scholarly journals A Inspection on Sentiment Research of Big Data: Techniques, Open Challenges

2019 ◽  
Vol 8 (6S4) ◽  
pp. 362-365
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Service Industry ◽  
Research Work ◽  
Educational Institutions ◽  
Business Organizations ◽  
The Public ◽  
Day By Day

Due to the invention of Web 2.0, the users have become more interest to share their content day by day. The emergence of various social networking sites has added to a greater extent to these activities. These provide a very good platform for the users to share the opinions of the persons across the globe. The opinions shared by the customers on the web can have the prevalent over the service industry. Many industries such as educational institutions, researchers, business organizations are concentrating opinion mining which is also called as sentiment analysis (SA) to retrieve the views and opinions posted by the public. This paper made a survey on Sentiment Analysis (SA) which aims to discusses technical aspects of SA (techniques, types) .This paper further highlights the main challenges faced by SA. These challenges present a lot of scope for research work in the future

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.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing 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 like stock exchange.</p>

scholarly journals Use of social networking sites: Facebook group as a learning management system

2015 ◽  
pp. 232-249
Keyword(s):  
Social Networking ◽  
Undergraduate Students ◽  
Social Networking Sites ◽  
Management System ◽  
Learning Management System ◽  
Academic Disciplines ◽  
Business Organizations ◽  
Step Procedure ◽  
Learning Management ◽  
Day By Day

Social networking sites (SNSs) are becoming popular day by day in academia as well as in business organizations around the world. Facebook as the largest and fastest networking sites, is one of the important SNSs that can play an important role in different academic disciplines. The aim of this study is to investigate the use of SNSs by the undergraduate students of International Islamic University of Malaysia (IIUM). The specific objectives are: (i) to explore the frequency of using SNSs by the undergraduates; (ii) to identify the purpose of using SNSs; (iii) to examine the perceptions of undergraduates for using SNSs as an academic tool; and (iv) finally, to propose Facebook group as a learning management system (LMS) of IIUM. A structured survey questionnaire was distributed among 500 undergraduate students of IIUM and 351 responses were received. The results report that Facebook and Google+ are preferred SNSs. Sharing information with friends, getting connected with people for different level, making new friends, and passing time are the main reasons for using SNSs. This study also proposes a six-step procedure for using Facebook group as a LMS.

scholarly journals Robustious Feature Selection Based Genetic Algorithm (RFS-GA) For Cross Domain Opinion Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 6267-6279
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Performance Metrics ◽  
Research Work ◽  
Features Selection ◽  
Cross Domain ◽  
Multiple Challenges ◽  
Day By Day

Day by day the requirement of information for processing the sentiment analysis is getting increased multiple times. For these kind of reasons, feature selection is utilized to detect the opinion among different reviews and comments. Sentiment analysis is becoming like phenomenon due to increase of social media’s popularity. Currently, significant advancements are shown in this research domain, but still multiple challenges are to be solved – i.e., sentiment analysis in cross domains. In this paper rumbustious feature selection based genetic algorithm is proposed to address the problem of analyzing the sentiments in cross domain. It performs classification based optimistic-class and pessimistic-class. The dataset used to this research work includes books, DVDs, gadgets and kitchen appliances. Initially the features selection is performed and opinion mining is performed by Genetic Algorithm. Benchmark performance metrics are selected for measuring the performance of proposed work against existing method. Results depict that the proposed work has better performance than that of the existing work as far as chosen performance metrics.

