Twitter Usage Across Industry: A Spatiotemporal Analysis

Gupta, Neha; Crosby, Henry; Purser, David; Javis, Stephen; Guo, Weisi

doi:10.1109/bigdataservice.2018.00018

Cited by 10 publications

(8 citation statements)

References 21 publications

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“…Zhang et al, 2015). Multiple researches compared the two, and some of these researches compared them to other approaches including decision trees (DT) and for the same previous aims; (Alamsyahl et al, 2018;Anastasia & Budi, 2016;Giancristofaro et al, 2016;Gupta et al, 2018;Rane & Kumar, 2018;Z. Zhang, Zhang, et al, 2018;Zhang, Chen, et al, 2018) used multiple classifiers to compare their results in analysing commuters' sentiment toward transportation related topics, while (D'Andrea et al, 2015;Gal-Tzur et al, 2018;Hoang et al, 2016;Kuflik et al, 2017;Tse et al, 2016) used different machine learning techniques to identify the posts related to a transportation topic.…”

Section: S6-rq1: What Are the Datasets Used By Researchers?mentioning

confidence: 99%

Investigating transportation research based on social media analysis: a systematic mapping review

et al. 2021

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Social media is a pool of users' thoughts, opinions, surrounding environment, situation and others. This pool can be used as a real-time and feedback data source for many domains such as transportation. It can be used to get instant feedback from commuters; their opinions toward the transportation network and their complaints, in addition to the traffic situation, road conditions, events detection and many others. The problem is in how to utilize social media data to achieve one or more of these targets. A systematic review was conducted in the field of transportation-related research based on social media analysis (TRR-SMA) from the years between 2008 and 2018; 74 papers were identified from an initial set of 703 papers extracted from 4 digital libraries. This review will structure the field and give an overview based on the following grounds: activity, keywords, approaches, social media data and platforms and focus of the researches. It will show the trend in the research subjects by countries, in addition to the activity trends, platforms usage trend and others. Further analysis of the most employed approach (Lexicons) and data (text) will be also shown. Finally, challenges and future works are drawn and proposed.

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Section: S6-rq1: What Are the Datasets Used By Researchers?mentioning

confidence: 99%

Investigating transportation research based on social media analysis: a systematic mapping review

et al. 2021

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“…The data collection is a process of gathering information on variables of interests through multiple online and offline available sources, such as the Internet, vehicular, satellite, base station, user devices, government and business databases, and sensors. The combined data may vary in sparsity and resolution across urban and industrial areas [46]. For the Internet data, the providers provide Application Programming Interfaces (API) for the third parties to access the open data.…”

Section: ) Datamentioning

confidence: 99%

A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning

Guo

Zhang

2020

IEEE Access

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In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capital and operational expenditure. Proactive network optimisation is widely acknowledged as one of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area.INDEX TERMS Online data, data analytics, proactive network optimisation, 5G.

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“…Sentiment lexica provided us with the objectivity or subjectivity of matched words present in the lexica and we provisioned four lexicas for this study; Bing Liu opinion lexicon [8], the MPQA subjectivity lexicon [10], AFINN [11] and SentiWordNet [12]. We then used a logistic regression classifier to arrive at machine learning sentiment score (positive, negative and neutral) by incorporating the methodology adopted in our previous research [13]. For the psycholinguistics analysis, the tweets were read into the R environment where they underwent a data cleaning process.…”

Section: B Tweets Sentiment Scoringmentioning

confidence: 99%

“…The other end of the scale was labelled completely happy, pleased, satisfied, or contented. (see [13] page 5 for details). This detailed scale anchoring enables a more complete valence rating that clearly defines positive sentiment as opposed to scales that use only happiness as anchoring points.…”

Section: B Tweets Sentiment Scoringmentioning

confidence: 99%

Triangulated Sentiment Analysis of Tweets for Social CRM

Griesser

Gupta

2019

2019 6th Swiss Conference on Data Science (SDS)

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Twitter Usage Across Industry: A Spatiotemporal Analysis

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 10 publications

References 21 publications

Investigating transportation research based on social media analysis: a systematic mapping review

Investigating transportation research based on social media analysis: a systematic mapping review

A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning

Triangulated Sentiment Analysis of Tweets for Social CRM

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