Abstract

An overwhelming amount of data is generated everyday on social media, encompassing a wide spectrum of topics. With almost every business decision depending on customer opinion, mining of social media data needs to be quick and easy. For a data analyst to keep up with the agility and the scale of the data, it is impossible to bank on fully supervised techniques to mine topics and their associated sentiments from social media. Motivated by this, we propose a weakly supervised approach (named, TweetGrep) that lets the data analyst easily define a topic by few keywords and adapt a generic sentiment classifier to the topic–by jointly modeling topics and sentiments using label regularization. Experiments with diverse datasets show that TweetGrep beats the state-ofthe-art models for both the tasks of retrieving topical tweets and analyzing the sentiment of the tweets (average improvement of 4.97% and 6.91% respectively in terms of area under the curve). Further, we show that TweetGrep can also be adopted in a novel task of hashtag disambiguation, which significantly outperforms the baseline methods.

Abstract

In this paper, we consider the problem of learning representations for authors from bibliographic co-authorship networks. Existing methods for deep learning on graphs, such as DeepWalk, suffer from link sparsity problem as they focus on modeling the link information only. We hypothesize that capturing both the content and link information in a unified way will help mitigate the sparsity problem. To this end, we present a novel model'Author2Vec', which learns low-dimensional author representations such that authors who write similar content and share similar network structure are closer in vector space. Such embeddings are useful in a variety of applications such as link prediction, node classification, recommendation and visualization. The author embeddings we learn are empirically shown to outperform DeepWalk by 2.35% and 0.83% for link prediction and clustering task respectively.

Abstract

Extractive summarization techniques typically aim to maximize the information coverage of the summary with respect to the original corpus and report accuracies in ROUGE scores. Automated text summarization techniques should consider the dimensions of comprehensibility, coherence and readability. In the current work, we identify the discourse structure which provides the context for the creation of a sentence. We leverage the information from the structure to frame a monotone (non-decreasing) sub-modular scoring function for generating comprehensible summaries. Our approach improves the overall quality of comprehensibility of the summary in terms of human evaluation and gives sufficient content coverage with comparable ROUGE score. We also formulate a metric to measure summary comprehensibility in terms of Contextual Independence of a sentence. The metric is shown to be representative of human judgement of text comprehensibility.

Abstract

Doc2Sent2Vec is an unsupervised approach to learn lowdimensional feature vector (or embedding) for a document. This embedding captures the semantics of the document and can be fed as input to machine learning algorithms to solve a myriad number of applications in the field of data mining and information retrieval. Some of these applications include document classification, retrieval, and ranking. The proposed approach is two-phased. In the first phase, the model learns a vector for each sentence in the document using a standard word-level language model. In the next phase, it learns the document representation from the sentence sequence using a novel sentence-level language model. Intuitively, the first phase captures the word-level coherence to learn sentence embeddings, while the second phase captures the sentence-level coherence to learn document embeddings. Compared to the state-of-the-art models that learn document vectors directly from the word sequences, we hypothesize that the proposed decoupled strategy of learning sentence embeddings followed by document embeddings helps the model learn accurate and rich document representations. We evaluate the learned document embeddings by considering two classification tasks: scientific article classification and Wikipedia page classification. Our model outperforms the current state-of-the-art models in the scientific article classification task by∼ 12.07% and the Wikipedia page classification task by∼ 6.93%, both in terms of F1 score. These results highlight the superior quality of document embeddings learned by the Doc2Sent2Vec approach

Abstract

With improving efficiency and cost effectiveness of public cloud systems, there has been a growing trend to complement in-house cloud environment with them. We propose an open solution to the problem of distributing jobs in a hybrid environment, as a substitute to hadoop systems which are cloud specific. The proposed system, MultiStack, is a big data orchestration platform for deploying big data jobs across multiple cloud providers. The specific architecture elaborated in this paper uses Amazon Web Services as the public cloud provider and Openstack as the private cloud framework. Our solution supports complete Hadoop ecosystem tools - hive, pig, Hbase, oozie etc. and on-demand scaling of Hadoop clusters. The proposed framework aims at reducing the Job completion time on workloads along with decrease in cost using Spot Instance provisioning compared to on-demand provisioning. This is achieved by providing two modes of operation: Proactive scheduling and Reactive scheduling, which takes into account user providing job characteristics(eg memory, cpu, etc), quota limitation and business objectives. From our experiments, we conclude that Multistack is able to reduce average job completion time by 30-37% with minimal increase in cost.

