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.

Spectators are increasingly using social platforms to express their opinions and share their emotions during big public events. Those reactions reveal the subjective perception of the event and extend its understanding. This has motivated us to develop a system to explore and visualize volume, patterns, and trends of user sentiments as they evolve over time.  Previous work in sentiment analysis and opinion mining has addressed these issues. But the majority of them distinguish only two polarity categories, leaving a more detailed and insightful analysis to be desired.  In this paper, we suggest using a fine-grained, multi-category emotion model to classify and visualize users' emotional reactions in public events. We describe EmotionWatch, a tool that constructs visual summaries of public emotions, and apply it to the 2012 Olympics as a test case. We report findings from a user study evaluating the usability of the tool and validating the emotion model. Results show that users prefer a more detailed inspection of public emotions over the simplified analysis. Despite its complexity, users were able to effectively grasp, understand, and interpret the emotional reactions using EmotionWatch. The same user study also pointed out few design improvements for the future development of analogous systems.

The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.

College students often have to team up for classprojects, and they select each other based not only onpast performance (e.g., grades) but also on whetherthey get along (e.g., whether they trust each other).There has not been any study on the relationshipbetween team formation for class projects and socialmedia. To fix that, we ask a group of university studentsto tell us with whom they wish to work, gather theironline Facebook data, and test the predictors of teamformation. We find that self-organized selection ofteam members does not strongly depend on pastgrades but rather on Facebook-derived proxies fortie strength, popularity, and homophily. These resultshave important theoretical implications for the teamformation literature and practical implications foronline educational platforms.

Information Extraction (IE) is the technique for transforming unstructured textual data into structured representation that can be understood by machines. The exponential growth of the Web generates an exceptional quantity of data for which automatic knowledge capture is essential. This work describes the methodology for Web scale Information Extraction adopted by the LODIE project (Linked Open Data Information Extraction). LODIE aims to develop Information Extraction techniques able to (i) scale at web level and (ii) adapt to user information need. The core idea behind LODIE is the usage of Linked Open Data, a very large-scale information resource, as a ground-breaking solution for IE, which provides invaluable annotated data on a growing number of domains.

The emergence and ubiquity of online social networks have enriched web data with evolving interactions and communities both at mega-scale and in real-time. This data offers an unprecedented opportunity for studying the interaction between society and disease outbreaks. The challenge we describe in this data paper is how to extract and leverage epidemic outbreak insights from massive amounts of social media data and how this exercise can benefit medical professionals, patients, and policymakers alike. We attempt to prepare the research community for this challenge with four datasets. Publishing the four datasets will commoditize the data infrastructure to allow a higher and more efficient focal point for the research community.

Due to the explosive growth of the Internet online reviews, we can easily collect a large amount of labeled reviews from different domains. But only some of them are beneficial for training a desired target-domain sentiment classifier. Therefore, it is important for us to identify those samples that are the most relevant to the target domain and use them as training data. To address this problem, a novel approach, based on instance selection and instance weighting via PU learning, is proposed. PU learning is used at first to learn an in-target-domain selector, which assigns an in-target-domain probability to each sample in the training set. For instance selection, the samples with higher in-target-domain probability are used as training data; For instance weighting, the calibrated in-target-domain probabilities are used as sampling weights for training an instance-weighted naive Bayes model, based on the principle of maximum weighted likelihood estimation. The experimental results prove the necessity and effectiveness of the approach, especially when the size of training data is large. It is also proved that the larger the Kullback-Leibler divergence between the training and test data is, the more effective the proposed approach will be.

In this paper we present an approach aimed at enriching the Open Information Extraction paradigm with semantic relation ontologization by integrating syntactic and semantic features into its workflow. To achieve this goal, we combine deep syntactic analysis and distributional semantics using a shortest path kernel method and soft clustering. The output of our system is a set of automatically discovered and ontologized semantic relations.

In the literature, various approaches have been proposedto address the domain adaptation problem in sentiment classification (also called cross-domainsentiment classification). However, the adaptation performance normally much suffers when the data distributionsin the source and target domains differ significantly. In this paper, we suggest to perform activelearning for cross-domain sentiment classification by actively selecting a smallamount of labeled data in the target domain. Accordingly, we propose an novel activelearning approach for cross-domain sentiment classification. First, we traintwo individual classifiers, i.e., the source and target classifiers with thelabeled data from the source and target respectively. Then, the two classifiersare employed to select informative samples with the selection strategy of QueryBy Committee (QBC). Third, the two classifier is combined to make theclassification decision. Importantly, the two classifiers are trained by fullyexploiting the unlabeled data in the target domain with the label propagation(LP) algorithm. Empirical studies demonstrate the effectiveness of our active learning approach for cross-domainsentiment classification over some strong baselines.

This paper proposes an effective approach to model the emotional space of words to infer their Sense Sentiment Similarity (SSS). SSS reflects the distance between the words regarding their senses and underlying sentiments. We propose a probabilistic approach that is built on a hidden emotional model in which the basic human emotions are considered as hidden. This leads to predict a vector of emotions for each sense of the words, and then to infer the sense sentiment similarity. The effectiveness of the proposed approach is investigated in two Natural Language Processing tasks: Indirect yes/no Question Answer Pairs Inference and Sentiment Orientation Prediction.