title: Building a Twitter opinion lexicon from automatically-annotated tweets

authors: Bravo-Marquez, Felipe

subjects: Science & Technology

abstract: Opinion lexicons, which are lists of terms labelled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media. 
In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet. 
To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modelled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labelled by a seed lexicon formed by combining multiple hand-annotated lexicons. 
Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve signifi- cant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments.

text: Building a Twitter opinion lexicon from automatically-annotated tweets Opinion lexicons, which are lists of terms labelled by sentiment, are widely used resources to support automatic sentiment analysis of textual passages. However, existing resources of this type exhibit some limitations when applied to social media messages such as tweets (posts in Twitter), because they are unable to capture the diversity of informal expressions commonly found in this type of media. 
In this article, we present a method that combines information from automatically annotated tweets and existing hand-made opinion lexicons to expand an opinion lexicon in a supervised fashion. The expanded lexicon contains part-of-speech (POS) disambiguated entries with a probability distribution for positive, negative, and neutral polarity classes, similarly to SentiWordNet. 
To obtain this distribution using machine learning, we propose word-level attributes based on (a) the morphological information conveyed by POS tags and (b) associations between words and the sentiment expressed in the tweets that contain them. We consider tweets with both hard and soft sentiment labels. The sentiment associations are modelled in two different ways: using point-wise-mutual-information semantic orientation (PMI-SO), and using stochastic gradient descent semantic orientation (SGD-SO), which learns a linear relationship between words and sentiment. The training dataset is labelled by a seed lexicon formed by combining multiple hand-annotated lexicons. 
Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by using PMI-SO alone. This is significant because PMI-SO is a state-of-the-art measure for establishing world-level sentiment. Additionally, we show that lexicons created with our method achieve signifi- cant improvements over SentiWordNet for classifying tweets into polarity classes, and also outperform SentiStrength in the majority of the experiments.

year: 2016-09-15

title: A process mining technique using pattern recognition

authors: Liesaputra, Veronica

subjects: computer science

abstract: Several works have proposed process mining techniques to discover process models fromevent logs. With the existing works, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces in the logs. In this paper, we propose a novel technique for process mining based on using a pattern recognition technique called Maximal Pattern Mining (MPM). Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Furthermore, by using the MPM, the discovered models are generally much easier to understand.

text: A process mining technique using pattern recognition Several works have proposed process mining techniques to discover process models fromevent logs. With the existing works, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces in the logs. In this paper, we propose a novel technique for process mining based on using a pattern recognition technique called Maximal Pattern Mining (MPM). Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Furthermore, by using the MPM, the discovered models are generally much easier to understand.

year: 2015

title: Pani me te rawakore: Home-making and Maori homelessness without hope or a home

authors: Groot, Shiloh Ann Maree

subjects: Social Science

abstract: The authors draw from interviews with Maori homeless people and relevant local and international literature to show homemaking practices by Maori for those living on the streets. The proposition is that Maori cultural practices shape a person's efforts to retain a positive sense of self and place, and to engage in home-making while dwelling on the streets. We consider colonialism and societal, developments that have impacted whanau (extended family) economically, culturally and socially, contributing to high rates of homelessness among Maori today. We argue that homelessness is endemic to experiences of colonialism, not only at the personal, but also at the iwi (tribe) level where many Maori have experienced over 150 years of being rendered out of place in their hau kainga (tribal homelands). Finally, we present a case study entitled 'Maia' to show common aspects of various Maori people who are homeless and who access services in Auckland.

text: Pani me te rawakore: Home-making and Maori homelessness without hope or a home The authors draw from interviews with Maori homeless people and relevant local and international literature to show homemaking practices by Maori for those living on the streets. The proposition is that Maori cultural practices shape a person's efforts to retain a positive sense of self and place, and to engage in home-making while dwelling on the streets. We consider colonialism and societal, developments that have impacted whanau (extended family) economically, culturally and socially, contributing to high rates of homelessness among Maori today. We argue that homelessness is endemic to experiences of colonialism, not only at the personal, but also at the iwi (tribe) level where many Maori have experienced over 150 years of being rendered out of place in their hau kainga (tribal homelands). Finally, we present a case study entitled 'Maia' to show common aspects of various Maori people who are homeless and who access services in Auckland.

year: 2015