Semi-Supervised Learning For Identifying Opinions In Web Content

Thesis (Ph.D.) - Indiana University, Information Science, 2011Opinions published on the World Wide Web (Web) offer opportunities for detecting personal attitudes regarding topics, products, and services. The opinion detection literature indicates that both a large body of opinions and a wide variety of opinion features are essential for capturing subtle opinion information. Although a large amount of opinion-labeled data is preferable for opinion detection systems, opinion-labeled data is often limited, especially at sub-document levels, and manual annotation is tedious, expensive and error-prone. This shortage of opinion-labeled data is less challenging in some domains (e.g., movie reviews) than in others (e.g., blog posts). While a simple method for improving accuracy in challenging domains is to borrow opinion-labeled data from a non-target data domain, this approach often fails because of the domain transfer problem: Opinion detection strategies designed for one data domain generally do not perform well in another domain. However, while it is difficult to obtain opinion-labeled data, unlabeled user-generated opinion data are readily available. Semi-supervised learning (SSL) requires only limited labeled data to automatically label unlabeled data and has achieved promising results in various natural language processing (NLP) tasks, including traditional topic classification; but SSL has been applied in only a few opinion detection studies. This study investigates application of four different SSL algorithms in three types of Web content: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. SSL algorithms are also evaluated for their effectiveness in sparse data situations and domain adaptation. Research findings suggest that, when there is limited labeled data, SSL is a promising approach for opinion detection in Web content. Although the contributions of SSL varied across data domains, significant improvement was demonstrated for the most challenging data domain--the blogosphere--when a domain transfer-based SSL strategy was implemented

Wine communication in a global market: a study of metaphor through the genre of Australian wine reviews

This thesis is a report on wine communication focused on metaphoric language identified in the genre of wine reviews. Specifically, the research centred on Australian wine reviews written by Australian wine critics about Australian wines currently exported to the greater China region. In the genre of wine reviews, metaphoric expressions are frequently used to talk about wine (Caballero & Suárez-Toste, 2008). The thesis developed understanding of the influence of metaphoric language and its potential to constrain or motivate people’s sensory and affective responses to wine and highlighted the need to consider congruency of metaphoric language in terms of wine communication and education. The research was theoretically framed by the conceptual metaphor theory (CMT) of Lakoff and Johnson (1980) and took a cognitive linguistic perspective to metaphor analysis (Croft & Cruse, 2004). Wine appreciation was argued to be a social event in contrast to an observational event. From this perspective, wine appreciation is concerned with influencing audience perceptions in contrast to a spontaneous commentary of an event. The thesis presents the findings of two qualitative studies that used a corpus approach to metaphor use and understanding in the genre of wine reviews. The investigation identified metaphoric expressions in Australian wine reviews and went on to explore their understanding and transfer by wine educators in Australia and China. Metaphor identification used the Metaphor Identification Procedure Vrije Universiteit (Steen et al., 2010) and the UCREL Semantic Annotation System (Archer et al., 2004) for semantic and conceptual analysis. Results indicated six underpinning metaphoric themes (i.e., AN OBJECT, A THREE DIMENSIONAL ARTEFACT, AN INSTITUTIONAL ARTEFACT, A TEXTILE, A LIVING ORGANISM, and A PERSON) of which spatial and temporal properties were often integrated. A comparison of wine educator responses to interpretation and transmission tasks showed that anthropomorphic metaphor (i.e., WINE IS A PERSON) tended to be conceptualized similarly by participants more often than other metaphoric themes. In conclusion, the cultural artefact of language used in the genre of wine reviews and the metaphoric potential of linguistic choices on sensory and affective perceptions indicates a need for the consideration of congruency when wine communication crosses cultural and linguistic borders

Sentiment Analysis: An Overview from Linguistics

Sentiment analysis is a growing field at the intersection of linguistics and computer science, which attempts to automatically determine the sentiment, or positive/negative opinion, contained in text. Sentiment can be characterized as positive or negative evaluation expressed through language. Common applications of sentiment analysis include the automatic determination of whether a review posted online (of a movie, a book, or a consumer product) is positive or negative towards the item being reviewed. Sentiment analysis is now a common tool in the repertoire of social media analysis carried out by companies, marketers and political analysts. Research on sentiment analysis extracts information from positive and negative words in text, from the context of those words, and the linguistic structure of the text. This brief survey examines in particular the contributions that linguistic knowledge can make to the problem of automatically determining sentiment

