We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution. Yet, through experiments on 10 distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority-class baseline across a number of NLI datasets. Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.

/pdf/hypothesis-only-baselines-in-natural-language-inference-2p1z2f2yq6.pdf

Hypothesis Only Baselines in Natural Language Inference

This review pursues a twofold goal, the first is to preserve and enhance the chronicles of recent educational data mining (EDM) advances development; the second is to organize, analyze, and discuss the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw data base, which was transformed into an ad-hoc data base suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of value-instances to depict EDM approaches based on descriptive and predictive models were identified. One key finding is: most of the EDM approaches are ground on a basic set composed by three kinds of educational systems, disciplines, tasks, methods, and algorithms each. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Ayala%20-%20Educational%20Data%20Mining%20-%202014.pdf

Review: Educational data mining: A survey and a data mining-based analysis of recent works

Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.

Current State of Text Sentiment Analysis from Opinion to Emotion Mining

Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Emotion and sentiment analysis from Twitter text

Text often expresses the writer's emotional state or evokes emotions in the reader. The nature of emotional phenomena like reading and writing can be interpreted in different ways and represented with different computational models. Affective computing (AC) researchers often use a categorical model in which text data are associated with emotional labels. We introduce a new way of using normative databases as a way of processing text with a dimensional model and compare it with different categorical approaches. The approach is evaluated using four data sets of texts reflecting different emotional phenomena. An emotional thesaurus and a bag-of-words model are used to generate vectors for each pseudo-document, then for the categorical models three dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Non-negative Matrix Factorization (NMF). For the dimensional model a normative database is used to produce three-dimensional vectors (valence, arousal, dominance) for each pseudo-document. This three-dimensional model can be used to generate psychologically driven visualizations. Both models can be used for affect detection based on distances amongst categories and pseudo-documents. Experiments show that the categorical model using NMF and the dimensional model tend to perform best.

Emotions in text: dimensional and categorical models

In this paper we present an evaluation of new techniques for automatically detecting emotions in text. The study estimates categorical model and dimensional model for the recognition of four affective states: Anger, Fear, Joy, and Sadness that are common emotions in three datasets: SemEval-2007 "Affective Text", ISEAR (International Survey on Emotion Antecedents and Reactions), and children's fairy tales. In the first model, WordNet-Affect is used as a linguistic lexical resource and three dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Non-negative Matrix Factorization (NMF). In the second model, ANEW (Affective Norm for English Words), a normative database with affective terms, is employed. Experiments show that a categorical model using NMF results in better performances for SemEval and fairy tales, whereas a dimensional model performs better with ISEAR.

/pdf/evaluation-of-unsupervised-emotion-models-to-textual-affect-2cc1jwoyj8.pdf

Evaluation of Unsupervised Emotion Models to Textual Affect Recognition

In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one’s audience. Most previous work has made independence assumptions over topological, textual and label information on social networks. In this work, we employ recursive neural networks to break down these independence assumptions to obtain inference about demographic characteristics on Twitter. We show that our model performs better than existing models including the state-of-the-art.

/pdf/demographic-inference-on-twitter-using-recursive-neural-34pal0xzr0.pdf

Demographic Inference on Twitter using Recursive Neural Networks

In this paper we present an evaluation of new techniques for automatically detecting sentiment polarity (Positive or Negative) in the students responses to Unit of Study Evaluations (USE). The study compares categorical model and dimensional model making use of five emotion categories: Anger, Fear, Joy, Sadness, and Surprise. Joy and Surprise are taken as a Positive polarity, whereas Anger, Fear and Sadness belong to Negative polarity in the binary classes, respectively. We evaluate the performances of category-based and dimension-based emotion prediction models on the 2,940 textual responses. In the former model, WordNet-Affect is used as a linguistic lexical resource and two dimensionality reduction techniques are evaluated: Latent Semantic Analysis (LSA) and Non-negative Matrix Factorization (NMF). In the latter model, ANEW (Affective Norm for English Words), a normative database with affective terms, is employed. Despite using generic emotion categories and no syntactical analysis, NMF-based categorical model and dimensional model result in better performances above the baseline.

https://educationaldatamining.org/EDM2010/uploads/proc/edm2010_submission_58.pdf

Sentiment Analysis in Student Experiences of Learning.

Historical newspapers are an important resource in humanities research, providing the source materials about people and places in historical context. The Trove collection in the National Library of Australia holds a large collection of digitised newspapers dating back to 1803. This paper reports on some work to apply named-entity recognition (NER) to data from Trove with the aim of supplying useful data to Humanities researchers using the HuNI Virtual Laboratory. We present an evaluation of the Stanford NER system on this data and discuss the issues raised when applying NER to the 155 million articles in the Trove archive. We then present some analysis of the results including a version published as Linked Data and an exploration of clustering the mentions of certain names in the archive to try to identify individuals.

Sunghwan Mac Kim

Papers

Emotions in text: dimensional and categorical models

Evaluation of Unsupervised Emotion Models to Textual Affect Recognition

Demographic Inference on Twitter using Recursive Neural Networks

Sentiment Analysis in Student Experiences of Learning.

Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers