scholarly journals A Crowdsourcing Based Framework for Sentiment Analysis

2020 ◽  
Vol 16 (4) ◽  
pp. 285-295
Author(s):  
Fatima Zohra Ennaji ◽  
Abdelaziz El Fazziki ◽  
Hasna El Alaoui El Abdallaoui ◽  
Hamada El Kabtane
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Real Life ◽  
Data Sources ◽  
Valuable Data ◽  
Analysis Process ◽  
Validity Check ◽  
Online Platforms ◽  
Majority Consensus

As social networking has spread, people started sharing their personal opinions and thoughts widely via these online platforms. The resulting vast valuable data represent a rich source for companies to deduct their products’ reputation from both social media and crowds’ judgments. To exploit this wealth of data, a framework was proposed to collect opinions and rating scores respectively from social media and crowdsourcing platform to perform sentiment analysis, provide insights about a product and give consumers’ tendencies. During the analysis process, a consumer category (strict) is excluded from the process of reaching a majority consensus. To overcome this, a fuzzy clustering is used to compute consumers’ credibility. The key novelty of our approach is the new layer of validity check using a crowdsourcing component that ensures that the results obtained from social media are supported by opinions extracted directly from real-life consumers. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The obtained results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design.

Analysis of Public Sentiments About Mega Online Sale Using Tweets on Big Billions Day Sale

2019 ◽  
pp. 59-76 ◽  
Author(s):  
Dilip Singh Sisodia ◽  
Ritvika Reddy
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Stock Exchange ◽  
The Public ◽  
Online Sales ◽  
Public Sentiment ◽  
Online Platforms ◽  
Wide Range ◽  
Automated Process ◽  
The Web

The opinion of others significantly influences our decision-making process about any product or service. The positive or negative opinions of prospective clients or customers may promote or demote the profit margin of any business activities. Therefore, analyzing 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 such as stock exchange, sale of products, etc. With the emergence of Web 2.0 services, a wide range of online platforms including micro-blogging, social networking, and many other review platforms are available. The automated process for public sentiment analysis from a large amount of social data present on the web helps to improve customer satisfaction. This chapter discusses the process of sentiment analysis of prospective buyers of mega online sales using their posted tweets about the big billions day sale.

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.

scholarly journals Detection and Analysis of Stress using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 335-342
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Text Messages ◽  
Word Sense ◽  
Severe Problem ◽  
Sense Disambiguation

Every year tens of millions of people suffer from depression and few of them get proper treatment on time. So, it is crucial to detect human stress and relaxation automatically via social media on a timely basis. It is very important to detect and manage stress before it goes into a severe problem. A huge number of informal messages are posted every day in social networking sites, blogs and discussion forums. This paper describes an approach to detect the stress using the information from social media networking sites, like tweeter.This paper presents a method to detect expressions of stress and relaxation on tweeter dataset i.e. working on sentiment analysis to find emotions or feelings about daily life. Sentiment analysis works the automatic extraction of sentiment related information from text. Here using TensiStrengthframework for sentiment strength detection on social networking sites to extract sentiment strength from the informal English text. TensiStrength is a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation. This classifies both positive and negative emotions based on the strength scale from -5 to +5 indications of sentiments. Stressed sentences from the conversation are considered &categorised into stress and relax. TensiStrength is robust, it can be applied to a widevarietyofdifferent social web contexts. Theeffectiveness of TensiStrength depends on the nature of the tweets.In human being there is inborn capability to differentiate the multiple senses of an ambiguous word in a particular context, but machine executes only according to the instructions. The major drawback of machine translation is Word Sense Disambiguation. There is a fact that a single word can have multiple meanings or "senses." In the pre-processing partof-speech disambiguation is analysed and the drawback of WSD overcomes in the proposed method by unigram, bigram and trigram to give better result on ambiguous words. Here, SVM with Ngram gives better resultPrecision is65% and Recall is 67% .But, the main objective of this technique is to find the explicit and implicit amounts of stress and relaxation expressed in tweets. Keywords: Stress Detection, Data Mining, TensiStrength, word sense disambiguation.

scholarly journals Unplugged

2018 ◽  
Author(s):  
Lea Powell
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Networking ◽  
Real Life ◽  
Positive Influence ◽  
Self Image ◽  
Unrealistic Expectations

This article serves as an explorative piece attempting to investigate social networking norms and their contribution towards increased levels of disengagement and disconnection. After recognizing superficial online trends of interaction within her own social network, the author discloses experiencing feelings of hopelessness. In attempt to explore these feelings and unmask the factors underlying these trends, elements of motivation, privacy, and an individual’s relationship with control are discussed. Themes of expectant accessibility and communication within the realm of technology are explored and compared to real life interactions and experiences, with emphasis on an observed dissonance occurring between them. Notions of social networking's contribution to unrealistic expectations of self-image and worth are addressed to caution the reader against over-embellishment and the risks associated with distorted representations of self. Concluding remarks credit the positive influence of social networking’s impact on society while warranting further investigation from the reader. Readers are encouraged to reflect on the topics in attempt to establish their own healthy, balanced relationship with technology and social media.

