title: From unlabelled tweets to Twitter-specific opinion words

authors: Bravo-Marquez, Felipe

subjects: Lexicon Generation

abstract: In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification.

text: From unlabelled tweets to Twitter-specific opinion words In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification.

year: 2015

title: A diversity-aware model for majority vote ensemble accuracy

authors: Lim, Nick Jin Sean

subjects: Science & Technology

abstract: Ensemble classifiers are a successful and popular approach for classification, and are frequently found to have better generalization performance than single models in practice. Although it is widely recognized that ‘diversity’ between ensemble members is important in achieving these performance gains, for classification ensembles it is not widely understood which diversity measures are most predictive of ensemble performance, nor how large an ensemble should be for a particular application. In this paper, we explore the predictive power of several common diversity measures and show – with extensive experiments – that contrary to earlier work that finds no clear link between these diversity measures (in isolation) and ensemble accuracy instead by using the ρ diversity measure of Sneath and Sokal as an estimator for the dispersion parameter of a Polya-Eggenberger distribution we can predict, independently of the choice of base classifier family, the accuracy of a majority vote classifier ensemble ridiculously well. We discuss our model and some implications of our findings – such as diversity-aware (non-greedy) pruning of a majority-voting ensemble.

text: A diversity-aware model for majority vote ensemble accuracy Ensemble classifiers are a successful and popular approach for classification, and are frequently found to have better generalization performance than single models in practice. Although it is widely recognized that ‘diversity’ between ensemble members is important in achieving these performance gains, for classification ensembles it is not widely understood which diversity measures are most predictive of ensemble performance, nor how large an ensemble should be for a particular application. In this paper, we explore the predictive power of several common diversity measures and show – with extensive experiments – that contrary to earlier work that finds no clear link between these diversity measures (in isolation) and ensemble accuracy instead by using the ρ diversity measure of Sneath and Sokal as an estimator for the dispersion parameter of a Polya-Eggenberger distribution we can predict, independently of the choice of base classifier family, the accuracy of a majority vote classifier ensemble ridiculously well. We discuss our model and some implications of our findings – such as diversity-aware (non-greedy) pruning of a majority-voting ensemble.

year: 2020

title: A hybrid architecture for labelling bilingual Māori-English tweets

authors: Trye, David

subjects: borrowing

abstract: Most large-scale language detection tools perform poorly at identifying Māori text. Moreover, rule-based and machine learning-based techniques devised specifically for the Māori-English language pair struggle with interlingual homographs. We develop a hybrid architecture that couples Māori-language orthography with machine learning models in order to annotate mixed Māori-English text. This architecture is used to label a new bilingual Twitter corpus at both the token (word) and tweet (sentence) levels. We use the collected tweets to show that the hybrid approach outperforms existing systems with respect to language detection of interlingual homographs and overall accuracy. We also evaluate its performance on out-of-domain data. Two interactive visualisations are provided for exploring the Twitter corpus and comparing errors across the new and existing techniques. The architecture code and visualisations are available online, and the corpus is available on request.

text: A hybrid architecture for labelling bilingual Māori-English tweets Most large-scale language detection tools perform poorly at identifying Māori text. Moreover, rule-based and machine learning-based techniques devised specifically for the Māori-English language pair struggle with interlingual homographs. We develop a hybrid architecture that couples Māori-language orthography with machine learning models in order to annotate mixed Māori-English text. This architecture is used to label a new bilingual Twitter corpus at both the token (word) and tweet (sentence) levels. We use the collected tweets to show that the hybrid approach outperforms existing systems with respect to language detection of interlingual homographs and overall accuracy. We also evaluate its performance on out-of-domain data. Two interactive visualisations are provided for exploring the Twitter corpus and comparing errors across the new and existing techniques. The architecture code and visualisations are available online, and the corpus is available on request.

year: 2022