Towards a Sentiment Analysis of Tweets from Online Newspapers Regarding the Coronavirus Pandemic

In the last year, both offline and online news have had the Coronavirus pandemic as their subject, especially social networking Twitter has significantly increased the news regarding Covid-19. The objectives of the project are: the analysis of news regarding the Coronavirus pandemic extracted from the Twitter profile of ANSA, a well-known Italian news agency and the analysis of sentiment and the number of likes for each news extracted The sentiment analysis has been carried out using the MAL lexicon (Morphologically Affective Lexicon), where the tweet is split into words and each paola is associated with a score. Positive (with a score greater than zero), negative (with a score less than zero) and neutral (with a score equal to zero) news were identified. As a result, it emerges that the sentiment changed day by day, so it is necessary to use sentiment indicators called indices, but only the positive sentiment index is taken into consideration as the negative one is complementary and the neutral one is almost zero. The positive index is then related to some parameters extrapolated from the Civil Protection site: number of cases, number of deaths and entry into intensive care. Furthermore, in addition to the parameters listed above, the positivity index is related to the days in which the decrees of the Prime Minister (DPCM) were signed. The last relationship analyzed is that between the average number of likes and the number of deaths. The results of the research shows that the sentiment of the news of the Ansa Agency contains 62.3% of positive news, 37.3% of negative news and only 0.3% of neutral news. Furthermore, sentiment is not influenced by the daily parameters: number of cases, number of deaths, entry into intensive care units and DPCMs. But there is a relationship between the average of like and the number of deaths. Doi: 10.28991/HIJ-2021-02-04-08 Full Text: PDF

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Entity Profiling to Identify Actor Involvement in Topics of Social Media Content

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ◽

10.22146/ijccs.59869 ◽

2020 ◽

Vol 14 (4) ◽

pp. 417

Author(s):

Puji Winar Cahyo ◽

Muhammad Habibi

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Topic Modeling ◽

Latent Dirichlet Allocation ◽

Online News ◽

Support Vector ◽

Media Content ◽

Positive Sentiment ◽

Negative Sentiment

The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.

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Study of Sentiment of Governor's Election Opinion in 2018

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841124 ◽

2018 ◽

pp. 231-238

Author(s):

Agung Eddy Suryo Saputro ◽

Khairil Anwar Notodiputro ◽

Indahwati A

Keyword(s):

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Addition Method ◽

Sentiment Mining ◽

Positive Sentiment ◽

ICERD in Malaysian online news reports: A cross-linguistic comparative corpus analysis

International Journal of Modern Languages And Applied Linguistics ◽

10.24191/ijmal.v3i4.7724 ◽

2019 ◽

Vol 3 (4) ◽

pp. 51

Author(s):

Siti Aeisha Joharry ◽

Nor Diyana Saupi

Keyword(s):

Comparative Analysis ◽

Public Discourse ◽

Online News ◽

Corpus Analysis ◽

Search Term ◽

International Convention ◽

Online Newspapers ◽

News Reports ◽

The Media ◽

Online Sources

The International Convention for the Elimination of Racial Discrimination (ICERD), which was not ratified in Malaysia, created a heated public discourse in the media. This cross-linguistic comparative study investigates the representation of ICERD in Malaysian news reports of two online sources in Malaysia – the widely read English portal: The Star Online, and its Malay equivalent: Berita Harian. A corpus-assisted discourse analysis was conducted to examine how news on ‘ICERD’ were reported in both English and Malay online newspapers. Initial comparative analysis of both newspapers revealed that the search term co-occurs statistically more frequently with the verb ‘ratify’ and its equivalent: ‘meratifikasi’. Patterns indicate that ‘ICERD’ was mostly referring to the act of sanctioning the agreement –particularly to ‘not ratify’ or ‘tidak akan meratifikasi’, which is concurrent with the timeframe of events. Interestingly, different patterns can be found in Berita Harian (e.g. the expression of ‘thanks’ or gratitude of not ratifying ICERD) that are not as revealing in The Star Online reports. Some inconsistencies were also reported between the two newspapers, e.g. referring to different ministers’ speech about the initial plan to ratify ICERD alongside five (The Star Online) or six (Berita Harian) other treaties in the following year.

