Planning in Reactive Environments

Milani, Alfredo; Poggioni, Valentina

doi:10.1111/j.1467-8640.2007.00315.x

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…However, these works do not discuss about a user's interest on the topic being discussed by the users. Our approach is different from others (Milani and Poggioni, 2007;Ukey et al, 2010;Milani and Santucci, 2012) as we compute the interests of a particular user and the users of a certain location by taking their sentiments (positively or negatively inclined) towards certain topics.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Extraction and Classification for the Analysis of Users Interest in Tweets

Milani

Niyogi

Mangal

et al. 2018

Int J of Web Info Systems

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Purpose -This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics are seen positively by the user.Design/methodology/approach -The proposed approach is based on the combination of sentiment extraction and classification analysis of tweet to extract the topic of interest. The proposed hybrid method is original. The topic extraction phase uses a method based on semantic distance in the WordNet taxonomy. Sentiment extraction uses NLPcore.Findings -The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results and confirm the suitability of the approach combining sentiment and categorization for the topic of interest extraction.Research limitations/implications -The hybrid method combining sentiment extraction and classification for user positive topics represents a novel contribution with many potential applications.Practical implications -The functionality of positive topic extraction is very useful as a component in the design of a recommender system based on user profiling from Twitter user behaviors.Social implications -The application of the proposed method in short-text social network can be massive and beyond the applications in tweets.Originality/value -There are few works that have considered both sentiment analysis and classification to find out users' interest. The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results.

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Section: Related Workmentioning

confidence: 99%

Sentiment Extraction and Classification for the Analysis of Users Interest in Tweets

Milani

Niyogi

Mangal

et al. 2018

Int J of Web Info Systems

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“…TwitterMonitorsystem is presented in Mathioudakis and Loudas (2010) who detect the real-time events in a defined time window (Leung et al, 2012;Milani and Santucci, 2012;Milani and Poggioni, 2007). This process is completed in three steps.…”

Section: Events Semantic Similaritymentioning

confidence: 99%

Analysis of tweets to find the basis of popularity based on events semantic similarity

Mudgal

Niyogi

Milani

et al. 2018

IJWIS

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Purpose The purpose of this paper is to propose and experiment a framework for analysing the tweets to find the basis of popularity of a person and extract the reasons supporting the popularity. Although the problem of analysing tweets to detect popular events and trends has recently attracted extensive research efforts, not much emphasis has been given to find out the reasons behind the popularity of a person based on tweets. Design/methodology/approach In this paper, the authors introduce a framework to find out the reasons behind the popularity of a person based on the analysis of events and the evaluation of a Web-based semantic set similarity measure applied to tweets. The methodology uses the semantic similarity measure to group similar tweets in events. Although the tweets cannot contain identical hashtags, they can refer to a unique topic with equivalent or related terminology. A special data structure maintains event information, related keywords and statistics to extract the reasons supporting popularity. Findings An implementation of the algorithms has been experimented on a data set of 218,490 tweets from five different countries for popularity detection and reasons extraction. The experimental results are quite encouraging and consistent in determining the reasons behind popularity. The use of Web-based semantic similarity measure is based on statistics extracted from search engines, it allows to dynamically adapt the similarity values to the variation on the correlation of words depending on current social trends. Originality/value To the best of the authors’ knowledge, the proposed method for finding the reason of popularity in short messages is original. The semantic set similarity presented in the paper is an original asymmetric variant of a similarity scheme developed in the context of semantic image recognition.

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“…In contrast, reactive planning operates in a timely fashion with highly dynamic, non-deterministic and unpredictable environments, assuming uncertainty in the world and the existence of multiple outcomes due to action failures or exogenous events [34]. Our reactive planner is not a temporal planner but it is designed to return timely responses in highly dynamic environments.…”

Section: Related Workmentioning

confidence: 99%

Reactive execution for solving plan failures in planning control applications

Guzmán

Castejon

Onaindía

et al. 2015

ICA

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Abstract. We present a novel reactive execution model for planning control applications which repairs plan failures at runtime. Our proposal is a domain-independent regression planning model which provides good-quality responses in a timely fashion. The use of a regressed model allows us to work exclusively with the sufficient and necessary information to deal with the plan failure. The model performs a time-bounded process that continuously operate on the plan to recover from incoming failures. This process guarantees there always exists a plan repair for a plan failure at anytime. The model is tested on a simulation of a real-world planetary space mission and on a well-known vehicle routing problem.

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Planning in Reactive Environments

Cited by 18 publications

References 21 publications

Sentiment Extraction and Classification for the Analysis of Users Interest in Tweets

Sentiment Extraction and Classification for the Analysis of Users Interest in Tweets

Analysis of tweets to find the basis of popularity based on events semantic similarity

Reactive execution for solving plan failures in planning control applications

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