Abstract

The problem of providing optimal assignment for backend storage is a central problem in the design of cloud systems. It has taken a further central role as a result of growing heterogeneity from emerging Software Defined Storage systems. In this paper, we propose a solution to optimal IO Workload assignment using statistical modelling to estimate measures of performance such as Throughput, IOPS, et al. The proposed system uses support vector regression to estimate the performance of individual IO Workloads on each available SDS system for optimal assignment. As a proof of concept, we demonstrate our solution in a heterogeneous environment comprising of HDFS, GlusterFS, and Ceph. We first show the accuracy of estimation of throughput and IOPS with values of coefficient of determination over 0.65 in all cases. We further show the analysis of using this regression model to classify workloads to respective SDS backend that will maximize throughput.

Abstract

Research in social media analysis is experiencing a recent surge with a large number of works applying representation learning models to solve high-level syntactico-semantic tasks such as sentiment analysis, semantic textual similarity computation, hashtag prediction and so on. Although the performance of the representation learning models are better than the traditional baselines for the tasks, little is known about the core properties of a tweet encoded within the representations. Understanding these core properties would empower us in making generalizable conclusions about the quality of representations. Our work presented here constitutes the first step in opening the black-box of vector embedding for social media posts, with emphasis on tweets in particular. In order to understand the core properties encoded in a tweet representation, we evaluate the representations to estimate the extent to which it can model each of those properties such as tweet length, presence of words, hashtags, mentions, capitalization, and so on. This is done with the help of multiple classifiers which take the representation as input. Essentially, each classifier evaluates one of the syntactic or social properties which are arguably salient for a tweet. This is also the first holistic study on extensively analysing the ability to encode these properties for a wide variety of tweet representation models including the traditional unsupervised methods (BOW, LDA), unsupervised representation learning methods (Siamese CBOW, Tweet2Vec) as well as supervised methods (CNN, BLSTM).

Abstract

Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack of a suitable annotated dataset. We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media. In this paper, we introduce learning sub-word level representations in our LSTM (Subword-LSTM) architecture instead of character-level or word-level representations. This linguistic prior in our architecture enables us to learn the information about sentiment value of important morphemes. This also seems to work well in highly noisy text containing misspellings as shown in our experiments which is demonstrated in morpheme-level feature maps learned by our model. Also, we hypothesize that encoding this linguistic prior in the Subword-LSTM architecture leads to the superior performance. Our system attains accuracy 4-5% greater than traditional approaches on our dataset, and also outperforms the available system for sentiment analysis in Hi-En code-mixed text by 18%.

Abstract

A highly stable and novel SMSF MZI based direct current sensor, capable of sensing current (0A to 4.5 A) and sensitivity of 0.3222 nm/A 2 at resonance wavelength with regression coefficient (R 2) value 0.9912 is developed.

Abstract

Graph-OLAP is an online analytical framework which allows us to obtain various projections of a graph, each of which helps us view the graph along multiple dimensions and multiple levels. Given a series of snapshots of a temporal heterogeneous graph, we aim to find interesting projections of the graph which have anomalous evolutionary behavior. Detecting anomalous projections in a series of such snapshots can be helpful for an analyst to understand the regions of interest from the temporal graph. Identifying such semantically related regions in the graph allows the analyst to derive insights from temporal graphs which enables her in making decisions. While most of the work on temporal outlier detection is performed on nodes, subgraphs and communities, we are the first to propose detection of evolutionary graph cuboid outliers. Further, we perform this detection in a query sensitive manner. Thus, an evolutionary graph cuboid outlier is a projection (or cuboid) of a snapshot of the temporal graph such that it contains an unexpected number of matches for the query with respect to other cuboids both in the same snapshot as well as in the other snapshots. Identifying such outliers is challenging because (1) the number of cuboids per snapshot could be large, and (2) number of snapshots could itself be large. We model the problem by predicting the outlier score for each cuboid in each snapshot. We propose to build subspace ensemble regression models to learn (a) the behavior of a cuboid across different snapshots, and (b) the behavior of all the cuboids in a given snapshot. Experimental results on both synthetic and real datasets show the effectiveness of the proposed algorithm in discovering evolutionary graph cuboid outliers.