scholarly journals Analysis of User Sentiment of Twitter to Draft KUHP

2020 ◽  
Vol 16 (3) ◽  
pp. 273
Author(s):  
Nawang Indah Cahyaningrum ◽  
Danty Welmin Yoshida Fatima ◽  
Wisnu Adi Kusuma ◽  
Sekar Ayu Ramadhani ◽  
Muhammad Rizqi Destanto ◽  
...  
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
The Other ◽  
Mining Methods ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Bahasa Indonesia

Twitter is one of social media where its user can share many responses for a phenomenon through a tweet. This research used 5000 tweets from Twitter users in Bahasa Indonesia with keyword “RUU KUHP(Draft Law of KUHP)” from 16th of September until 22nd of September 2019. That tweets were processed using Rstudio software with sentiment analysis that is one of Text Mining methods. This research aims to classify Twitter users’ responses to RUU KUHP to be negative sentiment, poisitive negative, and neutral. Also, this research also aims to know about topics’ frequencies that were related to RUU KUHP through visualization with bar plot and also wordcloud. This research also aims to know words that are associated with the most frequent words. Form this research, can be known that Twitter users’ responses to RUU KUHP tend to have neutral sentiment that means they did not take side between agreeing or disagreeing. From this research, also can be known about 10 most frequent words, there are kpk, tunda, dpr, pasal, kesal, jokowi, presiden, masuk, ya, and sahkan. Beside that, can be known the other words that are associated with them and also their probability.

scholarly journals Sentiment Analysis of West Java International Airport (Bijb) Kertajati on Twitter

2020 ◽  
Vol 4 (2) ◽  
pp. 176-182
Author(s):  
Oka Intan ◽  
Sri Widiyanesti
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Rapid Development ◽  
West Java ◽  
Word Cloud ◽  
Twitter User ◽  
The Social ◽  
Twitter Users ◽  
Negative Sentiment

The rapid development of technology allows everything to accessed by the internet that causes many users of social media and one of the social media is Twitter. An interesting topic to discuss on Twitter is about new and fresh things that attract many users to get involved. One of the things that attract Twitter users is the construction of a new airport, namely Kertajati Airport, which has some problems with airport activities, such as the small number of visitors, lonely conditions of the airport, and decreased number of routes. This study aims to find out Twitter user sentiments towards Kertajati Airport in West Java to know the quality of Kertajati Airport. The method used in this study is sentiment analysis by looking at the calculation of how many positive and negative sentiment have been obtained with the most result so it can reflect the quality of Kertajati Airport and then there is a word cloud to see the spread of word related to sentiment. The results of this study indicate that the quality of the Kertajati Airport cannot be said to be good because the results of the sentiment analysis found that negative sentiments have more percentages than positive sentiments

A Perspective of Electronic Cigarette Users on the COVID-19 Pandemic in Twitter: Observational Study (Preprint)

2020 ◽  
Author(s):  
Yankun Gao ◽  
Zidian Xie ◽  
Dongmei Li
Keyword(s):  
Social Media ◽  
Immune Responses ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Electronic Cigarette ◽  
Virus Infections ◽  
Twitter Data ◽  
The Us ◽  
Twitter Users ◽  
Negative Sentiment

BACKGROUND Previous studies indicated electronic cigarette users might be more vulnerable to COVID-19 infections and could develop more severe symptoms once contracted COVID-19 due to their impaired immune responses to virus infections. Social media has been widely used to express users’ responses to the COVID-19 pandemic. OBJECTIVE We aimed to examine the responses of electronic cigarette Twitter users to the COVID-19 pandemic using Twitter data. METHODS The COVID-19 dataset contained COVID-19-related Twitter posts (tweets) between March 5th, 2020 and April 3rd, 2020. Ecig group included Twitter users who didn’t have commercial accounts but ever retweeted e-cigarette promotion posts between May 2019 and August 2019. Twitter users who didn’t post or retweet any e-cigarette-related tweets were defined as Non-Ecig group. Sentiment analysis was conducted to compare sentiment scores towards the COVID-19 pandemic between both groups. Topic modeling was used to compare the main topics discussed between the two groups. RESULTS The US COVID-19 dataset consisted of 1,112,558 COVID-19-related tweets from 15,657 unique Twitter users in the Ecig group and 9,789,584 COVID-19-related tweets from 2,128,942 unique Twitter users in the Non-Ecig group. Sentiment analysis showed that the Ecig group have more negative sentiment scores than the Non-Ecig group. Results from topic modeling indicated the Ecig group had more concern about COVID-19 related death, while the Non-Ecig group cared more about the government’s responses to the COVID-19 pandemic. CONCLUSIONS Electronic cigarette Twitter users has more concern towards the COVID-19 pandemic. Twitter is a useful tool to timely monitor public responses to the COVID-19 pandemic.

