Opinion mining in online social media: a survey

Messaoudi, Chaima; Guessoum, Zahia; Romdhane, Lotfi

doi:10.1007/s13278-021-00855-8

Cited by 48 publications

(22 citation statements)

References 45 publications

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“…Due to enormous improvements in information and communication technology, data analytics has become a necessary tool for improving company operations and an influential and successful tool in designing corporate goals to target client expectations. Opinion mining, on the other hand, is relevant to domains other than business, such as government and politics (Messaoudi et al, 2022). Opinion mining has recently gained significant momentum and interest among researchers because it may be applied in various fields.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Twitter is a kind of digital environment that helps to shape a new social structure. This communication network has over a billion logins and millions of active users who post 500 million tweets daily (Messaoudi et al, 2022). Twitter users may transmit information known as "tweets," with each tweet having a character restriction of 140 characters until October 2018 and 280 characters presently (Santos et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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The Good, the Bad, and the Neutral: Twitter Users’ Opinion on the ASUU Strike

Muhammad

Nat

2022

InformingSciJ

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Aim/Purpose: Nigeria’s university education goes through incessant strikes by the Academic Staff Union of Universities (ASUU). This strike has led to shared emotion on micro-blogging sites like Twitter. This study analyzed selected historical tweets from the “ASUU” to understand citizens’ opinions. Background: The researchers conducted sentiment analysis and topic modelling to understand Twitter users’ opinions on the strike. Methodology: The researchers used the Valence Aware Dictionary for Sentiment Reasoning (VADER) technique for sentiment analysis, and the Latent Dirichlet allocation (LDA) was used for topic modelling. A total of 10,000 tweets were first extracted for the study. After data cleaning, 1323 tweets were left. Contribution: To the researcher’s best knowledge, no published study has presented a sentiment analysis on the topic of the ASUU strike using the Twitter dataset. This research will fill this gap by providing a sentiment analysis and drawing out subjects by exploring the tweets on the phrase “ASUU.” Findings: The sentiment analysis result using VADER returned 567 tweets as ‘Negative,’ with the remaining 544 and 212 categorized as Positive and Neutral. The result of the LDA returned six topics, all comprising seven keywords. The topics were the solution to the strike, ASUU strike effect, strike Call-off, appeal to ASUU, student protest and student appeal. Recommendation for Researchers: Researchers can use this study’s findings to compare with other contexts of opinion mining. Practitioners may also use the research to understand better the attitudes of their staff and students about the strikes to create actionable solutions before the suspension of the strike. Future Research: Future studies can collect information from other social networking and blogging sites.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The Good, the Bad, and the Neutral: Twitter Users’ Opinion on the ASUU Strike

Muhammad

Nat

2022

InformingSciJ

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“…The influence of social media on people’s lives and attitudes has been widely studied and established in many different perspectives (Messaoudi et al. 2022 ; Fu et al. 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…The influence of social media on people's lives and attitudes has been widely studied and established in many different perspectives (Messaoudi et al 2022;Fu et al 2020). Although social media is a broad term, it mainly refers to Facebook, Twitter, Reddit, Instagram, and YouTube.…”

Section: Introductionmentioning

confidence: 99%

Detection of dangerous events on social media: a critical review

Jamil

Pais

Cordeiro

2022

Soc. Netw. Anal. Min.

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The usability of the events information on social media has been widely studied recently. Several surveys have reviewed the specific type of events on social media using various techniques. Most of the existing methods for event detection are segregated as they approach certain situations that limit the overall details of events happening consecutively on social media while ignoring the crucial relationship between the evolution of these events. Numerous events that materialize on the social media sphere every day before our eyes jeopardize people’s safety and are referred to by using a high-level concept of dangerous events. The front of dangerous events is broad, yet no known work exists that fully addresses and approaches this issue. This work introduces the term dangerous events and defines its scope in terms of practicality to establish the origins of the events caused by the previous events and their respective relationship. Furthermore, it divides dangerous events into sentiment, scenario, and action-based dangerous events grouped on their similarities. The existing research and methods related to event detection are surveyed, including some available events datasets and knowledge-base to address the problem. Finally, the survey is concluded with suggestions for future work and possible related challenges.

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“…In recent years, social media have made a huge source of free unlimited information available. This information can be used to explore user perceptions and inform future decisions [ 10 , 21 , 47 , 53 , 59 ], however, interpreting information from social media is not trivial [ 28 , 30 ]. In addition, if we are to contribute to the increase in adherence to games for health, it is important to gauge user perceptions not only in terms of usability and user experience (UX), but also in terms of health-related aspects.…”

Section: Introductionmentioning

confidence: 99%

An opinion mining methodology to analyse games for health

Silva

Santos

2022

Multimed Tools Appl

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Despite the positive impact of games for health on players’ health, users tend to stop playing them after a short period of time, leading benefits to fade. It is therefore important to understand how to sustain interest and, in this way, preserve the health benefits of games for health. This could be achieved by continuously reviewing user feedback after product launch and using this information to inform (re)design and better address user needs. With the growth of social media, user opinions became widely available in public forums. This abundance of information affords us the possibility of, through the application of natural language processing and sentiment analysis techniques, tapping into user opinions and automatically analysing and extracting knowledge from them. This paper introduces a methodology that analyses user comments posted on YouTube about the Just Dance game, to automatically extract information about Usability, User Experience (UX), and Perceived Health Impacts related to Quality of Life (H-QoL). In doing so, the methodology uses a pre-established vocabulary, based on the English lexicon and its semantic relations, to annotate the presence of 38 concepts (five of Usability, 18 of UX, and 15 of H-QoL) and to analyse sentiment. The results of the information extraction and processing are displayed on a dashboard that allows for the exploration and browsing of the results, which can be useful to better understand the opinions and impacts perceived by users and to inform the (re)design of games for health. The methodology proposed builds upon over 500,000 user comments collected from over 32,000 videos.

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Opinion mining in online social media: a survey

Cited by 48 publications

References 45 publications

The Good, the Bad, and the Neutral: Twitter Users’ Opinion on the ASUU Strike

The Good, the Bad, and the Neutral: Twitter Users’ Opinion on the ASUU Strike

Detection of dangerous events on social media: a critical review

An opinion mining methodology to analyse games for health

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