Enhancements in Decision Making Through Sentiment Analysis of Twitter Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4083-4091
Author(s):  
Jagadish S. Kallimani ◽  
S. H. Ajeya ◽  
D. Keerthana ◽  
Manoj J. Shet ◽  
Prasada Hegde
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Social Networking Sites ◽  
Multi Layer Perceptron ◽  
The Public ◽  
Twitter Data ◽  
Product Service ◽  
Reliable Source ◽  
Computational Identification

All trades and business run predominantly on customer satisfaction and serves as the key to success. Usually, the decisions made by people is largely dependent on others’ perspectives. Hence, it becomes important to have reviews in your favor to sustain and outperform competitors in the market. Collecting reviews and predictions and analyzing them is an effective method to get insights on how the product, service or subject is accepted by the public. It also helps us discover the fields or aspects that needs to be improved. This comes under the field of Sentiment Analysis which refers to the computational identification of views, perspectives, opinions and emotions from text and speech through Natural Language Processing. With the emergence of the internet, blogging and social-networking sites are a rage. Twitter is one of the popular and ubiquitous sites and acts as a reliable source of feedback. In this paper, we seek to detect the emotion portrayed in a given tweet with significant accuracy. We propose the use of Word2Vec model and Count Vectorizer to extract features from pre-processed data. The output is fed to trained Multi-Layer Perceptron classifier to detect the emotion behind the sentence.

scholarly journals Predicting Election Results using NLTK

2019 ◽  
Vol 9 (1) ◽  
pp. 4519-4529
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Text Processing ◽  
Research Work ◽  
Supervised Machine Learning ◽  
Analysis Tool ◽  
Machine Learning Classification

In today’s world, people are usually using social media networks for trying to communicate with other users and for sharing information across the world. The online social networking sites have become considerable tools and are providing a common medium for a number of users to communicate with each other. Twitter is the most prominent microblogging website and one among the social networking sites that grow on a daily basis. Social media incorporates an extensive amount of data in the form of tweets, forums, status updates, comments, etc. in an attempt to automatically process and analyze these data, applications can rely on analysis approaches such as sentiment analysis. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), to obtain user's opinions and sentiments. Natural Language Toolkit (NLTK) is a library based on machine learning methods in python & sentiment analysis tool. Which provides the base for text processing and classification? The research work proposed a machine learning-based classifier to extract the tweets on elections and analyze the opinion of the tweeples (people who use twitter). The tweets can be categorized as positive, negative and neutral towards a particular politician. We classify these processed tweets using a supervised machine learning classification approach. The classifier used to classify the tweets as positive, negative or neutral is Naive Bayes Classifier. The classifier is trained with tweets bearing a distinctive polarity. The percentage of positive and negative tweets is then measured and graphically represented.

scholarly journals Victimization of Facebook Users: An Empirical Study among Female Students in Tangail

2019 ◽  
Vol 6 (2) ◽  
pp. 157-168
Author(s):  
Mohammad Ashraful Alam ◽  
Mahmuda Akter ◽  
Saikat Alam Santona
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
National Level ◽  
Research Work ◽  
Female Students ◽  
Social Networking Site ◽  
Educational Institutions ◽  
Public Consciousness ◽  
Purposive Sample ◽  
Structured Questionnaire

Social networking site is an important area of research. It is a known fact that young adults and teenagers, especially students are the most avid users of online Social networking sites which have a great impact on their lives. Yet, little is known about the sardonic experiences of students in social networking sites. With approximate 22.880 million users Facebook holds its number one popularity rank. Therefore, this paper takes Facebook as commissar of these Social Networking sites and focuses to provide an insight on the victimization of Female student and tends to explore the current nature and causes of female Facebook victimization. Female students are opted because they are more vulnerable in these sites than male. This paper reports the findings of a research work that was carried out in the higher secondary educational institutions in Tangail by using a non-probability purposive sample of 15 to 26 years aged female students. 70 samples were collected through a structured questionnaire and the gleaned data was analyzed by using SPSS. This study demonstrates, although social networking site have opened up a new way of socialization, respondents had experienced several types of cyber crime in Facebok. Such experiences create a state of insecurity, depression, fear, isolation and to a very large extent suicide. It is relatively a new phenomenon and hopefully both the effective technical strategy along with public consciousness used as a useful tool to reduce female vulnerability in Facebook from individual to national level in Babgladesh.  

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.

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