The Effects of Different Kernels in SVM Sentiment Analysis on Mass Social Distancing

2020 ◽  
Vol 9 (2) ◽  
pp. 161
Author(s):  
Komang Dhiyo Yonatha Wijaya ◽  
Anak Agung Istri Ngurah Eka Karyawati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Kernel Method ◽  
Idle Time ◽  
Linear Kernel ◽  
Social Distancing ◽  
The Social ◽  
Negative Sentiment ◽  
F Measure ◽  
Kernel Yield

During this pandemic, social media has become a major need as a means of communication. One of the social medias used is Twitter by using messages referred to as tweets. Indonesia currently undergoing mass social distancing. During this time most people use social media in order to spend their idle time However, sometimes, this result in negative sentiment that used to insult and aimed at an individual or group. To filter that kind of tweets, a sentiment analysis was performed with SVM and 3 different kernel method. Tweets are labelled into 3 classes of positive, neutral, and negative. The experiments are conducted to determine which kernel is better. From the sentiment analysis that has been performed, SVM linear kernel yield the best score Some experiments show that the precision of linear kernel is 57%, recall is 50%, and f-measure is 44%

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

SISTEMASI ◽  
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

scholarly journals PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

2022 ◽  
Vol 10 (4) ◽  
pp. 583-593
Author(s):  
Syiva Multi Fani ◽  
Rukun Santoso ◽  
Suparti Suparti
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Virtual Networks ◽  
Number Of Clusters ◽  
Silhouette Coefficient ◽  
Twitter Account ◽  
Computer Based ◽  
Twitter Users ◽  
Clustering Data ◽  
Coefficient Method

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

Web Text Mining

2016 ◽  
Author(s):  
Ricardo Baeza-Yates ◽  
Roi Blanco ◽  
Malú Castellanos
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Web Search ◽  
Internet Users ◽  
Entity Retrieval ◽  
Web Text Mining ◽  
Text Content ◽  
The Web

Web search has become a ubiquitous commodity for Internet users. This fact puts a large number of documents with plenty of text content at our fingertips. To make good use of this data, we need to mine web text. This triggers the two problems covered here: sentiment analysis and entity retrieval in the context of the Web. The first problem answers the question of what people think about a given product or a topic, in particular sentiment analysis in social media. The second problem addresses the issue of solving certain enquiries precisely by returning a particular object: for instance, where the next concert of my favourite band will be or who the best cooks are in a particular region. Where to find these objects and how to retrieve, rank, and display them are tasks related to the entity retrieval problem.

Sentiment Classification

2015 ◽  
pp. 1-26
Author(s):  
Jalel Akaichi
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Training Model ◽  
Sentiment Classification ◽  
The Other ◽  
Support Vector ◽  
Analysis Techniques ◽  
Sentiment Lexicon ◽  
And Behavior

In this work, we focus on the application of text mining and sentiment analysis techniques for analyzing Tunisian users' statuses updates on Facebook. We aim to extract useful information, about their sentiment and behavior, especially during the “Arabic spring” era. To achieve this task, we describe a method for sentiment analysis using Support Vector Machine and Naïve Bayes algorithms, and applying a combination of more than two features. The output of this work consists, on one hand, on the construction of a sentiment lexicon based on the Emoticons and Acronyms' lexicons that we developed based on the extracted statuses updates; and on the other hand, it consists on the realization of detailed comparative experiments between the above algorithms by creating a training model for sentiment classification.

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.

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

2020 ◽  
Vol 79 (11) ◽  
pp. 1432-1437 ◽  
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.

Exploring user sentiment towards sponsorship and ambush marketing

2019 ◽  
Vol 20 (4) ◽  
pp. 583-602 ◽  
Author(s):  
Nick Burton
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Value Added ◽  
Statistical Modelling ◽  
Consumer Attitudes ◽  
Content Type ◽  
Event Marketing ◽  
Ambush Marketing ◽  
Ethics Research ◽  
Twitter Users

Purpose The purpose of this paper is to explore consumer attitudes towards ambush marketing and official event sponsorship through the lens of sentiment analysis, and to examine social media users' ethical responses to digital event marketing campaigns during the 2018 FIFA World Cup. Design/methodology/approach The study employed a sentiment analysis, examining Twitter users’ utilization of sponsor and non-sponsor promotional hashtags. Statistical modelling programme R was used to access Twitter’s API, enabling the analysis and coding of user tweets pertaining to six marketing campaigns. The valence of each tweet – as well as the apparent user motivation underlying each post – was assessed, providing insight into Twitter users’ ethical impressions of sponsor and ambush marketer activities on social media and online engagement with social media marketing. Findings The study’s findings indicate that consumer attitudes towards ambush marketing may be significantly more positive than previously thought. Users’ attitudes towards ambush marketing appear significantly more positive than previously assumed, as users of social media emerged as highly responsive to creative and value-added non-sponsor campaigns. Originality/value The findings affirm that sentiment analysis may afford scholars and practitioners a viable means of assessing consumer attitudes towards social marketing activations, dependent upon campaign objectives and strategy. The study provides a new and invaluable context to consumer affect and ambush ethics research, advancing sponsorship and ambush marketing delivery and social sponsorship analytical practice.

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