Computing Opinions for Twitter Review Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4360-4363
Author(s):  
S. Tenkale Pallavi ◽  
S. Jagannatha
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Web Pages ◽  
Human Being ◽  
Association Network ◽  
The Social ◽  
Individual Human

Customers and users post their opinions or reviews on social networking sites and it has increased the amount of data WWW. With this users from all over world try to share their opinions and sentiments on the blogging sites every day. Internet is being used in form of web pages, social media, and sometimes blogs which increases online portals sentiments, reviews, opinions, references, scores, and feedbacks are also generated by people. Twitter is the most famous micro-blogging site where users express their opinions in the form of tweets. The user can express their sentiments about various aspects e.g., books, celebrities, restaurants, various products, research, events, etc. All these opinions plays vital roles and they are quite important for various businesses, for government schemes, and for individual human being as well. Still, there are many curbs in mining reviews or opinions and process to calculate them. These limitations have turned into highland in investigating the actual gist of opinions and measuring its polarity. Hence, we recommend an inventive way to compute the sentiments for given reviews or opinions. This recommendation is centered on the social networking sites’ information of various Tweets, a word-emotion-association-network is put up in association to represent opinions and semantics that decides the base for the emotions (sentiment) analysis of opinion or reviews.

scholarly journals Real Time Cyberbullying Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 5197-5201
Keyword(s):  
Social Networking ◽  
Detection System ◽  
Research Work ◽  
Real Life ◽  
Threshold Value ◽  
Cyber Bullying ◽  
Good Time ◽  
Online Harassment ◽  
Online Platforms ◽  
Cyberbullying Detection

Automated approaches for detecting cyberbullying on online platforms has remained a primary research concern over past years. Cyber bullying is defined as the use of electronic communication to bully a person, typically by sending messages of intimidating or threatening nature. The victims especially teenagers suffer from loss of confidence, depression, sleep disorder. The research on automated cyberbullying approach is mainly focused on data driven methods. Such methods work on a database of static texts, usually collected from online platforms and are not feasible for dynamic nature of a real-life social networking scenarios. The aim of our research is to develop a cyberbullying detection system using Fuzzy Logic. Three types of bullying emotions are considered in this research work namely aggression, abuse and threat. In the proposed approach chat between two users is continuously monitored and emotion present in each message is determined. Based on the emotion each user’s behavior is categorized as decent or bullying. If the detected bullying nature is higher than a defined threshold value the account of user is ceased and reported automatically. The proposed approach is tested with a chat application developed in Microsoft .Net Framework and approach can detect cyber bullying in good time. The proposed approach, if implemented with social networking platforms can serve as a useful aid for preventing online harassment. The developed algorithm can also be applied in surveillance and human behavioral analysis.

scholarly journals Social Media Addiction Effects on Well-Being among Tertiary School Graders an Empirical Study in Viaskhapatnam

2021 ◽  
Vol 04 (07) ◽  
Author(s):  
Subhashini Akurathi ◽  
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Social Life ◽  
Real Life ◽  
Well Being ◽  
Depression And Anxiety ◽  
Long Distance ◽  
Psychological Well Being ◽  
Study Results ◽  
The Impact

Psychological well-being has become one of the crucial aspects of modern psychology. It has attracted not only psychologists but also medical practitioners in compliance with the mental health, emotions, depression, stress, etc. of the individual’s academic and social life. Right from the existence of behavioral sciences, efforts have been made by researchers of different disciplines to get a deep understanding of the various and different dimensions and correlates of psychological well-being. Social media platforms are an online association site where individuals cooperate to construct, offer and change their thought and remarks concerning any data. Over the past decade, online long distance social networking communication has brought significant changes in the way people communicate and collaborate. The study aimed to determine the impact of social media’s early health issues such as student depression and anxiety. A descriptive study was conducted among Tertiary level educated students in Visakhapatnam with a sample of 130 respondents. It included questions on demographical information, the pattern of social networking usage, social relationship, and health effects. Results: Present study results found that there is a significant association between time spent on social media and the number of social networking apps. There is a positive correlation between Depression feelings with serious active on social networking apps than in real life. Conclusion: This study concludes that more usage of social networking sites is affecting the Tertiary school student’s well-being such as depression and anxiety.

