Abstract

Sentiment analysis is a discipline of Natural Language Processing which deals with analysing the subjectivity of the data. It is an important task with both commercial and academic functionality. Languages like English have several resources which assist in the task of sentiment analysis. SentiWordNet for English is one such important lexical resource that contains subjective polarity for each lexical item. With growing data in native vernacular, there is a need for language-specific SentiWordNet (s). In this paper, we discuss a generic approach followed for the development of a Tamil SentiWordNet using currently available resources in English. For Tamil SentiWordNet, a substantial agreement Fleiss Kappa score of 0.663 was obtained after verification from Tamil annotators. Such a resource would serve as a baseline for future improvements in the task of sentiment analysis specific to Tamil data.

Abstract

Dynamic resource assignment is a common problem in multi-agent systems. We consider scenarios in which dynamic agents have preferences about assignments and the resources that can be assigned using online auctions. We study the trade-off between the following online auction properties: (i) truthfulness, (ii) expressiveness, (iii) efficiency, and (iv) average case performance. We theoretically and empirically compare four different online auctions: (i) Arrival Priority Serial Dictatorship, (ii) Split Dynamic VCG, (iii) e-Action, and (iv) Online Ranked Competition Auction. The latter is a novel design based on the competitive secretary problem. We show that, in addition to truthfulness and algorithmic efficiency, the degree of competition also plays an important role in selecting the best algorithm for a given context

Abstract

We consider a general crowdsourcing setting with strategic workers whose qualities are unknown and design a multiarmed bandit (MAB) mechanism, CrowdUCB, which is deterministic, regret minimizing, and offers immediate payments to the workers. The problem involves sequentially selecting workers to process tasks in order to maximize the social welfare while learning the qualities of the strategic workers (strategic about their costs). Existing MAB mechanisms are either: (a) deterministic which potentially cause significant loss in social welfare, or (b) randomized which typically lead to high variance in payments. CrowdUCB completely addresses the above problems with the following features: (i) offers deterministic payments, (ii) achieves logarithmic regret in social welfare, (iii) renders allocations more effective by allocating blocks of tasks to a worker instead of a single task, and (iv) offers payment to a worker immediately upon completion of an assigned block of tasks. CrowdUCB is a mechanism with learning that learns the qualities of the workers while eliciting their true costs, irrespective of whether or not the workers know their own qualities. We show that CrowdUCB is ex-post individually rational (EPIR) and ex-post incentive compatible (EPIC) when the workers do not know their own qualities and when they update their beliefs in sync with the requester. When the workers know their own qualities, CrowdUCB is EPIR and ε−EPIC where ε is sub-linear in terms of the number of tasks.

Abstract

Crowdfunding is emerging as a popular means to generate funding from citizens for public projects. This is popularly known as civic crowdfunding. In this paper, we focus on crowdfunding public projects with provision point: these are projects in which contributions must reach a predetermined threshold in order for the project to be provisioned. On web based civic crowdfunding platforms, the success of crowdfunding public projects has been somewhat mixed. In this paper, our objective is to design a mechanism that improves the success of crowdfunding public projects. In particular, we propose a class of mechanisms for crowdfunding platforms with sequentially arriving agents. This class of mechanisms induces an extensive form game for agents arriving on the platform and we show that the game has a non-empty set of sub-game perfect equilibria at which the project is fully funded. We call this new class of mechanisms Provision Point Mechanism with Securities (PPS). The novelty of PPS lies in the use of a prediction market to incentivize agents to contribute in proportion to their true value for the project and to contribute as soon as they arrive at the crowdfunding platform. Different variations of PPS are possible depending on the underlying prediction market. In this paper, we use a cost function (or equivalently, scoring rule) based prediction market; in fact, we specify the requirements that a cost function should satisfy to be used in PPS. We study and compare two specific instances of PPS: (1) Logarithmic Market Scoring Rule based and (2) Quadratic Scoring Rule based. We also discuss the considerations that should guide the choice of the cost function when deploying our mechanism on crowdfunding platforms

