A Literature Review on Twitter Data Analysis

Anber, Hana; Salah, Akram; El-Aziz, A. A. Abd

doi:10.17706/ijcee.2016.8.3.241-249

Cited by 32 publications

(11 citation statements)

References 10 publications

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“…We identify this approach as the "engagement analysis". The unstructured data allows exploring "qualitatively" the user-generated content to monitor and detect trends [48,53]. The trends can be combined with the structured data, in particular the temporal data (i.e., the date of creation of tweets) to investigate the evolution of these trends over time.…”

Section: Methodsmentioning

confidence: 99%

From Sustainable Development Goals to Sustainable Cities: A Social Media Analysis for Policy-Making Decision

et al. 2021

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The United Nations (UN) adopted the seventeen “Sustainable Development Goals” (SDGs) in early September 2015. One of these goals is SDG 11, which refers to the sustainable cities and communities. In this context, local governments face the challenge of aligning with this objective. As a result, they are increasing outreach to their organizational boundaries to involve citizens in policy making and strategy development, continually listening to citizens’ voices. One of the methods citizens use to express themselves is social media. This paper will emphasize social media platforms and specially Twitter to explore the public discourse about cities in the context of SDG 11. We applied descriptive quantitative and qualitative analysis to analyze the tweets that include terms and hashtags referring to the SDG 11. The data analysis process is composed of three major procedures: 1-Engagement analysis, 2-Trends based analysis and 3-Data Insights. Our results show that: 1-the COVID’19 pandemic negatively impacted users engagement towards SDG 11, 2-new technologies such AI and IoT are gaining more importance to help cities reach SDG 11, and 3-the SDGs are related and one SDG can impact other SDGs.

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Section: Methodsmentioning

confidence: 99%

From Sustainable Development Goals to Sustainable Cities: A Social Media Analysis for Policy-Making Decision

et al. 2021

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“…Tweets extraction was in strait a span variable from 3 to 12 months by altogether completely different authors. Tweet is associate unstructured data, that has to be filtered by mistreatment varied tongue method techniques like stemming, stop word and noun removal [7] to urge the useful data.…”

Section: Related Workmentioning

confidence: 99%

Social Communications using Big Data Applications

Prakashkumar¹,

Beschi²

2019

IJITEE

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Social computing is interactive and collaborative behavior between computer users. Personal computing is an individual user activity in that one user generally commands computing. In social computing, the Internet allows users to interact through many mediums, including: Social computing is basically the use of a computer for social purposes. Before the Internet, computers were largely used as tools for increasing productivity. The Internet introduced a social element where users could network, share interests, publish personal insights and use their computers for more than just doing a job faster. Social computing can still benefit businesses synergistically if it is used for business purposes. Social computing can be used to market products and promote customer relations. Online marketing and viral marketing are two types of promotional advertising that have grown out of social computing.

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“…The relationship in this scenario is either guided or unguided. The enormous data supplied by this microblogging platform, such as tweet messages, user information, and the number of followers/followings in the network, serve an important role in data analysis, prompting most studies to explore and evaluate the various interpretation methods to acquire the most recently used innovations [ 9 ]. One such method to analyze textual data to get the polarity of the emotions expressed is Sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis

Modi

Shah

et al. 2022

Ann. Data. Sci.

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In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.

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A Literature Review on Twitter Data Analysis

Cited by 32 publications

References 10 publications

From Sustainable Development Goals to Sustainable Cities: A Social Media Analysis for Policy-Making Decision

From Sustainable Development Goals to Sustainable Cities: A Social Media Analysis for Policy-Making Decision

Social Communications using Big Data Applications

Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis

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