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: Variation in mandible shape and body size of house mice Mus musculus across the New Zealand archipelago: A Trans-Tasman comparison using geometric morphometrics

authors: West, Annie Grace

subjects: geometric morphometrics

abstract: Islands create unique environments that provide niche expansion opportunities for invading species not previously available on continental areas. This thesis investigates variation in house mouse mandible shape and body size across the New Zealand archipelago, and aims to identify environmental variables that might influence change between island populations using geometric morphometric techniques.
Mice were introduced to New Zealand in the early 19th century with European explorers and settlers. The Mus phylogroups currently known in New Zealand include Mus musculus domesticus, M. m. musculus, and M. m. castaneus. Each of these subspecies has a distinct distribution throughout the archipelago, with M. m. domesticus dominating the northern New Zealand ‘mainland’.
Past studies have shown habitat composition and co-existence with rats can significantly influence the activity and behaviour of house mice. Additionally, environmental events such as seed masting are known to provide abundant food resources that inflate house mouse density. Consequently, different habitats may promote variation in dietary components. Here I examined variation between house mice inhabiting five different forest environments across the North and South Islands. Significant variation in mandible shape and body size was found between all five forest populations. Annual rainfall was the most significant covariate with mandible shape. General variation in body size somewhat followed rising latitude, supporting the general ecological trend known as Bergmann’s rule. Habitat type, ambient temperature, and presence of rats were also significant predictors of body size under regression.
Ecological complexity and biodiversity are known to vary with island size. Islands with greater landmass are associated with increased habitat and species diversity which support heightened competition and predation. Small mammals colonising diminutive, remote islands often experience ecological release from these interactions, leading to the largest cases of gigantism currently known. This study investigated variation between six populations of house mice on small, often very remote islands in the New Zealand archipelago. Significant variation in body size and mandible shape was found between all six populations, but again, rainfall was the most significant covariate with mandible shape. The general pattern in body size between island populations followed Bergmann’s rule, and is also consistent with previous studies that observe an inverse relationship between island size and body size, peaking at 50° latitudes. Ambient temperature, rainfall, and genetics were also significant predictors of body size.
Significant variation was also found between all New Zealand mouse populations compared with samples from Sydney. Australia. Sydney was an important shipping port during the settlement of New Zealand. Modern Sydney house mice are probably descendants of the original population from which northern New Zealand domesticus mice are derived. Sydney mandibles clustered with extreme examples of variation in New Zealand mouse mandible shapes on separate axes. Collectively, these studies describe significant variation between distinct house mouse populations of the New Zealand archipelago, and lay the foundations for further research in this area.

text: Variation in mandible shape and body size of house mice Mus musculus across the New Zealand archipelago: A Trans-Tasman comparison using geometric morphometrics Islands create unique environments that provide niche expansion opportunities for invading species not previously available on continental areas. This thesis investigates variation in house mouse mandible shape and body size across the New Zealand archipelago, and aims to identify environmental variables that might influence change between island populations using geometric morphometric techniques.
Mice were introduced to New Zealand in the early 19th century with European explorers and settlers. The Mus phylogroups currently known in New Zealand include Mus musculus domesticus, M. m. musculus, and M. m. castaneus. Each of these subspecies has a distinct distribution throughout the archipelago, with M. m. domesticus dominating the northern New Zealand ‘mainland’.
Past studies have shown habitat composition and co-existence with rats can significantly influence the activity and behaviour of house mice. Additionally, environmental events such as seed masting are known to provide abundant food resources that inflate house mouse density. Consequently, different habitats may promote variation in dietary components. Here I examined variation between house mice inhabiting five different forest environments across the North and South Islands. Significant variation in mandible shape and body size was found between all five forest populations. Annual rainfall was the most significant covariate with mandible shape. General variation in body size somewhat followed rising latitude, supporting the general ecological trend known as Bergmann’s rule. Habitat type, ambient temperature, and presence of rats were also significant predictors of body size under regression.
Ecological complexity and biodiversity are known to vary with island size. Islands with greater landmass are associated with increased habitat and species diversity which support heightened competition and predation. Small mammals colonising diminutive, remote islands often experience ecological release from these interactions, leading to the largest cases of gigantism currently known. This study investigated variation between six populations of house mice on small, often very remote islands in the New Zealand archipelago. Significant variation in body size and mandible shape was found between all six populations, but again, rainfall was the most significant covariate with mandible shape. The general pattern in body size between island populations followed Bergmann’s rule, and is also consistent with previous studies that observe an inverse relationship between island size and body size, peaking at 50° latitudes. Ambient temperature, rainfall, and genetics were also significant predictors of body size.
Significant variation was also found between all New Zealand mouse populations compared with samples from Sydney. Australia. Sydney was an important shipping port during the settlement of New Zealand. Modern Sydney house mice are probably descendants of the original population from which northern New Zealand domesticus mice are derived. Sydney mandibles clustered with extreme examples of variation in New Zealand mouse mandible shapes on separate axes. Collectively, these studies describe significant variation between distinct house mouse populations of the New Zealand archipelago, and lay the foundations for further research in this area.

year: 2017

title: Recognition and indigenizing official statistics: Reflections from Aotearoa New Zealand and Australia

authors: Kukutai, Tahu

subjects: Indigenou

abstract: In First World colonised nations such as Aotearoa New Zealand and Australia, population statistics form the evidentiary base for how Indigenous peoples are known and `managed' through state policy approaches. Yet, population statistics are not a neutral counting. Decisions of what and how to count reflect particular assumptions about Indigenous identity, ways of life and wellbeing. More often than not, the requirements and priorities of government take precedence over the informational needs and priorities of Indigenous communities. Whereas National Statistics Offices (NSOs) once rendered Indigenous peoples invisible in official statistics through non-recognition, the more pressing problem in the 21st century is that of misrecognition. In seeking to move beyond statistical misrecognition, we propose a set of guiding principles for bringing government reporting frameworks and Indigenous concepts of identity and wellbeing into closer proximity. We argue that a principled approach to collecting, disseminating and analysing Indigenous data not only avoids misrecognising Indigenous peoples but enhances the functionality of official statistics for Indigenous peoples and NSOs alike.

text: Recognition and indigenizing official statistics: Reflections from Aotearoa New Zealand and Australia In First World colonised nations such as Aotearoa New Zealand and Australia, population statistics form the evidentiary base for how Indigenous peoples are known and `managed' through state policy approaches. Yet, population statistics are not a neutral counting. Decisions of what and how to count reflect particular assumptions about Indigenous identity, ways of life and wellbeing. More often than not, the requirements and priorities of government take precedence over the informational needs and priorities of Indigenous communities. Whereas National Statistics Offices (NSOs) once rendered Indigenous peoples invisible in official statistics through non-recognition, the more pressing problem in the 21st century is that of misrecognition. In seeking to move beyond statistical misrecognition, we propose a set of guiding principles for bringing government reporting frameworks and Indigenous concepts of identity and wellbeing into closer proximity. We argue that a principled approach to collecting, disseminating and analysing Indigenous data not only avoids misrecognising Indigenous peoples but enhances the functionality of official statistics for Indigenous peoples and NSOs alike.

year: 2015