title: A century of the evolution of the urban system in Brazil

authors: Matlaba, Valente José

subjects: Economics Departments

abstract: In this paper, we study the hitherto unexplored evolution of the size distribution of 185 urban areas in Brazil between 1907 and 2008. We find that the power law parameter of the size distribution of the 100 largest urban areas increases from 0.63 in 1907 to 0.89 in 2008, which confirms an agglomeration process in which the size distribution has become more unequal. A panel fixed effects model pooling the same range of urban size distributions provides a power law parameter equal to 0.53, smaller than those from cross-sectional estimation. Clearly, Zipf’s Law is rejected. The lognormal distribution fits the city size distribution quite well until the 1940s, but since then applies to small and medium size cities only. These results are consistent with our understanding of historical-political and socio-economic processes that have shaped the development of Brazilian cities.

text: A century of the evolution of the urban system in Brazil In this paper, we study the hitherto unexplored evolution of the size distribution of 185 urban areas in Brazil between 1907 and 2008. We find that the power law parameter of the size distribution of the 100 largest urban areas increases from 0.63 in 1907 to 0.89 in 2008, which confirms an agglomeration process in which the size distribution has become more unequal. A panel fixed effects model pooling the same range of urban size distributions provides a power law parameter equal to 0.53, smaller than those from cross-sectional estimation. Clearly, Zipf’s Law is rejected. The lognormal distribution fits the city size distribution quite well until the 1940s, but since then applies to small and medium size cities only. These results are consistent with our understanding of historical-political and socio-economic processes that have shaped the development of Brazilian cities.

year: 2011-06

title: Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis

authors: Bravo-Marquez, Felipe

subjects: sentiment analysis

abstract: The most popular sentiment analysis task in Twitter is the automatic classification of tweets into sentiment categories such as positive, negative, and neutral. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. These models are affected by label sparsity, because the manual annotation of tweets is labour-intensive and time-consuming.

This thesis addresses the label sparsity problem for Twitter polarity classification by automatically building two type of resources that can be exploited when labelled data is scarce: opinion lexicons, which are lists of words labelled by sentiment, and synthetically labelled tweets.

In the first part of the thesis, we induce Twitter-specific opinion lexicons by training words level classifiers using representations that exploit different sources of information: (a) the morphological information conveyed by part-of-speech (POS) tags, (b) associations between words and the sentiment expressed in the tweets that contain them, and (c) distributional representations calculated from unlabelled tweets. Experimental results show that the induced lexicons produce significant improvements over existing manually annotated lexicons for tweet-level polarity classification.

In the second part of the thesis, we develop distant supervision methods for generating synthetic training data for Twitter polarity classification by exploiting unlabelled tweets and prior lexical knowledge. Positive and negative training instances are generated by averaging unlabelled tweets annotated according to a given polarity lexicon. We study different mechanisms for selecting the candidate tweets to be averaged. Our experimental results show that the training data generated by the proposed models produce classifiers that perform significantly better than classifiers trained from tweets annotated with emoticons, a popular distant supervision approach for Twitter sentiment analysis.

text: Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis The most popular sentiment analysis task in Twitter is the automatic classification of tweets into sentiment categories such as positive, negative, and neutral. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. These models are affected by label sparsity, because the manual annotation of tweets is labour-intensive and time-consuming.

This thesis addresses the label sparsity problem for Twitter polarity classification by automatically building two type of resources that can be exploited when labelled data is scarce: opinion lexicons, which are lists of words labelled by sentiment, and synthetically labelled tweets.

In the first part of the thesis, we induce Twitter-specific opinion lexicons by training words level classifiers using representations that exploit different sources of information: (a) the morphological information conveyed by part-of-speech (POS) tags, (b) associations between words and the sentiment expressed in the tweets that contain them, and (c) distributional representations calculated from unlabelled tweets. Experimental results show that the induced lexicons produce significant improvements over existing manually annotated lexicons for tweet-level polarity classification.

In the second part of the thesis, we develop distant supervision methods for generating synthetic training data for Twitter polarity classification by exploiting unlabelled tweets and prior lexical knowledge. Positive and negative training instances are generated by averaging unlabelled tweets annotated according to a given polarity lexicon. We study different mechanisms for selecting the candidate tweets to be averaged. Our experimental results show that the training data generated by the proposed models produce classifiers that perform significantly better than classifiers trained from tweets annotated with emoticons, a popular distant supervision approach for Twitter sentiment analysis.

year: 2017

title: Indigenous bodies: Ordinary lives

authors: Hokowhitu, Brendan

abstract: Ambivalence is the overwhelming feeling that haunts my relationship with physicality. Not only my body, but the bodies of an imagined multitude of Indigenous peoples dissected and made whole again via the violent synthesis of the colonial project. Like my own ambivalence (and by “ambivalence” I refer to simultaneous abhorrence and desire), the relationship between Indigenous peoples and physicality faces the anxiety of representation felt within Indigenous studies in general.

text: Indigenous bodies: Ordinary lives Ambivalence is the overwhelming feeling that haunts my relationship with physicality. Not only my body, but the bodies of an imagined multitude of Indigenous peoples dissected and made whole again via the violent synthesis of the colonial project. Like my own ambivalence (and by “ambivalence” I refer to simultaneous abhorrence and desire), the relationship between Indigenous peoples and physicality faces the anxiety of representation felt within Indigenous studies in general.

year: 2016