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KNN classifier based approach for multi-class sentiment analysis of twitter data

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.12656 ◽

2018 ◽

Vol 7 (3) ◽

pp. 1372

Author(s):

Soudamini Hota ◽

Sudhir Pathak

Keyword(s):

Sentiment Analysis ◽

Opinion Mining ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Online News ◽

Classification Algorithm ◽

Sentiment Classification ◽

Supervised Machine Learning ◽

K Nearest Neighbor ◽

News Reports

‘Sentiment’ literally means ‘Emotions’. Sentiment analysis, synonymous to opinion mining, is a type of data mining that refers to the analy-sis of data obtained from microblogging sites, social media updates, online news reports, user reviews etc., in order to study the sentiments of the people towards an event, organization, product, brand, person etc. In this work, sentiment classification is done into multiple classes. The proposed methodology based on KNN classification algorithm shows an improvement over one of the existing methodologies which is based on SVM classification algorithm. The data used for analysis has been taken from Twitter, this being the most popular microblogging site. The source data has been extracted from Twitter using Python’s Tweepy. N-Gram modeling technique has been used for feature extraction and the supervised machine learning algorithm k-nearest neighbor has been used for sentiment classification. The performance of proposed and existing techniques is compared in terms of accuracy, precision and recall. It is analyzed and concluded that the proposed technique performs better in terms of all the standard evaluation parameters.

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Julkinen kaivoskeskustelu diskursiivisena välienselvittelynä

Alue ja Ympäristö ◽

10.30663/ay.65574 ◽

2018 ◽

Vol 47 (1) ◽

pp. 79-103 ◽

Cited By ~ 1

Author(s):

Veera Kangaspunta

Keyword(s):

Discourse Analysis ◽

Critical Discourse Analysis ◽

Online News ◽

Public Debate ◽

Critical Discourse ◽

Online Newspapers ◽

The Public ◽

Analytical Tools ◽

Unique Part

The aim of this article is to approach one specific environmental topic and the public debate around this topic from a user-oriented perspective – through online news comments. The article analyses online news and comments sections from three Finnish online newspapers concerning the mining accident of Talvivaara company in November 2012. Discourse and discursive legitimation strategies are used as analytical tools with the focus of critical discourse analysis. The study aims to solve what kind of discourses the public debate contains and how these discourses are connected to certain legitimation strategies. In addition, the article also continues the conceptual deliberation about the concept of the public as a group of people participating in public discussion. The study shows that Talvivaara news and news comments consist four main strategies, authorization, rationalization, moral evaluations and mythopoiesis, used for legitimation, relegitimation and delegitimation. However, the parties differ in the way they utilize these strategies and different discourses. Consequently, online news commenting appears as a unique part of the public debate about the topic, rather than remaining marginal flaming. The users tend to absorb the role of the public as a part of the public showdown about the shared issue.

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Analysis of Social Media Users Sentiments against Omnibus Law Based on Hashtags on Twitter

SISTEMASI ◽

10.32520/stmsi.v11i1.1685 ◽

2022 ◽

Vol 11 (1) ◽

pp. 197

Author(s):

Okta Fanny ◽

Heri Suroyo

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Main Topic ◽

Accuracy Score ◽

Test Results ◽

Bayes Classifier ◽

The Public ◽

Average Accuracy ◽

Positive Sentiment ◽

Negative Sentiment

From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

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Sentiment Analysis through Machine Learning

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.56.3.32 ◽

2021 ◽

Vol 56 (3) ◽

pp. 384-393

Author(s):

Md. Abbas Ali Khan ◽

Ali-Emran ◽

Md. Alamgir Kabir ◽

Mohammad Hanif Ali ◽

A. K. M. Fazlul Haque

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Short Distance ◽

The Internet ◽

Text Document ◽

Ride Sharing ◽

Wheeled Vehicle ◽

The People ◽

Day By Day ◽

Rainy Days

In recent years, App-Based Transportation System (ABTS) like Ride Sharing (Uber, Patho) has become popular day by day. For our daily life, a rickshaw (a 3-wheeled vehicle usually for one or two passengers that one man pulls) is most important for a short distance. If we add this vehicle to our ABTS system, it will be very much helpful for us, specifically for the rainy season in Bangladesh. On heavy rainy days, in our city Dhaka, other vehicles like CNG, cars, and bikes become unused because roads go underwater. However, the man who pulled the rickshaw can serve this condition. It is more important than the conventional rickshaw is unable to provide such service properly. In this regard, we are proposing an App-Based Rickshaw (ABR), which is convenient to get over distance through the internet. To do this, we have collected data through close questionnaires’ from several types of people. In contrast, collected data are based on a text document. So our aim is to Sentiment Analysis (SA) of the people through machine learning and checks the feasibility of applicability in the real world.

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Best Practices of News and Media Web Design

International Journal of Business Analytics ◽

10.4018/ijban.2018100103 ◽

2018 ◽

Vol 5 (4) ◽

pp. 43-60 ◽

Cited By ~ 1

Author(s):

Sonya Zhang ◽

Samuel Lee ◽

Karen Hovsepian ◽

Hannah Morgia ◽

Kelli Lawrence ◽

...

