scholarly journals Assessing Public Opinions of Products Through Sentiment Analysis

2021 ◽  
Vol 33 (4) ◽  
pp. 125-141
Author(s):  
C. Y. Ng ◽  
Kris M. Y. Law ◽  
Andrew W. H. Ip
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
New Product Development ◽  
Social Networking Sites ◽  
New Product ◽  
User Generated Content ◽  
Product Reviews ◽  
Public Opinions ◽  
Critical Design ◽  
Learning Technique

In the world of social networking, consumers tend to refer to expert comments or product reviews before making buying decisions. There is much useful information available on many social networking sites for consumers to make product comparisons. Sentiment analysis is considered appropriate for summarising the opinions. However, the sentences posted online are generally short, which sometimes contains both positive and negative word in the same post. Thus, it may not be sufficient to determine the sentiment polarity of a post by merely counting the number of sentiment words, summing up or averaging the associated scores of sentiment words. In this paper, an unsupervised learning technique, k-means, in conjunction with sentiment analysis, is proposed for assessing public opinions. The proposed approach offers the product designers a tool to promptly determine the critical design criteria for new product planning in the process of new product development by evaluating the user-generated content. The case implementation proves the applicability of the proposed approach.

User-generated content on the Facebook page of an emergency management agency

2017 ◽  
Vol 41 (4) ◽  
pp. 558-579 ◽  
Author(s):  
Jayan Chirayath Kurian ◽  
Blooma Mohan John
Keyword(s):  
Social Networking ◽  
Emergency Management ◽  
Social Networking Sites ◽  
Disaster Recovery ◽  
Information Dissemination ◽  
Social Networking Site ◽  
User Generated Content ◽  
Content Type ◽  
Management Agency ◽  
Mixed Response

Purpose The purpose of this paper is to explore themes eventuating from the user-generated content posted by users on the Facebook page of an emergency management agency. Design/methodology/approach An information classification framework was used to classify user-generated content posted by users including all of the content posted during a six month period (January to June 2015). The posts were read and analysed thematically to determine the overarching themes evident across the entire collection of user posts. Findings The results of the analysis demonstrate that the key themes that eventuate from the user-generated content posted are “Self-preparedness”, “Emergency signalling solutions”, “Unsurpassable companion”, “Aftermath of an emergency”, and “Gratitude towards emergency management staff”. Major user-generated content identified among these themes are status-update, criticism, recommendation, and request. Research limitations/implications This study contributes to theory on the development of key themes from user-generated content posted by users on a public social networking site. An analysis of user-generated content identified in this study implies that, Facebook is primarily used for information dissemination, coordination and collaboration, and information seeking in the context of emergency management. Users may gain the benefits of identity construction and social provisions, whereas social conflict is a potential detrimental implication. Other user costs include lack of social support by stakeholders, investment in social infrastructure and additional work force required to alleviate the technological, organisational, and social barriers in communication among stakeholders in emergency management. A collective activity system built upon the Activity Theory was used as a lens to describe users’ activity of posting content on the Facebook page of an emergency management agency. Practical implications By analysing the findings, administrators and policy makers of emergency management could identify the extent to which the core principles of disaster recovery are accomplished using public social networking sites. These are achieved in relation to: pre-disaster recovery planning; partnership and inclusiveness; public information messaging; unity of effort; and, psychological recovery to maximise the success of recovery in a disaster. Furthermore, a core principle which evoked a mixed response was timeliness and flexibility. Originality/value Previous studies have examined the role of social networking sites in disastrous situations, but to date there has been very little research into determining themes found in user-generated content posted on the Facebook page of an emergency management agency. Hence, this study addresses the gap in literature by conducting a thematic analysis of user-generated content posted on the Facebook page of the Federal Emergency Management Agency.

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 Sentiment Analysis of Patients' Opinions in Healthcare using Lexicon-based Method

2019 ◽  
Vol 9 (1) ◽  
pp. 6977-6981
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Healthcare Service ◽  
Opinion Mining ◽  
Research Area ◽  
Service Needs ◽  
Health Organizations ◽  
The World ◽  
New Research

The rapid increase in technology made people across the world use social networking sites to express their opinions on a topic, product or service. The success of a healthcare service directly depends on its users. If a majority of users like the service then it is a success otherwise, the service needs to be improvised. For improvising the service, the users' opinions need to be analyzed. Manually extracting and analyzing the content present on the web is a tedious task. This gave rise to a new research area called Sentiment Analysis. It is otherwise known as opinion mining. It is being used by many health organizations to make effective decisions on their service. This paper presents the sentiment analysis of patients' opinions on hospitals which is mainly used to improve healthcare service. This is implemented using a lexicon-based methodology to analyze the sentiment.

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.

