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%

Sentiment Analysis of Tribal, Religion, and Race with LIWC

2021 ◽  
Vol 9 (3) ◽  
pp. 443
Author(s):  
Prasetyo Adi Utomo ◽  
AAIN Eka Karyawati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Average Score ◽  
The Social ◽  
Religion And Race ◽  
Negative Sentiment ◽  
F Measure

During this pandemic, social media has become a major need as a means of communication. One of the social media used is Twitter by using messages referred to as tweets. In Indonesia itself, there are various tribes, religions, and races in their society so the use of these names is also become commonly used. However, sometimes, the use of the name is followed by 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 LIWC method that divides tweets into 3 classes of positive, neutral, and negative. From the sentiment analysis that has been performed, the average score for precision is 69.62%, recall is 70%, and f-measure is 69.81%

scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

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

Sentiment Analysis of Users’ Comments on Indian Hindi News Channels Using Mozdeh: An Evaluation Based on YouTube Videos

2021 ◽  
pp. 097325862110492
Author(s):  
Maya Deori ◽  
Manoj Kumar Verma ◽  
Vinit Kumar
Keyword(s):  
Social Media ◽  
Time Series ◽  
Sentiment Analysis ◽  
The Political ◽  
Political News ◽  
The Social ◽  
Youtube Videos ◽  
The Common ◽  
Negative Sentiment ◽  
Word Frequencies

Sentiment analysis is the channel to pick out the text from the social media dataset to inquire about the positive and negative opinions of the statement and its subjective and objectiveness. The purpose of the study is to manifest the sentiment of the text posted in five Hindi news channels, that is, AajTak, ABP News, India TV, NDTV India and Republic Bharat on YouTube are investigated by adopting the Mozdeh software to highlight the sequential temperament of the viewers by evaluating the positive and negative sentiments. The present study is subsequently limited to the data being extracted and evaluated by the software Mozdeh. The sentiments of each Hindi news channel are analysed along with the top word frequencies and displaying the time-series graph. The investigation presents that the channel with maximum average positive average negative sentiment belongs to India TV and the female category was in the peak compared to male altogether but the unidentified gender was the highest. During the time series analysis, the year 2020 was seen to be the most productive year since all the spikes were precisely detected. The audience of these channels turns out to be more attentive towards the political and entertainment news world. The study also highlights the tendency and interest of the common audiences to watch the news which dedicates that people are usually unsatisfied with the content being displayed in the videos and the common concentration is mostly based on the political news.

scholarly journals Using Social Media in Tourist Sentiment Analysis: A Case Study of Andalusia during the Covid-19 Pandemic

2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Cost Savings ◽  
Mass Scale ◽  
Tourism Sector ◽  
The Social ◽  
Analytical Tools ◽  
Media Data

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

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

Sentiment Analysis for the Social Media

2017 ◽  
Author(s):  
Elif Uysal ◽  
Semih Yumusak ◽  
Kasim Oztoprak ◽  
Erdogan Dogdu
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
The Social

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.

scholarly journals Sentiments and Perceptions of Business Respondents on Social Media: an Exploratory Analysis

2015 ◽  
Vol 31 (2) ◽  
pp. 283-304 ◽  
Author(s):  
Vanessa Torres van Grinsven ◽  
Ger Snijkers
Keyword(s):  
Social Media ◽  
Social Structure ◽  
Media Studies ◽  
Exploratory Analysis ◽  
Statistical Institute ◽  
The People ◽  
The Social ◽  
Survey Participation ◽  
Business Survey ◽  
Negative Sentiment

Abstract The perceptions and sentiments of business respondents are considered important for statistical bureaus. As perceptions and sentiments are related to the behavior of the people expressing them, gaining insights into the perceptions and sentiments of business respondents is of interest to understand business survey response. In this article we present an exploratory analysis of expressions in the social media regarding Statistics Netherlands. In recent years, social media have become an important infrastructure for communication flows and thus an essential network in our social structure. Within that network participants are actively involved in expressing sentiments and perceptions. The results of our analysis provide insights into the perceptions and sentiments that business respondents have of this national statistical institute and specifically its business surveys. They point towards the specific causes that have led to a positive or a negative sentiment. Based on these results, recommendations aimed at influencing the perceptions and sentiments will be discussed, with the ultimate goal of stimulating survey participation. We also suggest recommendations regarding social media studies on sentiments and perceptions of survey respondents.

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