scholarly journals Online Realtime Sentiment Analysis Tweets by Utilizing Streaming API Features From Twitter

2019 ◽  
Vol 9 (1) ◽  
pp. 53
Author(s):  
Nfn Bahrawi
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Relevant Information ◽  
Processing Technique ◽  
Web Based ◽  
Analysis Process ◽  
Analysis System

<p class="JGI-AbstractIsi">Twitter is one of the social media that has a simple and fast concept, because short messages, news or information on Twitter can be more easily digested. This social media is also widely used as an object for researchers or industry to conduct sentiment analysis in the fields of social, economic, political or other fields. Opinion mining or also commonly called sentiment analysis is the process of analyzing text to get certain information in a sentence in the form of opinion. Sentiment analysis is one of the branches of the science of Text mining where text mining is a natural language processing technique and analytical method that is applied to text data to obtain relevant information. Public opinion or sentiment in social media twitter is very dynamic and fast changing, a real time sentiment analysis system is needed and it is automatically updated continuously so that changes can always be monitored, anytime and anywhere. This research builds a system so that it can analyze sentiment from twitter social media in realtime and automatically continuously. The results of the system trial succeeded in drawing data, conducting sentiment analysis and displaying it in graphical and web-based realtime and updated automatically. Furthermore, this research will be developed with a focus on the accuracy of the algorithms used in conducting the sentiment analysis process.</p>

scholarly journals Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media

2018 ◽  
Author(s):  
Filippo Chiarello ◽  
Andrea Bonaccorsi ◽  
Gualtiero Fantoni ◽  
Giacomo Ossola ◽  
Andrea Cimino ◽  
...  
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Decision Process ◽  
New Products ◽  
Point Of View ◽  
Content Sharing ◽  
Technological Complexity ◽  
Box Office ◽  
Future Outcomes

In recent years, social media have become ubiquitous and important for social networking and content sharing. Moreover, the content generated by these websites remains largely untapped. Some researchers proved that social media have been a valuable source to predict the future outcomes of some events such as box-office movie revenues or political elections. Social media are also used by companies to measure the sentiment of customers about their brand and products. This work proposes a new social media based model to measure how users perceive new products from a technical point of view. This model relies on the analysis of advantages and drawbacks of products, which are both important aspects evaluated by consumers during the buying decision process. This model is based on a lexicon developed in a related work (Chiarello et. al, 2017) to analyse patents and detect advantages and drawbacks connected to a certain technology. The results show that when a product has a certain technological complexity and fuels a more technical debate, advantages and drawbacks analysis is more efficient than sentiment analysis in producing technical-functional judgements.

scholarly journals Development of Personas to Communicate Narrative-Based Information About the HPV Vaccine on Twitter

2021 ◽  
Vol 3 ◽  
Author(s):  
Philip M. Massey ◽  
Shawn C. Chiang ◽  
Meredith Rose ◽  
Regan M. Murray ◽  
Madeline Rockett ◽  
...  
Keyword(s):  
Social Media ◽  
Health Information ◽  
Hpv Vaccine ◽  
Design Thinking ◽  
Digital Health ◽  
Hpv Vaccination ◽  
Real Life ◽  
Data Sources ◽  
Vaccine Hesitancy ◽  
Persona Development

Introduction: Personas are based on real-life typologies of people that can be used to create characters and messages to communicate important health information through relatable narrative storylines. Persona development is data-driven and can involve multiple phases of formative research and evaluation; however, personas are largely underutilized in digital health research. The purpose of this study was to create and document persona development to deliver narrative-focused health education for parents on Twitter with the goal of increasing uptake of HPV vaccination among adolescents.Methods: Leveraging data from a mixed-method study conducted in the U.S. with a diverse population of parents with adolescents ages 9–14, we used both qualitative and quantitative data (e.g., the National Immunization Survey—Teen, focus groups, and social media) to create personas. These data sources were used to identify and develop key characteristics for personas to reflect a range of parents and their diverse understandings and experiences related to HPV vaccination. A parent advisory board provided insight and helped refine persona development.Results: Four personas emerged and were characterized as the (1) Informed Altruist, (2) Real Talker, (3) Information Gatherer, and (4) Supporter. Characteristics differed across personas and provided insights into targeted narrative strategies. Described attributes included demographics, psychographics, communication style, vaccine goals and aspirations, vaccine challenges and frustrations, and vaccine hesitancy.Discussion: This work demonstrates how multiple data sources can be used to create personas to deliver social media messages that can address the diverse preferences and needs of parents for HPV vaccine information. With increasing usage of social media for health information among parents, it is important for researchers to consider marketing and design thinking to create health communication messages that resonate with audiences.

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