The Impact of Web 2.0 on Public Engagement in the Israeli Home Front (From the 2006 Lebanon War to the 2014 Israel-Gaza Conflict)

2018 ◽  
Vol 5 (2) ◽  
pp. 52-63
Author(s):  
Galit Margalit Ben-Israel
Keyword(s):  
Social Media ◽  
Web 2.0 ◽  
Social Networking ◽  
Public Participation ◽  
Public Engagement ◽  
Social Networking Sites ◽  
Home Front ◽  
User Generated Content ◽  
Asymmetric Conflicts ◽  
The Impact

This article deals with citizen engagement and public participation being in crisis on the Israeli home front, in the era of Web 2.0. Since 2004, Web 2.0 characterizes changes that allow users to interact and collaborate with each other in a social media dialogue as creators of user-generated content in social networking sites: Facebook, Twitter, blogs, wikis, YouTube, hosted services, applications, WhatsApp, etc. Since 2006, Israel is involved in asymmetric conflicts. The research defines the impact of Web 2.0 on public engagement in the Israeli home front. The case studies examined in the research are: 1) The 2006 Lebanon War (July-August 2006); 2) The Gaza War (27 December 2008 and ended on 18 January 2009); 3) Operation Pillar of Defense (November 2012); and 4) The 2014 Israel–Gaza conflict.

scholarly journals Tourism Information Diffusion through SNSs: A Theoretical Investigation

2020 ◽  
Vol 12 (5) ◽  
pp. 1731 ◽  
Author(s):  
Ting Liu ◽  
Jianhong Xia ◽  
Lesley Crowe-Delaney
Keyword(s):  
Decision Making ◽  
Social Networking ◽  
Network Structure ◽  
Social Networking Sites ◽  
Information Diffusion ◽  
User Generated Content ◽  
Factors Affecting ◽  
Tourist Destinations ◽  
Tourism Information ◽  
Tourist Information

Social networking sites (SNSs) are known to have a role in promoting tourism and influencing how it is marketed to consumers, but there seems to be few deep analyses of SNS’s efficacy in tourists’ decision making and destination promotion. To address this, we present Tourism Information Diffusion Ecosystem (TIDE), a novel theoretical framework to help understand this system of tourism SNS information diffusion. TIDE defines who participates in the system, what roles participants play in distributing tourist information contained within user-generated content, how content within a network is distributed, and if this user-generated information, once diffused, has been transferred into tourists’ visiting actions, and the reasons why these actions have been generated. We discovered user typologies and the powerful characteristics of this network structure to be important factors affecting visiting actions in choosing particular tourist destinations.

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 To Improve Feature Extraction and Opinion Classification Issues in Customer Product Reviews Utilizing an Efficient Feature Extraction and Classification (EFEC) Algorithm

2018 ◽  
Vol 10 (2) ◽  
pp. 587
Author(s):  
Palaiyah Solainayagi ◽  
Ramalingam Ponnusamy
Keyword(s):  
Feature Extraction ◽  
Social Networking ◽  
Social Networking Sites ◽  
Essential Role ◽  
Classification Problem ◽  
Product Reviews ◽  
Major Goal ◽  
Shopping Experience ◽  
General Opinion ◽  
F Measure

<span lang="EN-US">Currently, customer's product review opinion plays an essential role in deciding the purchasing of the online product. A customer prefers to acquire the opinion of other customers by viewing their opinion during online products' reviews, blogs and social networking sites, etc. The majority of the product reviews including huge words. A few users provide the opinion; it is tough to analysis and understands the meaning of reviews. To improve user fulfillment and shopping experience, it has become a general practice for online sellers to allow their users to review or to communicate opinions of the products that they have sold. The major goal of the paper is to solve feature extraction problem and opinion classification problem from customers utilized product reviews which extract the feature words and opinion words from product reviews. To propose an Efficient Feature Extraction and Classification (EFEC) algorithm is implementing to extracts a feature from opinion words. The reviewer usually marks both positive and negative parts of the reviewed product, despite the fact that their general opinion on the product may be positive or negative. An EFEC algorithm is utilized to predict the number of positive and negative opinion in reviews. Based on Experimental evaluations, proposed algorithm improves accuracy 15.05%, precision 13.7%, recall 15.59% and F-measure 15.07% of the proposed system compared than existing methodologies</span>

scholarly journals Effects of Narcissism, Leisure Boredom, and Gratifications Sought on User-Generated Content Among Net-Generation Users

2013 ◽  
pp. 49-63 ◽  
Author(s):  
Damon Chi Him Poon ◽  
Louis Leung
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Large Degree ◽  
User Generated Content ◽  
Net Generation ◽  
The Internet ◽  
Online Forums ◽  
Consumer Reviews ◽  
Negative Feelings ◽  
Leisure Boredom

This research identifies the gratifications sought by the Net-generation when producing user-generated content (UGC) on the internet. Members of the Net-generation want to vent negative feelings, show affection to their friends and relatives, be involved in others’ lives, and fulfill their need to be recognized. These gratifications, to a large degree, were found to be significantly associated with the users’ various levels of participation in UGC (e.g., Facebook, blogs, online forums, etc.). What’s more, narcissism was predictive of content generation in social networking sites, blogs, and personal webpages, while leisure boredom was significantly linked to expressing views in forums, updating personal websites, and participating in consumer reviews. In particular, the results showed that Net-geners who encountered leisure boredom had a higher tendency to seek interaction with friends online. Implications of findings are discussed.

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