scholarly journals A Nation in Crisis... in Three Acts

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
William Senn
Keyword(s):  
Sentiment Analysis ◽  
Thematic Analysis ◽  
Text Analysis ◽  
Detailed Examination ◽  
Spoken Word ◽  
Press Conference ◽  
Search Terms ◽  
The Hours ◽  
The Rose

A dramatic, spoken-word performance based on a detailed examination of the text of more than 1.7 million tweets concerning coronavirus and covid that were sent during the hours surrounding the president's declaration of emergency on March 13, 2020 including the transcript of the remarks made at the Rose Garden press conference. The researcher used the Twitter JSON API to retrieve all of the tweets containing the search terms "covid" and "corona" occurring during the timeframe. A text analysis was performed to identify the most frequently occurring n-grams present in the corpus of tweets. Thematic analysis and sentiment analysis were used to categorize the tweets. The transcript of the Rose Garden press conference remarks was separately analyzed using the same techniques.

scholarly journals Unpacking the Quantifying and Qualifying Potential of Semi-Open Job Satisfaction Questions through Computer-Aided Sentiment Analysis

2021 ◽  
Author(s):  
Indy Wijngaards ◽  
Martijn Burger ◽  
Job van Exel
Keyword(s):  
Job Satisfaction ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Discriminant Validity ◽  
Added Value ◽  
Information Richness ◽  
Convergent And Discriminant Validity ◽  
Computer Aided ◽  
Satisfaction Question ◽  
Open Question

AbstractDespite their suitability for mitigating survey biases and their potential for enhancing information richness, open and semi-open job satisfaction questions are rarely used in surveys. This is mostly due to the high costs associated with manual coding and difficulties that arise when validating text measures. Recently, advances in computer-aided text analysis have enabled researchers to rely less on manual coding to construct text measures. Yet, little is known about the validity of text measures generated by computer-aided text analysis software and only a handful of studies have attempted to demonstrate their added value. In light of this gap, drawing on a sample of 395 employees, we showed that the responses to a semi-open job satisfaction question can reliably and conveniently be converted into a text measure using two types of computer-aided sentiment analysis: SentimentR, and Linguistic Inquiry and Word Count (LIWC) 2015. Furthermore, the substantial convergence between the LIWC2015 and, in particular, SentimentR measure with a closed question measure of job satisfaction and logical associations with closed question measures of constructs that fall within and outside job satisfaction’s nomological network, suggest that a semi-open question has adequate convergent and discriminant validity. Finally, we illustrated that the responses to our semi-open question can be used to fine-tune the computer-aided sentiment analysis dictionaries and unravel antecedents of job satisfaction.

What is the impact of staff wearing uniforms in psychiatric inpatient settings?

2021 ◽  
Author(s):  
Sophie Collingwood ◽  
Laura McKenzie-Smith
Keyword(s):  
Mental Health ◽  
Thematic Analysis ◽  
Negative Impact ◽  
Psychiatric Inpatient ◽  
Ideal Self ◽  
Search Terms ◽  
Mental Health Settings ◽  
Inpatient Settings ◽  
The Impact

Background: Uniform has traditionally been worn in psychiatric inpatient and other mental health settings, but there has been a move to non-uniform in recent years. Some services have made the change back to uniform, raising questionsabout the potential impact on patients and staff.Aim: To review the impact of uniform within a psychiatric inpatient or mental health setting.Method: Databases were searched for articles exploring the impact of uniform use using specified search terms. Articles were assessed for suitability with inclusion and exclusion criteria, critically appraised, then analysed for themes using thematic analysis.Results: 17 papers were included in the review. Thematic analysis identified five main themes and 29 subthemes. Main themes were Attitudes and interactions, A freer environment, Are you both nurses?, The ‘ideal self’ and There are more important things. A critical appraisal of the articles suggested issues with validityand reliability, which are discussed.Discussion: Studies identified that wearing non-uniform facilitated positive changes in both patients and staff. This raises the potential negative impact of uniform on both patients and staff, and the role of power imbalance in these settings is discussed. Further themes around identification of staff out of uniform were considered.Implications for practice: The use of uniform in mental health and psychiatric inpatient settings should be considered carefully, due to the potential negative impact, whilst also recognising the importance of staff identification and supporting professional identity.

Malaysian vs Philippines News Report

2018 ◽  
Vol 2 (2) ◽  
pp. 47
Author(s):  
Wan Faizatul Azirah Ismayatim ◽  
Sridevi Sriniwasss ◽  
Nadiah Thanthawi Jauhari
Keyword(s):  
Critical Discourse Analysis ◽  
Thematic Analysis ◽  
Text Analysis ◽  
Prime Minister ◽  
Systemic Functional Linguistics ◽  
Critical Discourse ◽  
Main Process ◽  
Controversial Issues ◽  
Security Forces ◽  
Lexical Cohesion

