scholarly journals Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences

2019 ◽  
Vol 11 (16) ◽  
pp. 4459 ◽  
Author(s):  
Vasile-Daniel Păvăloaia ◽  
Elena-Mădălina Teodor ◽  
Doina Fotache ◽  
Magdalena Danileţ
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Emotional Reactions ◽  
User Preferences ◽  
Communication Strategy ◽  
Customer Relationship ◽  
Beverage Industry ◽  
Different Types

Any brand’s presence on social networks has a significant impact on emotional reactions of its users to different types of posts on social media (SM). If a company understands the preferred types of posts (photo or video) of its customers, based on their reactions, it could make use of these preferences in designing its future communication strategy. The study examines how the use of SM technology and customer-centric management systems could contribute to sustainable business development of companies by means of social customer relationship management (sCRM). The two companies included in the study provide a general consumer good in the beverage industry. As such, it may be said that users interacting with the posts these companies make on their official channels are in fact customers or potential customers. The study aims to analyze customer reaction to two types of posts (photos or videos) on six social networks: Facebook, Twitter, Instagram, Pinterest, Google+ and Youtube. It brings evidence on the differences and similarities between the SM customer behaviors of two highly competitive brands in the beverage industry. Drawing on current literature on SM, sCRM and marketing, the output of this study is the conceptualization and measurement of a brand’s SM ability to understand customer preferences for different types of posts by using various statistical tools and the sentiment analysis (SA) technique applied to big sets of data.

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

scholarly journals Sentiment analysis based on fuzzy propagation in online social networks: A case study on TweetScope

2014 ◽  
Vol 11 (1) ◽  
pp. 215-228 ◽  
Author(s):  
Duc Trung ◽  
Jason Jung
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Significant Influence ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Important Task ◽  
Propagation Modeling ◽  
Practical System

Understanding customers? opinion and subjectivity is regarded as an important task in various domains (e.g., marketing). Particularly, with many types of social media (e.g., Twitter and FaceBook), such opinions are propagated to other users and might make a significant influence on them. In this paper, we propose a fuzzy propagation modeling for opinion mining by sentiment analysis of online social networks. Thereby, a practical system, called TweetScope, has been implemented to efficiently collect and analyze all possible tweets from customers.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

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 Social media and sentiment in bioenergy consultation

2016 ◽  
Vol 10 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Victoria Uren ◽  
Daniel Wright ◽  
James Scott ◽  
Yulan He ◽  
Hassan Saif
Keyword(s):  
Social Media ◽  
New Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Public Consultation ◽  
Short Paper ◽  
Consultation Process ◽  
Content Type ◽  
The Public ◽  
The Uk

Purpose – This paper aims to address the following challenge: the push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organizations towards energy development projects. Design/methodology/approach – This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised and illustrated using a sample of tweets containing the term “bioenergy”. Findings – Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications – Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Social implications – Social media have the potential to open access to the consultation process and help bioenergy companies to make use of waste for energy developments. Originality/value – Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

scholarly journals A Survey on Analysis of Twitter Opinion Mining Using Sentiment Analysis

2019 ◽  
pp. 537-542
Author(s):  
Vishnu VardanReddy ◽  
Mahesh Maila ◽  
Sai Sri Raghava ◽  
Yashwanth Avvaru ◽  
Sri. V. Koteswarao
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Online Communication ◽  
Valuable Insight ◽  
Huge Amount ◽  
Twitter Data ◽  
Product Service ◽  
Different Types

In recent years, there is a rapid growth in online communication. There are many social networking sites and related mobile applications, and some more are still emerging. Huge amount of data is generated by these sites everyday and this data can be used as a source for various analysis purposes. Twitter is one of the most popular networking sites with millions of users. There are users with different views and varieties of reviews in the form of tweets are generated by them. Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of twitter data and lot needs to be done. In this paper we discuss the levels, approaches of sentiment analysis, sentiment analysis of twitter data, existing tools available for sentiment analysis and the steps involved for same. Two approaches are discussed with an example which works on machine learning and lexicon based respectively.

scholarly journals Strategies of SMM Management, Comparison of Instagram and Facebook Social Networks

2017 ◽  
Vol 3 (5) ◽  
pp. 51
Author(s):  
Ramis Akhmedov
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Customer Relationship ◽  
Social Media Marketing ◽  
Crm System ◽  
Research Questions ◽  
Short Time ◽  
A Company ◽  
Potential Customers ◽  
The Ideal

<p class="Default">SMM occupies an important role in the lives of people and so many people are represented in social networks, it provides the ideal platform for companies so they can communicate with their current and potential customers. This study continues to explore how companies can use social media marketing to build and maintain relationships with customers. This investigates through conducted research questions. How SMM is effective in terms of CRM? Can Facebook replace CRM system? Why do people choose to follow a company on Instagram? To analyze more clearly the focus will be on Instagram and Facebook applications, which in a short time acquired great popularity among private users as well as among the companies. The purpose of this study is to indicate the integration of customer relationship management (CRM) with social media marketing (SMM) strategies, and defines its benefits for business.</p><p class="Default"> </p>

scholarly journals Sentiment Analysis of the Syrian Conflict on Twitter

2021 ◽  
Vol 11 (22) ◽  
pp. 46-61
Author(s):  
Danijela Lucić ◽  
Josip Katalinić ◽  
Tomislav Dokman
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Data Set ◽  
Political Landscape ◽  
Important Means ◽  
Human Emotions ◽  
Syrian Conflict

Social media have become an important means of imposing ideas and interests in social‏ conflicts. The Syrian conflict is analysed using sentiment analysis of tweets in order to establish how the‏ sentiment shapes the modern political landscape and influences recipient knowledge. The importance of‏ social networks and their potential in overthrowing regimes as well as in radicalization are highlighted.‏ The authors suggest several stages that can be used for analysing tweets and how they impact the reader‏ with selected narration. Sentiment analysis is used on a trained data set as a way to gain insight into‏ tweets of different factions in the Syria conflict. Selected tweets on missile strikes were published on 14‏ April 2018 and the day after. The Twitter profiles of three different sides – pro-Assad, pro-West and anti-‏ Assad – were also analysed. The results show that there is a real battle on social media with the purpose‏ of influencing human emotions.

Sentiment Analysis in Crisis Situations for Better Connected Government

2021 ◽  
pp. 162-181
Author(s):  
Asdrúbal López Chau ◽  
David Valle-Cruz ◽  
Rodrigo Sandoval-Almazán
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Citizen Participation ◽  
Important Means ◽  
The Government ◽  
Made In ◽  
Crisis Situations

One of the pillars of connected government is citizen centricity: an approach in which citizen participation is essential. In Mexico, social networks are currently one of the most important means by which citizens express their needs and provide opinions to the government. The goal of this chapter is to contribute to citizen centricity by adapting the methodology of sentiment analysis of social media posts to an expanded version for crisis situations. The main difference in this approach from the normally accepted one is that instead of using pre-defined classes (positive and negative) for sentiments, the authors first determined the different data categories and then applied them to the classic process of sentiment analysis. This approach was tested using posts on Mexico's earthquake in 2017. They found that needs, demands, and claims made in the posts reflect sentiments in a better way, and this can help to improve the government-citizen connection.

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