. This ontology is a semantic knowledge based on the extraction of entities and relationships, where entities are something about we can say something, and the relationships are the interactions between the entities. From this model of semantic knowledge, it is possible to infer the public opinion, and new knowledge, such as the terminological base around of the opinion.

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Localized twitter opinion mining using sentiment analysis

Cited by 57 publications

References 9 publications

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

Temporal and Spatial Evolution and Influencing Factors of Public Sentiment in Natural Disasters—A Case Study of Typhoon Haiyan

Opinion mining using a knowledge extraction system from the web

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