title: Transferring sentiment knowledge between words and tweets

authors: Bravo-Marquez, Felipe

subjects: computer science

abstract: Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.

text: Transferring sentiment knowledge between words and tweets Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we study how to transfer sentiment labels from the word domain to the tweet domain and vice versa by making their corresponding instances compatible. We model instances of these two domains as the aggregation of instances from the other (i.e., tweets are treated as collections of the words they contain and words are treated as collections of the tweets in which they occur) and perform aggregation by averaging the corresponding constituents. We study two different setups for averaging tweet and word vectors: 1) representing tweets by standard NLP features such as unigrams and part-of-speech tags and words by averaging the vectors of the tweets in which they occur, and 2) representing words using skip-gram embeddings and tweets as the average embedding vector of their words. A consequence of our approach is that instances of both domains reside in the same feature space. Thus, a sentiment classifier trained on labelled data from one domain can be used to classify instances from the other one. We evaluate this approach in two transfer learning tasks: 1) sentiment classification of tweets by applying a word-level sentiment classifier, and 2) induction of a polarity lexicon by applying a tweet-level polarity classifier. Our results show that the proposed model can successfully classify words and tweets after transfer.

year: 2018

title: Reflections concerning a response to post-modernism

authors: Kelly, Martin

abstract: Post-modernism has had a major influence on academic thought. It has served well in exposing the hidden assumptions which many people have unknowingly allowed to influence their behaviour, but it has provided little support to those who are seeking to discover how they might attempt to ‘progress’. Post-modernism may encourage pessimism and lack of hope; these attributes are debilitating to academics and students. In a post-modern environment any quest for quality learning experiences may come to appear pointless. Responses to the post-modern condition need to be identified. In the area of educational research, we argue that ‘narrative’ provides a suitable response

text: Reflections concerning a response to post-modernism Post-modernism has had a major influence on academic thought. It has served well in exposing the hidden assumptions which many people have unknowingly allowed to influence their behaviour, but it has provided little support to those who are seeking to discover how they might attempt to ‘progress’. Post-modernism may encourage pessimism and lack of hope; these attributes are debilitating to academics and students. In a post-modern environment any quest for quality learning experiences may come to appear pointless. Responses to the post-modern condition need to be identified. In the area of educational research, we argue that ‘narrative’ provides a suitable response

year: 1998

title: Ōpōtiki Mai Tawhiti Kapa Haka

authors: Maxwell, Te Kahautu

abstract: Ōpōtiki Mai Tawhiti competed in Te Matatini World Championship Kapa Haka Competition and placed second overall out of 45 kapa haka. Placed 1st Male Leader, 1st Waiata a Ringa Composition, 1st, Poi Composition, 2nd Haka Composition, 1st overall composition, 1st Waiata Tira, 1st Waiata a Ringa, 3rd Whakakeke, 2nd Poi.

text: Ōpōtiki Mai Tawhiti Kapa Haka Ōpōtiki Mai Tawhiti competed in Te Matatini World Championship Kapa Haka Competition and placed second overall out of 45 kapa haka. Placed 1st Male Leader, 1st Waiata a Ringa Composition, 1st, Poi Composition, 2nd Haka Composition, 1st overall composition, 1st Waiata Tira, 1st Waiata a Ringa, 3rd Whakakeke, 2nd Poi.

year: 2015