An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media Content

The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.

Business-Oriented Analytics With Social Network of Things

In the modern era of technological advancements, internet of things (IoT) and social network of things (SNoT) have gained vitality with the extensive application of sensors for accumulation of socially relevant data. A colossal amount of social data collected becomes unfeasible to process and deliver with progress in time and domain. Therefore, a major problem lies in analysis, interpretation, and understanding of the huge amount of social data. This challenge has been greatly leveraged by context-aware computing, which permits storing context information so that meaningful analysis of data can be achieved. Also, the importance of context-aware social networking and network diffusion is elaborated with the aim to develop effective solutions to issues in this domain. The main concept here is people around a person share common experiences with that person, which in turn can be made interactive, thereby leading to collective and quick resolving of problems. Social network of things is closely coupled with context awareness to make interpretation of big data easier and compatible to recent trends.

A Novel Algorithm for Sentiment Analysis of Online Movie Reviews

Others' opinions can be decisive while choosing among various options, especially when those choices involve worthy resources like spending time and money buying products or services. Customers relying on their peers' past reviews on e-commerce websites or social media have drawn a considerable interest to sentiment analysis due to realization of its commercial and business benefits. Sentiment analysis can be exercised on movie reviews, blogs, customer feedback, etc. This chapter presents a novel approach to perform sentiment analysis of movie reviews given by users on different websites. Also, challenges like presence of thwarted words, world knowledge, and subjectivity detection in sentiments are addressed in this chapter. The results are validated by using two supervised machine learning approaches, k-nearest neighbor and naive Bayes, both on method of sentiment analysis without addressing aforementioned challenges and on proposed method of sentiment analysis with all challenges addressed. Empirical results show that proposed method outperformed the one that left challenges unaddressed.

Social Networking Data Analysis Tools and Services

Social networks have drawn remarkable attention from IT professionals and researchers in data sciences. They are the most popular medium for social interaction. Online social networking (OSN) can be defined as involving networking for fun, business, and communication. Social networks have emerged as universally accepted communication means and boomed in turning this world into a global town. OSN media are generally known for broadcasting information, activities posting, contents sharing, product reviews, online pictures sharing, professional profiling, advertisements and ideas/opinion/sentiment expression, or some other stuff based on business interests. For the analysis of the huge amount of data, data mining techniques are used for identifying the relevant knowledge from the huge amount of data that includes detecting trends, patterns, and rules. Data mining techniques, machine learning, and statistical modeling are used to retrieve the information. For the analysis of the data, three methods are used: data pre-processing, data analysis, and data interpretation.

Authorship Attribution for Online Social Media

This chapter gives a comprehensive knowledge of various machine learning classifiers to achieve authorship attribution (AA) on short texts, specifically tweets. The need for authorship identification is due to the increasing crime on the internet, which breach cyber ethics by raising the level of anonymity. AA of online messages has witnessed interest from many research communities. Many methods such as statistical and computational have been proposed by linguistics and researchers to identify an author from their writing style. Various ways of extracting and selecting features on the basis of dataset have been reviewed. The authors focused on n-grams features as they proved to be very effective in identifying the true author from a given list of known authors. The study has demonstrated that AA is achievable on the basis of selection criteria of features and methods in small texts and also proved the accuracy of analysis changes according to combination of features. The authors found character grams are good features for identifying the author but are not yet able to identify the author independently.

Thwarting Spam on Facebook

Spam on the online social networks (OSNs) is evolving as a prominent problem for the users of these networks. Spammers often use certain techniques to deceive the OSN users for their own benefit. Facebook, one of the leading OSNs, is experiencing such crucial problems at an alarming rate. This chapter presents a methodology to segregate spam from legitimate posts using machine learning techniques: naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The textual, image, and video features are used together, which wasn't considered by the earlier researchers. Then, 1.5 million posts and comments are extracted from archival and real-time Facebook data, which is then pre-processed using RStudio. A total of 30 features are identified, out of which 10 are the best informative for identification of spam vs. ham posts. The entire dataset is shuffled and divided into three ratios, out of which 80:20 ratio of training and testing dataset provides the best result. Also, RF classifier outperforms NB and SVM by achieving overall F-measure 89.4% on the combined feature set.

Social Network Analysis

Over the last decade, technology has thrived to provide better, quicker, and more effective platforms to help individuals connect and disseminate information to other individuals. The increasing popularity of these networks and its huge content in the form of text, images, and videos provides new opportunities for data analytics in the context of social networks. This motivates data mining experts and researchers to deploy various mining apparatus and application-specific tools for analysing the massive, intricate, and dynamic social media knowledge. The research detailed in this chapter would entail major social network concepts with data analysis techniques. Moreover, it gives insight to representation and modelling of social networks with research datasets and tools.

Knowledge Discovery Using Data Stream Mining

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets, which continuously and rapidly grow over time, are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous literature that has been published in this domain over the past few years. Due to this, isolating the correct study would be grueling task for researchers and practitioners. While addressing a real-world problem, it would be difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it is an amalgamation of all techniques used for data stream mining.

Impact of Sarcasm in Sentiment Analysis Methodology

Sentiment of analysis is a study area applied to numerous environments (financial, political, academic, business, and communication) whose purpose is to search for messages posted on social media, and through these to identify and classify people's opinions about particular item as positive or negative. Rating the sentiment expressed in opinionated messages is such an important task that currently companies invest a lot of money in collecting this type of information and the development of methods and techniques to classify the sentiment that they express, so that they can use the results as useful information in preparing marketing and sales strategies efficiently. However, one of the major problems facing the feelings of analysis is the difficulty of methods to properly analyze messages with sarcastic and/or ironic content, as these linguistic phenomena have the characteristic of transforming the polarity or meaning of a positive or negative statement into its opposite.

Trust and Credibility Analysis of Websites

Trust on any online information is psychosomatic and hidden by nature. The choice is in the hands of the information seeker to consider, evaluate, and confirm the contents of the websites before using it. This makes a sharp concern for websites dealing with sensitive topics like health, research, or academics. There is no benchmark or tool that tells or characterises about making these “trust” decisions. Although web users make such decisions after considering numerous factors, still there are no such criteria to fulfil the underlying principle to deal with such decision making. This chapter is an effort to resolve the problem of how to measure the content provided by any website in terms of its credibility. Various models have been projected in this chapter to identify several factors pertaining to the credibility of content and users' trust on any website and accordingly analyse the identified factors to assess the websites.