Web opinion mining for social networking sites

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.

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

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2141.109119 ◽

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.

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f1073.0486s419 ◽

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

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Online Crowds Opinion-Mining it to Analyze Current Trend: A Review

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v5i5.pp1180-1187 ◽

2015 ◽

Vol 5 (5) ◽

pp. 1180

Author(s):

Haritha Akkineni ◽

P.V.S Lakshmi ◽

B.Vijaya Babu

Keyword(s):

Social Networking ◽

Online Social Networks ◽

Social Networking Sites ◽

Opinion Mining ◽

Current Trend ◽

Decision Making Process ◽

Comprehensive Overview ◽

The Social ◽

Current Scenario ◽

The Web

Online presence of the user has increased, there is a huge growth in the number of active users and thus the volume of data created on the online social networks is massive. Much are concentrating on the Internet Lingo. Notably most of the data on the social networking sites is made public which opens doors for companies, researchers and analyst to collect and analyze the data. We have huge volume of opinioned data available on the web we have to mine it so that we could get some interesting results out of it with could enhance the decision making process. In order to analyze the current scenario of what people are thinking focus is shifted towards opinion mining. This study presents a systematic literature review that contains a comprehensive overview of components of opinion mining, subjectivity of data, sources of opinion, the process and how does it let one analyze the current tendency of the online crowd in a particular context. Different perspectives from different authors regarding the above scenario have been presented. Research challenges and different applications that were developed with the motive opinion mining are also discussed.

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Location based Twitter Opinion Mining using Common-Sense Information

Global Journal of Enterprise Information System ◽

10.18311/gjeis/2017/15616 ◽

2017 ◽

Vol 9 (2) ◽

pp. 28 ◽

Cited By ~ 4

Author(s):

Amita Jain ◽

Minni Jain

Keyword(s):

Social Networking ◽

Common Sense ◽

Social Networking Sites ◽

Opinion Mining ◽

Public Information ◽

Social Media Site ◽

Machine Learning Model ◽

Product Service ◽

Female Specific ◽

Mining Model

Sentiment analysis research of public information from social networking sites has been increasing immensely in recent years. Data available at social networking sites is one of the most effective and accurate source to identify the public sentiment of any product/service. In this paper, we propose a novel localized opinion mining model based on common sense information extracted from ConceptNet ontology. The proposed methodology allows interpretation and utilization of data extracted from social media site “Twitter” to identify public opinions. This paper includes location specific, male- female specific and concept specific popularities of product. All extracted concepts are used to calculate senti_score and to build a machine learning model that classifies the user opinions as positive or negative.

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