This paper reports on a study on Experiential meaning particularly the main process types used in the reporting of the airstrike event launched by Malaysian security forces on March 5, 2013 during the intrusion of “Sulu Sultan” followers in Lahad Datu. Data for the study comprised text reports pertinent to the airstrike event published in four different English newspapers which are The News Straits Times (NST), The Star (TS), The Philippine Daily Inquirer (TPDI) and The Philippine Star (TPS). A total of 8 texts were analysed. Various methods have been developed to study newspapers representation and stance of controversial issues which include content analysis, critical discourse analysis, lexical cohesion, the use of metaphors, transitivity and thematic analysis among others. However, the framework of transitivity has not been widely used. Hence, Systemic Functional Linguistics (SFL), in particular, the System of Transitivity propounded by M.A.K. Halliday (1994) was used to bridge the gap in research and the methodology of text analysis was deployed. The study revealed that NST was the only newspaper which highlighted the sorrow and the grief of Malaysians and its Prime Minister in which this newspaper accounts for the most in employing the Mental Processes, while TS, TPDI and TPS highlighted more on the physical actions and the resoluteness of both countries in handling the Lahad Datu conflict when Material Processes were dominant in these newspapers.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

A Study and Comparison of Sentiment Analysis Techniques Using Demonetization

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Krishna Kumar Mohbey ◽  
Brijesh Bakariya ◽  
Vishakha Kalal
Keyword(s):  
Sentiment Analysis ◽  
Text Analysis ◽  
Analytical Approach ◽  
Document Type ◽  
Analysis Techniques

Sentiment analysis is an analytical approach that is used for text analysis. The aim of sentiment analysis is to determine the opinion and subjectivity of any opinion, review, or tweet. The aim of this chapter is to study and compare some of the techniques used to classify opinions using sentiment analysis. In this chapter, different techniques of sentiment analysis have been discussed with the case study of demonetization in India during 2016. Based on the sentiment analysis, people's opinion can be classified on different polarities such as positive, negative, or neutral. These techniques will be classified on different categories based on size of data, document type, and availability. In addition, this chapter also discusses various applications of sentiment analysis techniques in different domains.

A Survey on Sentiment Analysis Techniques for Twitter

2022 ◽  
pp. 57-90
Author(s):  
Surabhi Verma ◽  
Ankit Kumar Jain
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Analysis Techniques ◽  
Goods And Services ◽  
Text Document ◽  
The Subject ◽  
Over Time

People regularly use social media to express their opinions about a wide variety of topics, goods, and services which make it rich in text mining and sentiment analysis. Sentiment analysis is a form of text analysis determining polarity (positive, negative, or neutral) in text, document, paragraph, or clause. This chapter offers an overview of the subject by examining the proposed algorithms for sentiment analysis on Twitter and briefly explaining them. In addition, the authors also address fields related to monitoring sentiments over time, regional view of views, neutral tweet analysis, sarcasm detection, and various other tasks in this area that have drawn the researchers ' attention to this subject nearby. Within this chapter, all the services used are briefly summarized. The key contribution of this survey is the taxonomy based on the methods suggested and the debate on the theme's recent research developments and related fields.

Network Text Analysis and Sentiment Analysis

2016 ◽  
pp. 137-153
Author(s):  
Veronica Ravaglia ◽  
Luca Zanazzi ◽  
Elvis Mazzoni
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Social Networking Sites ◽  
Word Of Mouth ◽  
Network Text Analysis ◽  
Online Conversations ◽  
Web Forums

Through Social Media, like social networking sites, wikis, web forums or blogs, people can debate and influence each other. Due to this reason, the analysis of online conversations has been recognized to be relevant to organizations. In the chapter we introduce two strategic tools to monitor and analyze online conversations, Sentiment Text Analysis (STA) and Network Text Analysis (NTA). Finally, we propose one empirical example in which these tools are integrated to analyze Word-of-Mouth regarding products and services in the Digital Marketplace.

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2018 ◽  
Vol 9 (2) ◽  
pp. 111-120
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

Emotion Analysis from Human Voice Using Various Prosodic Features and Text Analysis

2020 ◽  
Vol 17 (9) ◽  
pp. 4244-4247
Author(s):  
Vybhav Jain ◽  
S. B. Rajeshwari ◽  
Jagadish S. Kallimani
Keyword(s):  
Emotion Recognition ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Good Accuracy ◽  
Age Groups ◽  
Speech Emotion Recognition ◽  
Prosodic Features ◽  
Voice Analysis ◽  
Emotion Analysis ◽  
Human Voice

Emotion Analysis is a dynamic field of research with the aim to provide a method to recognize the emotions of a person only from their voice. It is more famously recognized as the Speech Emotion Recognition (SER) problem. This problem has been studied upon from more than a decade with results coming from either Voice Analysis or Text Analysis. Individually, both these methods have shown a good accuracy up till now. But, the use of both of these methods in unison has showed a much more better result than either one of those parts considered individually. When different people of different age groups are talking, it is important to understand their emotions behind what they say as this will in turn help us in reacting better. To try and achieve this, the paper implements a model which performs Emotion Analysis based on both Tone and Text Analysis. The prosodic features of the tone are analyzed and then the speech is converted to text. Once the text has been extracted from the speech, Sentiment Analysis is done on the extracted text to further improve the accuracy of the Emotion Recognition.

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