The Power of Social Media Analytics: Text Analytics Based on Sentiment Analysis and Word Clouds on R

Abstract

Apparently, word clouds have grown as a clear and appealing illustration or visualization strategy in terms of text. Word clouds are used as a part of various settings as a way to give a diagram by cleansing text throughout those words that come up with most frequently. Generally, this is performed constantly as an unadulterated text outline. In any case, that there is a bigger capability to this basic yet intense visualization worldview in text analytics. In this work, we investigate the adequacy of word clouds for general text analysis errands and also analyze the tweets to find out the sentiment and also discuss the legal aspects of text mining. We used R software to pull twitter data which depends altogether on word cloud as a visualization technique and also with the help of positive and negative words to determine the user sentiment. We indicate how this approach can be viably used to explain text analysis tasks and assess it in a qualitative user research.

Business Open Big Data Analytics to Support Innovative Leadership and Management Decision in Canada

“…big data) frame to support targeted and agile marketing and management strategies (Parsons & Lepkowski-White, 2018;Zahoor & Qureshi, 2017;Kunz et al, 2016). As a result, this literature review has interwoven the state of big data analytics and crowdsourcing through social media in relation to the benefits and challenges (Bhimani et al, 2018;Kabir, Karim, Newaz, & Hossain, 2018;Steiger, Matzler, 2012). This reaffirmed the current state of many organizations in luxury markets in Asia, online service or rating conglomerates that are in their infancy in social media realms and utilize at least one method of crowdsourcing whether from online community narratives, responsive two-way feedback mechanisms, frequency data analytics from platforms such as Facebook, Twitter or Instagram and the like (Liu, Burns, & Hou, 2017; Escobar-Rodriguez, Grávalos-Gastaminza, & Pérez-Calañas, 2017; Colliander, Dahlen, & Modig, 2015).…”

“…Customer relations managers and their respective departments, as highlighted previously, should have an infrastructure that could help enact strategy which may include tools such as text or word recognition applications that could work within social media mediums and help support efficiencies in the data analytics processes (Kabir, Karim, Newaz, & Hossain, 2018;Liau & Tan, 2014).Within a co-value or creation framework, customers could help with the process of analysis in that their feedback can inadvertently act as triangulation mechanisms by confirming, questioning, sharing frustrations, and making recommendations which can be used as part of the understanding and assessment connected to customer relations managers portfolios or core responsibilities (Parsons & Lepkowski-White, 2018).…”

Wearable Devices for Health and Wellbeing: Design Insights from Twitter

“…Sentiment analysis is an emerging area of Natural Language Processing (NLP) with research extending from document level characterization to taking in the boundary of words and phrases [19]. In addition, the emotion analysis feature provides an additional layer of contextual analysis.…”

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“…Inflammatory processes in the lung tissue lead to the pulmonary circulation disorders, which subsequently cause heart failure and a depression of myocardial contractility. Increased cardiac load leads to increased myocardial oxygen demand, which can be an inducer of cardiomyocytes apoptosis, especially in respiratory failure [2][3][4][5][6]. That is why pneumonia is associated with heart dysfunction both during and after illness [7].…”