title: Classifier chains for multi-label classification

authors: Read, Jesse

subjects: computer science

abstract: The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

text: Classifier chains for multi-label classification The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

year: 2009

title: Combining strengths, emotions and polarities for boosting Twitter sentiment analysis

authors: Bravo-Marquez, Felipe

abstract: Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express sentiments about specific topics or entities with different strengths and intensities, where these sentiments are strongly related to their personal feelings and emotions. A number of methods and lexical resources have been proposed to analyze sentiment from natural language texts, addressing different opinion dimensions. In this article, we propose an approach for boosting Twitter sentiment classification using different sentiment dimensions as meta-level features. We combine aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources. Our research shows that the combination of sentiment dimensions provides significant improvement in Twitter sentiment classification tasks such as polarity and subjectivity.

text: Combining strengths, emotions and polarities for boosting Twitter sentiment analysis Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express sentiments about specific topics or entities with different strengths and intensities, where these sentiments are strongly related to their personal feelings and emotions. A number of methods and lexical resources have been proposed to analyze sentiment from natural language texts, addressing different opinion dimensions. In this article, we propose an approach for boosting Twitter sentiment classification using different sentiment dimensions as meta-level features. We combine aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources. Our research shows that the combination of sentiment dimensions provides significant improvement in Twitter sentiment classification tasks such as polarity and subjectivity.

year: 2013-01-01

title: Governance and legitimacy: Indigenous development in Aotearoa

authors: Dodd, Materoa

subjects: Māori governance

abstract: Within the indigenous paradigm lies a paradox over what constitutes legitimate Māori authority: Iwi structures are legitimated and recognised by the state, and traditional forms (for example, Hapū and Māori women), have had their authority diminished. Iwi authorities are colonial constructs that diminish the rights of Hapū and Māori women. The manner in which such constructs have come about generally has not involved tribewide debate over important issues in which Hapū and Māori women have an interest. For their legitimacy, emergent self-governing structures will need to be grounded in principles that ensure legitimate representation of Māori people and not socially constructed hybrids of a colonial past. Structures need to recognise Hapū and Māori women in the formulation, anything less than this will trigger a legitimation crisis.

text: Governance and legitimacy: Indigenous development in Aotearoa Within the indigenous paradigm lies a paradox over what constitutes legitimate Māori authority: Iwi structures are legitimated and recognised by the state, and traditional forms (for example, Hapū and Māori women), have had their authority diminished. Iwi authorities are colonial constructs that diminish the rights of Hapū and Māori women. The manner in which such constructs have come about generally has not involved tribewide debate over important issues in which Hapū and Māori women have an interest. For their legitimacy, emergent self-governing structures will need to be grounded in principles that ensure legitimate representation of Māori people and not socially constructed hybrids of a colonial past. Structures need to recognise Hapū and Māori women in the formulation, anything less than this will trigger a legitimation crisis.

year: 2000-10