Unsupervised and knowledge-poor approaches to sentiment analysis

Sentiment analysis focuses upon automatic classiffication of a document's sentiment (and more generally extraction of opinion from text). Ways of expressing sentiment have been shown to be dependent on what a document is about (domain-dependency). This complicates supervised methods for sentiment analysis which rely on extensive use of training data or linguistic resources that are usually either domain-specific or generic. Both kinds of resources prevent classiffiers from performing well across a range of domains, as this requires appropriate in-domain (domain-specific) data. This thesis presents a novel unsupervised, knowledge-poor approach to sentiment analysis aimed at creating a domain-independent and multilingual sentiment analysis system. The approach extracts domain-specific resources from documents that are to be processed, and uses them for sentiment analysis. This approach does not require any training corpora, large sets of rules or generic sentiment lexicons, which makes it domain- and languageindependent but at the same time able to utilise domain- and language-specific information. The thesis describes and tests the approach, which is applied to diffeerent data, including customer reviews of various types of products, reviews of films and books, and news items; and to four languages: Chinese, English, Russian and Japanese. The approach is applied not only to binary sentiment classiffication, but also to three-way sentiment classiffication (positive, negative and neutral), subjectivity classifiation of documents and sentences, and to the extraction of opinion holders and opinion targets. Experimental results suggest that the approach is often a viable alternative to supervised systems, especially when applied to large document collections

Trialing project-based learning in a new EAP ESP course: A collaborative reflective practice of three college English teachers

Currently in many Chinese universities, the traditional College English course is facing the risk of being ‘marginalized’, replaced or even removed, and many hours previously allocated to the course are now being taken by EAP or ESP. At X University in northern China, a curriculum reform as such is taking place, as a result of which a new course has been created called ‘xue ke’ English. Despite the fact that ‘xue ke’ means subject literally, the course designer has made it clear that subject content is not the target, nor is the course the same as EAP or ESP. This curriculum initiative, while possibly having been justified with a rationale of some kind (e.g. to meet with changing social and/or academic needs of students and/or institutions), this is posing a great challenge for, as well as considerable pressure on, a number of College English teachers who have taught this single course for almost their entire teaching career. In such a context, three teachers formed a peer support group in Semester One this year, to work collaboratively co-tackling the challenge, and they chose Project-Based Learning (PBL) for the new course. This presentation will report on the implementation of this project, including the overall designing, operational procedure, and the teachers’ reflections. Based on discussion, pre-agreement was reached on the purpose and manner of collaboration as offering peer support for more effective teaching and learning and fulfilling and pleasant professional development. A WeChat group was set up as the chief platform for messaging, idea-sharing, and resource-exchanging. Physical meetings were supplementary, with sound agenda but flexible time, and venues. Mosoteach cloud class (lan mo yun ban ke) was established as a tool for virtual learning, employed both in and after class. Discussions were held at the beginning of the semester which determined only brief outlines for PBL implementation and allowed space for everyone to autonomously explore in their own way. Constant further discussions followed, which generated a great deal of opportunities for peer learning and lesson plan modifications. A reflective journal, in a greater or lesser detailed manner, was also kept by each teacher to record the journey of the collaboration. At the end of the semester, it was commonly recognized that, although challenges existed, the collaboration was overall a success and they were all willing to continue with it and endeavor to refine it to be a more professional and productive approach

EVALUATIVE LANGUAGE IN THE DISCOURSE OF CEBONG VS KAMPRET (‘TADPOLE VS MICROBATS’) ON TWITTER

As an interactive social media, Twitter gives significat role in creating social systems. Evaluative language was intensively used on the social media. The Cebong vs Kampret issue coloured on Twitter and polarized people. By using data Tweet and Reply from Twitter during 2019 this researcher investigates evaluative language. This research results that Twitter community were very emotionally force and defense on the Cebong vs Kampret issue, depicted from many evaluative languages classified into subsystem attitude. Subsystem graduation was also intensively used in accordance to that issue. It means that Twitter community emphasized on semantic scale in evaluating things and person