This article studies opinion mining from social media with probabilistic logic reasoning. As it is known, Twitter is one of the most active social networks, with millions of tweets sent daily, where ...multiple users express their opinion about traveling, economic issues, political decisions etc. As such, it offers a valuable source of information for opinion mining. In this paper we present OpinionMine, a Bayesian-based framework for opinion mining, exploiting Twitter Data. Initially, our framework imports Tweets massively by using Twitter’s API. Next, the imported Tweets are further processed automatically for constructing a set of untrained rules and random variables. Then, a Bayesian Network is derived by using the set of untrained rules, the random variables and an evidence set. After that, the trained model can be used for the evaluation of new Tweets. Finally, the constructed model can be retrained incrementally, thus becoming more robust. As application domain for the development of our methodology we have selected tourism because it is one of the most popular topics in social media. Our framework can predict users’ intention to visit a place. Among the advantages of our framework is that it follows an incremental learning strategy. That is, the derived model can be retrained incrementally with new training sets thus becoming more robust. Further, our framework can be easily adapted to opinion mining from social media on other topics, whereas the rules of the derived model are constructed in an efficient way and automatically.
Chip purchasing policies of the Original Equipment Manufacturers (OEMs) of laptop computers are characterized by similarity measures and probabilistic rules. Our main goal is to build an expert ...system for predicting purchasing behavior in the semiconductor market. The probabilistic rules and similarity measures are extracted from data of products bought by the OEMs in the semiconductor market over twenty quarters. We present the data collected and different qualitative data mining approaches to analyze and extract rules from the data that best characterize the purchasing behavior of the OEMs. Our analysis of the similar product selection shows that there are two main groups of OEMs buying similar products. Using our probabilistic rules, we obtain an average score of approximately 95% reconstructing quarterly data for a one year window.
We show that every strategy-proof and unanimous probabilistic rule on a binary restricted domain has binary support, and is a probabilistic mixture of strategy-proof and unanimous deterministic ...rules. Examples of binary restricted domains are single-dipped domains, which are of interest when considering the location of public bads. We also provide an extension to infinitely many alternatives.
It is proved that every strategy-proof, peaks-only or unanimous, probabilistic rule defined over a minimally rich domain of single-peaked preferences is a probability mixture of strategy-proof, ...peaks-only or unanimous, deterministic rules over the same domain. The proof employs Farkas’ Lemma and the max-flow min-cut theorem for capacitated networks.
This paper proposes a new framework for rule induction methods, called “layered rule induction”, based on rule layers con- strained by inequalities of statistical indices, such as confidence and ...support. The change of indices with an additional example reflects their sensitivity, and four patterns should be considered if confidence and support are focused on. Then, by using these two pairs of inequalities obtained by analysis, the proposed method classifies a set of formulae into four layers: the rule layer, subrule layer (in and out) and the non-rule layer. Using these layers, updates of probabilistic rules are equivalent to their move- ment between layers. Rules can be extracted from each rule layer. The proposed method was evaluated on datasets regarding headaches and meningitis, and the results show that the proposed method outperforms the conventional methods.
We study
many-to-many
matching with
substitutable
and
cardinally monotonic
preferences. We analyze
stochastic dominance
(
sd
)
Nash equilibria
of the game induced by any
probabilistic stable
matching ...rule. We show that a
unique
match is obtained as the outcome of each sd-Nash equilibrium. Furthermore,
individual-rationality
with respect to the
true
preferences is a necessary and sufficient condition for an equilibrium outcome. In the many-to-one framework, the outcome of each equilibrium in which firms behave
truthfully
is
stable
for the
true
preferences. In the many-to-many framework, we identify an equilibrium in which firms behave truthfully and yet the equilibrium outcome is not stable for the true preferences. However, each
stable
match for the
true
preferences can be achieved as the outcome of such equilibrium.
A soft computing tool, Genetic Algorithm, is employed here to determine the optimized system parameters of GaAs quantum wells for better high-frequency performance under hot electron condition. For a ...particular DC biasing field, it is possible to predict the optimum values of the system parameters, like electron temperature, channel width, carrier concentration, for realizing a particular high-frequency response characterized by a cutoff frequency, a frequency at which AC mobility falls to 0.707 of its low-frequency value. The cutoff frequency decreases with the rise of both the channel width and the carrier concentration, whereas it enhances with the increase of lattice temperature and is found to be higher at higher DC biasing fields.
We consider a probabilistic approach to the problem of assigning k indivisible identical objects to a set of agents with single-peaked preferences. Using the ordinal extension of preferences we ...characterize the class of uniform probabilistic rules by Pareto efficiency, strategy-proofness, and no-envy. We also show that in this characterization no-envy cannot be replaced by anonymity. When agents are strictly risk averse von Neumann-Morgenstern utility maximizer, then we reduce the problem of assigning k identical objects to a problem of allocating the amount k of an infinitely divisible commodity. PUBLICATION ABSTRACT
We consider a probabilistic approach to the problem of assigning k indivisible identical objects to a set of agents with single-peaked preferences. Using the ordinal extension of preferences we ...characterize the class of uniform probabilistic rules by Pareto efficiency, strategy-proofness, and no-envy. We also show that in this characterization no-envy cannot be replaced by anonymity. When agents are strictly risk averse von Neumann-Morgenstern utility maximizer, then we reduce the problem of assigning k identical objects to a problem of allocating the amount k of an infinitely divisible commodity. Copyright Springer-Verlag Berlin/Heidelberg 2003
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