title: Meta-level sentiment models for big social data analysis

authors: Bravo-Marquez, Felipe

subjects: Sentiment Classification

abstract: People react to events, topics and entities by expressing their personal opinions and emotions. These reactions can correspond to a wide range of intensities, from very mild to strong. An adequate processing and understanding of these expressions has been the subject of research in several fields, such as business and politics. In this context, Twitter sentiment analysis, which is the task of automatically identifying and extracting subjective information from tweets, has received increasing attention from the Web mining community. Twitter provides an extremely valuable insight into human opinions, as well as new challenging Big Data problems. These problems include the processing of massive volumes of streaming data, as well as the automatic identification of human expressiveness within short text messages. In that area, several methods and lexical resources have been proposed in order to extract sentiment indicators from natural language texts at both syntactic and semantic levels. These approaches address different dimensions of opinions, such as subjectivity, polarity, intensity and emotion. This article is the first study of how these resources, which are focused on different sentiment scopes, complement each other. With this purpose we identify scenarios in which some of these resources are more useful than others. Furthermore, we propose a novel approach for sentiment classification based on meta-level features. This supervised approach boosts existing sentiment classification of subjectivity and polarity detection on Twitter. Our results show that the combination of meta-level features provides significant improvements in performance. However, we observe that there are important differences that rely on the type of lexical resource, the dataset used to build the model, and the learning strategy. Experimental results indicate that manually generated lexicons are focused on emotional words, being very useful for polarity prediction. On the other hand, lexicons generated with automatic methods include neutral words, introducing noise in the detection of subjectivity. Our findings indicate that polarity and subjectivity prediction are different dimensions of the same problem, but they need to be addressed using different subspace features. Lexicon-based approaches are recommendable for polarity, and stylistic part-of-speech based approaches are meaningful for subjectivity. With this research we offer a more global insight of the resource components for the complex task of classifying human emotion and opinion.

text: Meta-level sentiment models for big social data analysis People react to events, topics and entities by expressing their personal opinions and emotions. These reactions can correspond to a wide range of intensities, from very mild to strong. An adequate processing and understanding of these expressions has been the subject of research in several fields, such as business and politics. In this context, Twitter sentiment analysis, which is the task of automatically identifying and extracting subjective information from tweets, has received increasing attention from the Web mining community. Twitter provides an extremely valuable insight into human opinions, as well as new challenging Big Data problems. These problems include the processing of massive volumes of streaming data, as well as the automatic identification of human expressiveness within short text messages. In that area, several methods and lexical resources have been proposed in order to extract sentiment indicators from natural language texts at both syntactic and semantic levels. These approaches address different dimensions of opinions, such as subjectivity, polarity, intensity and emotion. This article is the first study of how these resources, which are focused on different sentiment scopes, complement each other. With this purpose we identify scenarios in which some of these resources are more useful than others. Furthermore, we propose a novel approach for sentiment classification based on meta-level features. This supervised approach boosts existing sentiment classification of subjectivity and polarity detection on Twitter. Our results show that the combination of meta-level features provides significant improvements in performance. However, we observe that there are important differences that rely on the type of lexical resource, the dataset used to build the model, and the learning strategy. Experimental results indicate that manually generated lexicons are focused on emotional words, being very useful for polarity prediction. On the other hand, lexicons generated with automatic methods include neutral words, introducing noise in the detection of subjectivity. Our findings indicate that polarity and subjectivity prediction are different dimensions of the same problem, but they need to be addressed using different subspace features. Lexicon-based approaches are recommendable for polarity, and stylistic part-of-speech based approaches are meaningful for subjectivity. With this research we offer a more global insight of the resource components for the complex task of classifying human emotion and opinion.

year: 2014-10

title: Book review: The politics of language education: Individuals and institutions.

authors: Barnard, Roger

subjects: book review

abstract: This article reviews the book: “The politics of language education: Individuals and institutions”, edited by J.C. Alderson.