Abstract

Motivated by web based marketplaces where the number of bidders in an auction is a small subset of potential bidders, we consider auctions where the auctioneer (seller) wishes to increase her revenue and/or social welfare by expanding the pool of participants. To this end, the seller crowdsources this task by offering a referral bonus to the participants. With the introduction of referrals, a participant can now bid and/or refer other agents to bid. We call our auctions crowdsourced referral auctions since the seller exploits the knowledge that agents have about other potential participants in the crowd. We introduce the notion of price of locality to quantify the loss in social welfare due to restricted (local) access of the seller to potential bidders. We introduce the notion of Crowdsourced Referral Auction Mechanisms (CRAMs), propose two novel versions of CRAMs and study the induced referral game in the canonical context of an auction for selling a single indivisible item. We compare their revenue performance and game theoretic properties and show that both of them outperform the baseline auction without referrals

Abstract

Civic Crowdfunding is emerging as a popular means to mobilize funding from citizens for public projects. A popular mechanism deployed on civic crowdfunding platforms is a provision point mechanism, wherein, the total contributions must reach a predetermined threshold in order for the project to be provisioned (undertaken). Such a mechanism may have multiple equilibria; unfortunately, in many of these equilibria, the project is not funded even if it is highly valued among the agents.Recent work has proposed mechanisms with refund bonuses where the project gets funded in equilibrium if its net value is higher than a threshold among the agents who are aware of the crowdfunding effort. In this paper, we formalize the notion of social desirability of a public project and propose mechanisms which use the idea of referrals to expand the pool of participants and achieve an equilibrium in which the project gets funded if its net value exceeds a threshold among the entire agent population. We call this new class of mechanisms Referral-Embedded Provision Point Mechanisms (REPPM). We specifically propose two variants of REPPM and both these mechanisms have the remarkable property that, at equilibrium, referral bonuses are offered but there is no need for actual payment of these bonuses. We establish that any given agent’s equilibrium strategy is to refer other agents and to contribute in proportion to the agent’s true value for the project. By referring others to contribute, an agent can, in fact, reduce his equilibrium contribution. In our view, the proposed mechanisms will lead to an increase in the number of projects that are funded on civic crowdfunding platforms.

Abstract

These days' discussion forums provide dependable solutions to the problems related to multiple domains and areas. However, due to the presence of huge amount of less-informative/inappropriate posts, the identification of the appropriate problem-solution pairs has become a challenging task. The emergence of a variety of topics, domains and areas has made the task of manual labelling of the problem solution-post pairs a very costly and time consuming task. To solve these issues, we concentrate on deep semantic and logical relation between terms. For this, we introduce a novel semantic correlation graph to represent the text. The proposed representation helps us in the identification of topical and semantic relation between terms at a fine grain level. Next, we apply the improved version of personalised pagerank using random walk with restarts. The main aim is to improve the rank score of terms having direct or indirect relation with terms in the given question. Finally, we introduce the use of the node overlapping version of GAAC to find the actual span of answer text. Our experimental results show that the devised system performs better than the existing unsupervised systems.

Abstract

In this paper, we present a novel N-gram (N> = 1) filtration technique for keyphrase extraction. To filter the sophisticated candidate keyphrases (N-grams), we introduce the combined use of: 1) statistical feature (obtained by using weighted betweenness centrality scores of words, which is generally used to identify the border nodes/edges in community detection techniques); 2) co-location strength (calculated by using nearest neighbour Dbpedia texts). We also introduce the use of N-gram (N> = 1) graph, which reduces the bias effect of lower length N-grams in the ranking process and preserves the semantics of words (phraseness), based upon local context. To capture the theme of the document and to reduce the effect of noisy terms in the ranking process, we apply an information theoretic framework for key-player detection on the proposed N-gram graph. Our experimental results show that the devised system performs better than the current state-of-the-art unsupervised systems and comparable/better than supervised systems.

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.