Attitudes Evaluation Toward COVID-19 Pandemic: An Application of Twitter Sentiment Analysis and Latent Dirichlet Allocation

Shurrab, Saeed; Shannak, Yazan; Almshnanah, Abdulkarem; Khazaleh, Huthaifa; Najadat, Hassan

doi:10.1109/icics52457.2021.9464558

Cited by 4 publications

(7 citation statements)

References 19 publications

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“…The examination of sentiment during crucial events has gained considerable traction due to its efficacy in extracting valuable insights. Previous academic research has delved into the sentiment analysis of Twitter content during various disasters and crises [3]. The MF-LDA model demonstrates reduced perplexity and increased coverage rate.…”

Section: Iiliterature Riviewmentioning

confidence: 99%

“…On the whole, the utilization of social media presents myriad advantages in grasping public sentiments regarding occurrences, product appraisals, and governmental regulations. Its instantaneous nature, extensive user base, and analytical proficiencies render it an invaluable instrument for enterprises, policymakers, and analysts striving to obtain insights into public opinion and choices [3]. Twitter functions as a potent social media platform for extracting public sentiments, opinions, and gaining insights into ongoing events or crises through user-generated content like tweets and videos.…”

Section: Iintroductionmentioning

confidence: 99%

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Twitter Trending Topics Analysis Using Power BI on Real-Time Basis

Ranjan

2024

IJSREM

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In today’s world of growing social media usage twitter has become an enormous source of data from public in form of tweets. These tweets can be collect and useful to extract effective meaningful insights ex, public sentiments, choices and opinions about a particular event, product or person, which can prove helpful for business growth, political parties and celebrities to know public choices, sentiments and their reviews over a particular product , person or government decision. But before performing sentiment analysis on tweets data we need to clean and per-process it as tweets data is highly unstructured and noisy for that many methods are there:-data per-processing, stop-words removal, stemming, lemmatization etc. After data cleaning effective meaningful insights can be extracted and sentiment analysis can be performed to extract public opinions and their choices. Twitter data possesses significant power in capturing timely public opinions on a variety of topics, such as preferences for products, political tendencies, and sentiments within the business realm. The extensive user base of this platform provides a wide array of viewpoints, rendering it a valuable resource for comprehending consumer behaviour, political developments, and market outlooks. By employing sentiment analysis, organizations can measure levels of customer contentment, policymakers can evaluate public reception, and corporations can monitor how their brands are perceived to make well-informed decisions. The real-time aspect and high level of user involvement associated with Twitter data render it priceless for shaping strategies related to product innovation, political campaigns, and business expansion efforts. This research paper highlights the power of power BI tool in data visualization of real-time tweets of a user twitter account and builds effective dashboards and also perform sentiment analysis of real-time tweets. Keywords: LDA, Machine Learning, Topic Modelling, Sentiment Analysis, NLP, Social Media.

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Section: Iiliterature Riviewmentioning

confidence: 99%

Section: Iintroductionmentioning

confidence: 99%

Twitter Trending Topics Analysis Using Power BI on Real-Time Basis

Ranjan

2024

IJSREM

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“…Topic modelling greatly facilitates the extraction of trending topics in real-time, thereby providing users with immediate insights into ongoing discussions and emerging themes. This capability is of immense value to businesses, journalists, and researchers seeking to remain up-to-date with current events and public sentiments [9].…”

Section: Real-time Insightsmentioning

confidence: 99%

Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

Ranjan

2024

IJSREM

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As far as social media is concerned twitter has become a major source of public data in the form of tweets. Twitter is an emerging source of large textual data(big data).People can easily express their opinions , reviews, interests and tastes about a particular event, topic ,product etc., occurring worldwide. This makes twitter a good source of valuable data which can be further used to perform sentiment analysis, know trending topics of public intrests and public opinion on a particular product or event which can be beneficial for business growth and political parties to know public choices and they can take actions accordingly. The extraction of meaningful insights from the vast and dynamic stream of social media data is greatly facilitated by the implementation of topic modeling and sentiment analysis on Twitter tweets. The identification of recurring themes or subjects within a collection of tweets, which is the essence of topic modeling, serves to reveal patterns in discussions, thereby aiding in the comprehension of prevalent topics and trends. Concurrently, sentiment analysis, through the evaluation of the emotional tone of tweets, enables the discernment of whether the expressed sentiments are positive, negative, or neutral. The combination of these techniques provides researchers and businesses with valuable perspectives on public opinions, emerging issues, and user sentiments, thereby empowering them to make informed decisions and develop effective engagement strategies in the ever-evolving landscape of social media. But twitter data(tweets) are unstructured ,contains noisy data, urls , stop words, re-tweets video ,emoji etc., which need to be cleaned and preprocessed to perform proper sentiment analysis and use it to extract meaningful effective insights from it. This paper focuses on various methods of topic modeling and discovering latent topics ,text-mining approaches ,micro-blogging methods used in various researches .This paper focuses on latent dirichlet allocation method of topic modeling and text-mining to discover latent topics in tweets, micro-blogging ,text-mining approaches .A proper survey is done on previous researches on this topic in this article. Keywords:-LDA, text-mining, topic modeling, micro-blogging ,machine learning ,sentiment analysis, NLP.

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“…People's opinions about various topics obtained from LDA topic modeling are summarized using abstractive summarizers (14) . Two summarizers used are T5 summarizer and Pipeline.…”

Section: Summarizationmentioning

confidence: 99%

Deep Sentiment Classification using Topic Modeling for Covid-19

Velvizhy,

Deepa,

Priya

2023

IJST

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Objectives: To extract various topics related to Covid-19 from Twitter API using LDA topic modelling technique and to analyse the sentiment of the people about the extracted topics. An interactive Q/A system with both voice and text interface is also proposed to guide COVID-19 related decision-making. And also to summarize the tweets containing a query and to suggest suitable solutions. Method: The proposed extracts Covid-19 related tweets from twitter API and uses Natural Language Process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. The training dataset consists of 3,38,666 COVID 19 related comments and the testing dataset consists of 1,12,888 comments. LSTM recurrent neural network is used for sentiment analysis of the extracted tweets and to produce summary for each topic identified through topic modelling. Findings: The accuracy comparison has been done for the existing system against the proposed model with respect various machine learning classifiers. The findings are-LSTM gives an accuracy of 79.5%, the Naïve Bayes classifier gives the accuracy of 74%, the Multinomial Naïve Bayes gives an accuracy of 74.5%, whereas the linear regression classifier achieves an accuracy of 76%, KNN classifier achieves an accuracy of 74.5% and the random forest with an accuracy of 75.5%. Novelty: The proposal of interactive Question Answering system is first of its kind. This work sheds light on the importance of using public opinions and suitable computational techniques to understand issues surrounding Covid 19 and to guide related decision-making.

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Attitudes Evaluation Toward COVID-19 Pandemic: An Application of Twitter Sentiment Analysis and Latent Dirichlet Allocation

Cited by 4 publications

References 19 publications

Twitter Trending Topics Analysis Using Power BI on Real-Time Basis

Twitter Trending Topics Analysis Using Power BI on Real-Time Basis

Real-Time Twitter Trends Analysis Using Latent Dirichlet Allocation and Machine Learning

Deep Sentiment Classification using Topic Modeling for Covid-19

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