Detecting Evolution of Concepts based on Cause-Effect Relationships in Online Reviews

Analyzing how technology evolves is important for understanding technological progress and its impact on society. Although the concept of evolution has been explored in many domains (e.g., evolution of topics, events or terminology, evolution of species), little research has been done on automatically analyzing the evolution of products and technology in general. In this paper, we propose a novel approach for investigating the technology evolution based on collections of product reviews. We are particularly interested in understanding social impact of technology and in discovering how changes of product features influence changes in our social lives. We address this challenge by first distinguishing two kinds of product-related terms: physical product features and terms describing situations when products are used. We then detect changes in both types of terms over time by tracking fluctuations in their popularity and usage. Finally, we discover cases when changes of physical product features trigger the changes in product's use. We experimentally demonstrate the effectiveness of our approach on the Amazon Product Review Dataset that spans over 18 years.

[1]  J F van Reenen.  Omnia mutantur. , 1975, Diastema.

[2]  Franz von Kutschera,et al.  Causation , 1993, J. Philos. Log..

[3]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[4]  Paramita Mirza,et al.  Extracting Temporal and Causal Relations between Events , 2014, ACL.

[5]  Sourav S. Bhowmick,et al.  Omnia Mutantur, Nihil Interit: Connecting Past with Present by Finding Corresponding Terms across Time , 2015, ACL.

[6]  Vasudeva Varma,et al.  Modeling the evolution of product entities , 2014, SIGIR.

[7]  Gerhard Weikum,et al.  Bridging the Terminology Gap in Web Archive Search , 2009, WebDB.

[8]  Thomas Risse,et al.  NEER: An Unsupervised Method for Named Entity Evolution Recognition , 2012, COLING.

[9]  John D. Lafferty,et al.  Dynamic topic models , 2006, ICML.

[10]  Bengt Jonsson,et al.  A logic for reasoning about time and reliability , 1990, Formal Aspects of Computing.

[11]  Xin Tong,et al.  TextFlow: Towards Better Understanding of Evolving Topics in Text , 2011, IEEE Transactions on Visualization and Computer Graphics.

[12]  Steven Skiena,et al.  Statistically Significant Detection of Linguistic Change , 2014, WWW.

[13]  Ghislaine M. Lawrence.  The social construction of technological systems: new directions in the sociology and history of technology , 1989, Medical History.

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

[15]  Tom Burr,et al.  Causation, Prediction, and Search , 2003, Technometrics.

[16]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[17]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[18]  C. Granger.  Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[19]  P. Gilbert.  Combining var estimation and state space model reduction for simple good predictions , 1995 .

[20]  Bud Mishra,et al.  The Temporal Logic of Causal Structures , 2009, UAI.

[21]  Kjetil Nørvåg,et al.  Exploiting time-based synonyms in searching document archives , 2010, JCDL '10.

[22]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.

[23]  Gerhard Weikum,et al.  Entity timelines: visual analytics and named entity evolution , 2011, CIKM '11.

[24]  Kira Radinsky,et al.  Learning causality for news events prediction , 2012, WWW.

[25]  Samantha Kleinberg,et al.  Causality, Probability, and Time , 2012 .

[26]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[27]  B. Sax.  Karl Marx's theory of history: A defense , 1985 .

[28]  Dan I. Moldovan,et al.  Text Mining for Causal Relations , 2002, FLAIRS.

[29]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[30]  S. Payson,et al.  Product Evolution: What It Is and How It Can Be Measured , 1995 .

[31]  Jianwen Zhang,et al.  Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora , 2010, KDD.

[32]  Michael Gertz,et al.  Retro: Time-Based Exploration of Product Reviews , 2012, ECIR.

[33]  George Hripcsak,et al.  Methodological Review: A review of causal inference for biomedical informatics , 2011 .

[34]  James Allan,et al.  Topic detection and tracking: event-based information organization , 2002 .

[35]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[36]  Yan Liu,et al.  Temporal causal modeling with graphical granger methods , 2007, KDD '07.

[37]  Gerhard Weikum,et al.  Incorporating terminology evolution for query translation in text retrieval with association rules , 2010, CIKM '10.

[38]  Illtyd Trethowan.  Causality , 1938 .