A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors

Sentiment analysis and opinion mining from social media: A review

Ubiquitous presence of internet, advent of web 2.0 has made social media tools like blogs, Facebook, Twitter very popular and effective. People interact with each other, share their ideas, opinions, interests and personal information. These user comments are used for finding the sentiments and also add financial, commercial and social values. However, due to the enormous amount of user generated data, it is an expensive process to analyze the data manually. Increase in activity of opinion mining and sentiment analysis, challenges are getting added every day. There is a need for automated analysis techniques to extract sentiments and opinions conveyed in the user-comments. Sentiment analysis, also known as opinion mining is the computational study of sentiments and opinions conveyed in natural language for the purpose of decision making. Preprocessing data play a vital role in getting accurate sentiment analysis results. Extracting opinion target words provide fine-grained analysis on the customer reviews. The labeled data required for training a classifier is expensive and hence to over come, Domain Adaptation technique is used. In this technique, Single classifier is designed to classify homogeneous and heterogeneous input from di_erent domain. Sentiment Dictionary used to find the opinion about a word need to be consistent and a number of techniques are used to check the consistency of the dictionaries. This paper focuses on the survey of the existing methods of Sentiment analysis and Opinion mining techniques from social media

A Survey on Feature Extraction Techniques, Classification Methods and Applications of Sentiment Analysis

Abstract Rapid developments in the era of IoT technologies, coupled with the espousal of social media tools and applications, have promoted the use of data analytics as a means to gain significant insights from unstructured data. Sentiment analysis is an approach that identifies data polarity to classify a text as positive, neutral, or negative. Also referred to as opinion mining or subjective mining, sentiment analysis has applications that range from marketing and customer service to clinical medicine. The application of sentiment analysis in the epoch of big data has proved invaluable in classifying sentiment and, in general, determining opinions from the average person’s frame of mind Several sentiment analysis techniques have been developed over the years. In this regard, this article presents a brief survey on the sentiment analysis applications, as well as feature extraction and sentiment classification techniques. This article surveys various feature extractions techniques and concludes that each technique has its own pros and cons, and can be combined for better results. The survey on classification methods suggests that hybrid methods provide finer results than individual ones. The survey of applications surmises that sentiment analysis as applied to different sectors, helps expand business opportunities. Also, the paper presents a few open challenges in carrying out sentiment analysis

Sustainability analysis on Urban Mobility based on Social Media content

Urban transport became an important element in the promotion of strategies towards sustainability, in fact one of the challenges posed by booming urban populations is the question of mobility. Traditional travel survey methods used to study urban mobility are very expensive, and the data collected are of poor quality. This is mainly explained because of the difficulty of getting a representative sample of the population, and the lack of motivated participants. Therefore, travel surveys are carried out less and less frequently, and the result is that good travel data is not available to develop mobility and travel behaviour studies. Information and Communication Technologies (ICT) offer the opportunity to improve traditional travel survey methods, decreasing bias in the data, reducing respondent burden, and increasing data quality. On the other hand, nowadays the User Generated Content (UGC) is growing very fast in Internet. Social media have become a valuable source for knowledge but there is a big gap in the automatic Sentiment Analysis with Semantic taxonomy annotation of online textual content. The aim of this research is to identify sustainability issues related to urban mobility based in the perceptions and experiences that underlie in the UGC. The methodology follows a quantitative and qualitative content analysis using Sentiment Analysis techniques. This paper demonstrates empirically the feasibility of the automatic identification of the Sustainable Urban Mobility problems in the discourses generated by the UGC, through a powerful ad-hoc software combining Natural Language Processing and Sentiment Analysis field tools. The main contribution of this work is the development of a tool and methodology on sustainability analysis on urban environment. Our approach enriches the data of the traditional surveys, extends traditional analysis with Big-Data methods, using data mining algorithms and Natural Language Processing techniques to extract urban mobility information from Social Media data. These data include important information about activities and travels, and can help to improve our understanding of urban mobility

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

Learning Structured Knowledge from Social Tagging Data A critical review of methods and techniques

For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and the Semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data

Communicating with metaphors: A cross-cultural analysis of technology forums

Metaphor is a cognitive tool that helps create intimacy (Bowles & Katz, 2015). Lately, big data methods have enabled text analysis on a large scale, especially relevant for investigating language use on the Internet. Although several text mining techniques have been used before, the use of big data allows the development of new text analysis methods, especially those capable of sentiment analysis and lemmatization. This paper explores recent changes in cross-cultural communication with regard to metaphor use and its function in communication on the Internet, specifically about mobile phone use. Big data methods were employed to search for selected phrases in cross-cultural communication, and a list of commonly used metaphors was made. Big data technologies used in the survey permitted the identification of the phrases and collocations employed by Internet users. Text mining methods were used, including extraction of the information from unstructured data sources. The survey addressed the users of social media and mobile phone technology forums. They were profiled regarding their country and language, limiting the detailed analysis to English and Polish examples. Results show the growing scale of the creation of new metaphors in cross-cultural communication, and the importance of the cognitive functions of metaphor in a culturally and linguistically diverse environment