Tourist review analytics using complex networks

A number of techniques for Natural Language Processing (shortly, NLP) based on graph representations were developed. Usually they target a specific NLP task, such as: text summarisation, syntactic parsing, word sense disambiguation, ontology construction, sentiment and subjectivity analysis, or text clustering. In this paper we explore complex network representation of tourist reviews for extracting lexical and quantitative features of the review text. The most important contribution of our proposal consists of defining a new method for keywords extraction using Complex Network ranking metrics.

[1]  Dan Cristea,et al.  Summarisation Through Discourse Structure , 2005, CICLing.

[2]  Mirella Lapata,et al.  An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Costin Badica,et al.  Relating the Opinion Holder and the Review Accuracy in Sentiment Analysis of Tourist Reviews , 2014, KSEM.

[4]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.

[5]  Filippo Radicchi,et al.  Who Is the Best Player Ever? A Complex Network Analysis of the History of Professional Tennis , 2011, PloS one.

[6]  Dan Cristea,et al.  Towards an Automated Semiotic Analysis of the Romanian Political Discourse , 2013, Comput. Sci. J. Moldova.

[7]  Dragomir R. Radev,et al.  Networks and Natural Language Processing , 2008, AI Mag..

[8]  Ronaldo Menezes,et al.  A self-organized approach for detecting communities in networks , 2014, Social Network Analysis and Mining.

[9]  Yan Zhou,et al.  Minimum Spanning Tree Based Clustering Algorithms , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[10]  Dominic Widdows,et al.  A Graph Model for Unsupervised Lexical Acquisition , 2002, COLING.

[11]  Florentin Smarandache,et al.  Five Computational Actions in Information Processing , 2014 .

[12]  Robert E. Tarjan,et al.  Graph Clustering and Minimum Cut Trees , 2004, Internet Math..

[13]  Radu Razvan Slavescu,et al.  LELA - a natural language processing system for Romanian tourism , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[14]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[15]  Mikko Kivelä,et al.  Generalizations of the clustering coefficient to weighted complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Noah A. Smith,et al.  Dependency Parsing , 2009, Encyclopedia of Artificial Intelligence.

[17]  Kalev Leetaru,et al.  Culturomics 2.0: Forecasting large-scale human behavior using global news media tone in time and space , 2011, First Monday.

[18]  Rada Mihalcea,et al.  Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization , 2004, ACL.

[19]  R. Lambiotte,et al.  Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks , 2008, IEEE Transactions on Network Science and Engineering.

[20]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[21]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[22]  Stanley Milgram,et al.  An Experimental Study of the Small World Problem , 1969 .

[23]  Florentina Hristea,et al.  The Naïve Bayes Model for Unsupervised Word Sense Disambiguation: Aspects Concerning Feature Selection , 2012 .

[24]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[25]  S. M.G. Caldeira,et al.  The network of concepts in written texts , 2006 .

[26]  Albert-László Barabási,et al.  Scale-Free Networks: A Decade and Beyond , 2009, Science.

[27]  Karen Spärck Jones.  A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.