Keyword(s):

Design Patterns ◽

Web Design ◽

Online News ◽

Online Newspapers ◽

Four Dimensions ◽

Business News ◽

Tv News ◽

Social Sharing ◽

Sports News ◽

Content Design

As more print media move to online, news and media websites have evolved with increasing complexity in content, design, and monetization strategies. In this article, the authors examined and reported the web design patterns of 150 leading news and media websites in six different categories: TV news, online newspapers, online magazines, and technology news, sports news, and business news, using 28 analytics metrics in four dimensions: content structure, multimedia, social sharing, and advertising placements.

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Mining social media data to investigate patient perceptions regarding DMARD pharmacotherapy for rheumatoid arthritis

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-217333 ◽

2020 ◽

Vol 79 (11) ◽

pp. 1432-1437 ◽

Cited By ~ 1

Author(s):

Chanakya Sharma ◽

Samuel Whittle ◽

Pari Delir Haghighi ◽

Frada Burstein ◽

Roee Sa'adon ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Social Media ◽

Side Effects ◽

Sentiment Analysis ◽

Computer Algorithms ◽

Social Media Platforms ◽

Synthetic Agents ◽

Positive Sentiment ◽

Negative Sentiment ◽

Media Data

ObjectivesWe hypothesise that patients have a positive sentiment regarding biological/targeted synthetic disease modifying anti-rheumatic drugs (b/tsDMARDs) and a negative sentiment towards conventional synthetic agents (csDMARDs). We analysed discussions on social media platforms regarding DMARDs to understand the collective sentiment expressed towards these medications.MethodsTreato analytics were used to download all available posts on social media about DMARDs in the context of rheumatoid arthritis. Strict filters ensured that user generated content was downloaded. The sentiment (positive or negative) expressed in these posts was analysed for each DMARD using sentiment analysis. We also analysed the reason(s) for this sentiment for each DMARD, looking specifically at efficacy and side effects.ResultsComputer algorithms analysed millions of social media posts and included 54 742 posts about DMARDs. We found that both classes had an overall positive sentiment. The ratio of positive to negative posts was higher for b/tsDMARDs (1.210) than for csDMARDs (1.048). Efficacy was the most commonly mentioned reason in posts with a positive sentiment and lack of efficacy was the most commonly mentioned reason for a negative sentiment. These were followed by the presence/absence of side effects in negative or positive posts, respectively.ConclusionsPublic opinion on social media is generally positive about DMARDs. Lack of efficacy followed by side effects were the most common themes in posts with a negative sentiment. There are clear reasons why a DMARD generates a positive or negative sentiment, as the sentiment analysis technology becomes more refined, targeted studies could be done to analyse these reasons and allow clinicians to tailor DMARDs to match patient needs.

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Use of telecritical care for family visitation to ICU during the COVID-19 pandemic: an interview study and sentiment analysis

BMJ Quality & Safety ◽

10.1136/bmjqs-2020-011604 ◽

2020 ◽

pp. bmjqs-2020-011604

Author(s):

Farzan Sasangohar ◽

Atiya Dhala ◽

Feibi Zheng ◽

Nima Ahmadi ◽

Bita Kash ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Sentiment Analysis ◽

Family Members ◽

Automated Analysis ◽

Tertiary Hospital ◽

Care Team ◽

Patient Status ◽

Physical Presence ◽

Inductive Thematic Analysis

BackgroundWhen the COVID-19 pandemic restricted visitation between intensive care unit patients and their families, the virtual intensive care unit (vICU) in our large tertiary hospital was adapted to facilitate virtual family visitation. The objective of this paper is to document findings from interviews conducted with family members on three categories: (1) feelings experienced during the visit, (2) barriers, challenges or concerns faced using this service, and (3) opportunities for improvements.MethodsFamily members were interviewed postvisit via phone. For category 1 (feelings), automated analysis in Python using the Valence Aware Dictionary for sentiment Reasoner package produced weighted valence (extent of positive, negative or neutral emotive connotations) of the interviewees’ word choices. Outputs were compared with a manual coder’s valence ratings to assess reliability. Two raters conducted inductive thematic analysis on the notes from these interviews to analyse categories 2 (barriers) and 3 (opportunities).ResultsValence-based and manual sentiment analysis of 230 comments received on feelings showed over 86% positive sentiments (88.2% and 86.8%, respectively) with some neutral (7.3% and 6.8%) and negative (4.5% and 6.4%) sentiments. The qualitative analysis of data from 57 participants who commented on barriers showed four primary concerns: inability to communicate due to patient status (44% of respondents); technical difficulties (35%); lack of touch and physical presence (11%); and frequency and clarity of communications with the care team (11%). Suggested improvements from 59 participants included: on demand access (51%); improved communication with the care team (17%); improved scheduling processes (10%); and improved system feedback and technical capabilities (17%).ConclusionsUse of vICU for remote family visitations evoked happiness, joy, gratitude and relief and a sense of closure for those who lost loved ones. Identified areas for concern and improvement should be addressed in future implementations of telecritical care for this purpose.

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