Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

A literature survey of methods for analysis of subjective language

Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area

Topic-dependent sentiment analysis of financial blogs

While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches

Mining online diaries for blogger identification

In this paper, we present an investigation of authorship identification on personal blogs or diaries, which are different from other types of text such as essays, emails, or articles based on the text properties. The investigation utilizes couple of intuitive feature sets and studies various parameters that affect the identification performance. Many studies manipulated the problem of authorship identification in manually collected corpora, but only few utilized real data from existing blogs. The complexity of the language model in personal blogs is motivating to identify the correspondent author. The main contribution of this work is at least three folds. Firstly, we utilize the LIWC and MRC feature sets together, which have been developed with Psychology background, for the first time for authorship identification on personal blogs. Secondly, we analyze the effect of various parameters, and feature sets, on the identification performance. This includes the number of authors in the data corpus, the post size or the word count, and the number of posts for each author. Finally, we study applying authorship identification over a limited set of users that have a common personality attributes. This analysis is motivated by the lack of standard or solid recommendations in literature for such task, especially in the domain of personal blogs. The results and evaluation show that the utilized features are compact while their performance is highly comparable with other larger feature sets. The analysis also confirmed the most effective parameters, their ranges in the data corpus, and the usefulness of the common users classifier in improving the performance, for the author identification task

A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor

The tourism industry has been promoting its products and services based on the reviews that people often write on travel websites like TripAdvisor.com, Booking.com and other platforms like these. These reviews have a profound effect on the decision making process when evaluating which places to visit, such as which restaurants to book, etc. In this contribution is presented a cloud based software tool for the massive analysis of this social media data (TripAdvisor.com). The main characteristics of the tool developed are: i) the ability to aggregate data obtained from social media; ii) the possibility of carrying out combined analyses of both people and comments; iii) the ability to detect the sense (positive, negative or neutral) in which the comments rotate, quantifying the degree to which they are positive or negative, as well as predicting behaviour patterns from this information; and iv) the ease of doing everything in the same application (data downloading, pre-processing, analysis and visualisation). As a test and validation case, more than 33.500 revisions written in English on restaurants in the Province of Granada (Spain) were analyse