text: Book review: The politics of language education: Individuals and institutions. This article reviews the book: “The politics of language education: Individuals and institutions”, edited by J.C. Alderson.

year: 2010

title: Hoera Te Mimiha

authors: Hart, Philip

abstract: Hoera Te Mimiha was a rangatira of Ngati Koi, an inferior hapu to Ngati Tamatera, and had affiliations to other hapu. Although to Pakeha he was a mere labourer, in Maori society in Ohinemuri in the 1870s and 1880s he was a leading figure. Details have been traced of his whakapapa, his wider family, and especially his children, one of whom married Hone Werahiko. 
Mimiha spent much time in the Maori Land Court trying to prove his entitlement to be included as an owner of several blocks of land, not always successfully, for he could not prove some of his cases either through ancestry or occupation. Other rangatira with superior whakapapa were able to show the weaknesses of some of his claims, forcing him into giving evasive answers to cross-examination. His evidence did reveal what land he had occupied and farmed.
Mimiha willingly sold land, including his interest in the Ohinemuri goldfield (the boundaries of which he had pointed out to surveyors), and obtained a good income, for a time. Being interested in mining from the early 1870s onwards, he was one of the few Maori willing to descend a shaft at Thames, an experience that encouraged him to push for opening Ohinemuri to mining, even, it seemed, if that endangered Maori control of their land. Having prospected at Karangahake before it was opened to mining in 1875, he participated in this rush and subsequently in the Te Aroha one of 1880, having interests in claims at all three portions of the latter field. When his daughter’s partner was murdered at Te Aroha, he calmed Maori anger, once again winning the gratitude of Pakeha.
Because of selling much of his land, he struggled financially, although unlike some Maori he did not drink his money away. Cutting off part of the ear of a Pakeha accused of raping his wife resulted in several court cases marked by dubious evidence presented by both sides. After being imprisoned for perjury, efforts by Pakeha to have his conviction reviewed resulted in his being judged to have been the only witness not to have lied to the court, but this sordid affair blackened his character.

text: Hoera Te Mimiha Hoera Te Mimiha was a rangatira of Ngati Koi, an inferior hapu to Ngati Tamatera, and had affiliations to other hapu. Although to Pakeha he was a mere labourer, in Maori society in Ohinemuri in the 1870s and 1880s he was a leading figure. Details have been traced of his whakapapa, his wider family, and especially his children, one of whom married Hone Werahiko. 
Mimiha spent much time in the Maori Land Court trying to prove his entitlement to be included as an owner of several blocks of land, not always successfully, for he could not prove some of his cases either through ancestry or occupation. Other rangatira with superior whakapapa were able to show the weaknesses of some of his claims, forcing him into giving evasive answers to cross-examination. His evidence did reveal what land he had occupied and farmed.
Mimiha willingly sold land, including his interest in the Ohinemuri goldfield (the boundaries of which he had pointed out to surveyors), and obtained a good income, for a time. Being interested in mining from the early 1870s onwards, he was one of the few Maori willing to descend a shaft at Thames, an experience that encouraged him to push for opening Ohinemuri to mining, even, it seemed, if that endangered Maori control of their land. Having prospected at Karangahake before it was opened to mining in 1875, he participated in this rush and subsequently in the Te Aroha one of 1880, having interests in claims at all three portions of the latter field. When his daughter’s partner was murdered at Te Aroha, he calmed Maori anger, once again winning the gratitude of Pakeha.
Because of selling much of his land, he struggled financially, although unlike some Maori he did not drink his money away. Cutting off part of the ear of a Pakeha accused of raping his wife resulted in several court cases marked by dubious evidence presented by both sides. After being imprisoned for perjury, efforts by Pakeha to have his conviction reviewed resulted in his being judged to have been the only witness not to have lied to the court, but this sordid affair blackened his character.

year: 2016