Analyzing Public Sentiments: A Review

Large number of users share their opinions on Social networking sites. it can be useful for analyzing sentiments of different peoples about different domains/products. so that this analysis can be beneficial for making various decisions in various fields. For instance a company can analyze sentiments about products while a politician can view comments about them to improve their position in the society. in previous studies only track of sentiments can be taken but in this we are trying to analyze the public sentiments and trying to find out the possible reasons behind variation about comments based on that we tries to propose a system and tries to improve performance of system. DOI: 10.17762/ijritcc2321-8169.15021

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

Assessing Public Opinions Through Web 2.0: A Case Study on Wal-Mart

The recent advancement of Web 2.0 enables people to exchange their opinions on a variety of topics. Among these discussions, the opinions of employees, customers, and investors are of great interest to companies. Insight into such perspectives can help managers make better decisions on business policies and strategy. However, assessing online opinions is a nontrivial task. The high volume of messages, casual writing style, and the significant amount of noise require the application of sophisticated text mining techniques to digest the data. Previous research has successfully applied sentiment analysis to assess online opinions on specific items and topics. In this research, we propose the integration of topic analysis with sentiment analysis methods to assess the public opinions expressed in forums with diverse topics of discussion. Using a Wal- Mart-related Web forum as an example, we found that combining the two types of analysis can provide us with improved ability to assess public opinions on a company. Through further analysis on one cluster of discussions, several abnormal traffic and sentiment patterns were identified related to Wal-Mart events. The case study validates the propose framework as an IT artifact to assess online public opinion on companies of interest. Our research promotes further efforts to combine topic and sentiment analysis techniques in online research supporting business decision making

A blog mining framework

Blogs have become increasingly popular, and new blogs are generated every day. Many of the contents are useful for applications in various domains, such as business, politics, research, social work, and linguistics. However, automatically collecting and analyzing blogs isn't straightforward due to the large size and dynamic nature of the blogosphere. In this article, the authors propose a framework for blog mining that includes spiders, parsers, analyzers, and visualizers. They present several examples of blog mining applications based on their framework. © 2006 IEEE.published_or_final_versio

Combined analysis of news and Twitter messages

While it is widely recognized that streams of social media messages contain valuable information, such as important trends in the users’ interest in consumer products and markets, uncovering such trends is problematic, due to the extreme volumes of messages in such media. In the case Twitter messages, following the interest in relation to all known products all the time is technically infeasible. IE narrows topics to search. In this paper, we present experiments on using deeper NLP-based processing of product-related events mentioned in news streams to restrict the volume of tweets that need to be considered, to make the problem more tractable. Our goal is to analyze whether such a combined approach can help reveal correlations and how they may be captured.Peer reviewe

Combined analysis of news and Twitter messages

While it is widely recognized that streams of social media messages contain valuable information, such as important trends in the users’ interest in consumer products and markets, uncovering such trends is problematic, due to the extreme volumes of messages in such media. In the case Twitter messages, following the interest in relation to all known products all the time is technically infeasible. IE narrows topics to search. In this paper, we present experiments on using deeper NLP-based processing of product-related events mentioned in news streams to restrict the volume of tweets that need to be considered, to make the problem more tractable. Our goal is to analyze whether such a combined approach can help reveal correlations and how they may be captured.Peer reviewe

Mining Social Media to Understand Consumers' Health Concerns and the Public's Opinion on Controversial Health Topics.

Social media websites are increasingly used by the general public as a venue to express health concerns and discuss controversial medical and public health issues. This information could be utilized for the purposes of public health surveillance as well as solicitation of public opinions. In this thesis, I developed methods to extract health-related information from multiple sources of social media data, and conducted studies to generate insights from the extracted information using text-mining techniques. To understand the availability and characteristics of health-related information in social media, I first identified the users who seek health information online and participate in online health community, and analyzed their motivations and behavior by two case studies of user-created groups on MedHelp and a diabetes online community on Twitter. Through a review of tweets mentioning eye-related medical concepts identified by MetaMap, I diagnosed the common reasons of tweets mislabeled by natural language processing tools tuned for biomedical texts, and trained a classifier to exclude non medically-relevant tweets to increase the precision of the extracted data. Furthermore, I conducted two studies to evaluate the effectiveness of understanding public opinions on controversial medical and public health issues from social media information using text-mining techniques. The first study applied topic modeling and text summarization to automatically distill users' key concerns about the purported link between autism and vaccines. The outputs of two methods cover most of the public concerns of MMR vaccines reported in previous survey studies. In the second study, I estimated the public's view on the ac{ACA} by applying sentiment analysis to four years of Twitter data, and demonstrated that the the rates of positive/negative responses measured by tweet sentiment are in general agreement with the results of Kaiser Family Foundation Poll. Finally, I designed and implemented a system which can automatically collect and analyze online news comments to help researchers, public health workers, and policy makers to better monitor and understand the public's opinion on issues such as controversial health-related topics.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120714/1/owenliu_1.pd