A Review on Opinion Mining: Approaches, Practices and Application

Opinion Mining also known as Sentiment Analysis (SA) has recently become the focus of many researchers, because analysis of online text is useful and demanded in many different applications. Analysis of social sentiments is a trending topic in this era because users share their emotions in more suitable format with the help of micro blogging services like twitter. Twitter provides information about individual's real-time feelings through the data resources provided by persons. The essential task is to extract user's tweets and implement an analysis and survey. However, this extracted information can very helpful to make prediction about the user's opinion towards specific policies. The motive of this paper is to perform a survey on sentiment analysis algorithms that shows the utilizing of different ML and Lexicon investigation methodologies and their accuracy. Our paper also focuses on the three kinds of machine learning algorithms for Sentiment Analysis- Supervised, Unsupervised Algorithms

A Hierarchical Emotion Classification Technique for Thai Reviews

Emotion classification is an interesting problem in affective computing that can be applied in various tasks, such as speech synthesis, image processing and text processing. With the increasing amount of textual data on the Internet, especially reviews of customers that express opinions and emotions about products. These reviews are important feedback for companies. Emotion classification aims to identify an emotion label for each review. This research investigated three approaches for emotion classification of opinions in the Thai language, written in unstructured format, free form or informal style. Different sets of features were studied in detail and analyzed. The experimental results showed that a hierarchical approach, where the subjectivity of the review is determined first, then the polarity of opinion is identified and finally the emotional label is calculated, yielded the highest performance, with precision, recall and F-measure at 0.691, 0.743 and 0.709, respectively

Robust input representations for low-resource information extraction

Recent advances in the field of natural language processing were achieved with deep learning models. This led to a wide range of new research questions concerning the stability of such large-scale systems and their applicability beyond well-studied tasks and datasets, such as information extraction in non-standard domains and languages, in particular, in low-resource environments. In this work, we address these challenges and make important contributions across fields such as representation learning and transfer learning by proposing novel model architectures and training strategies to overcome existing limitations, including a lack of training resources, domain mismatches and language barriers. In particular, we propose solutions to close the domain gap between representation models by, e.g., domain-adaptive pre-training or our novel meta-embedding architecture for creating a joint representations of multiple embedding methods. Our broad set of experiments demonstrates state-of-the-art performance of our methods for various sequence tagging and classification tasks and highlight their robustness in challenging low-resource settings across languages and domains.Die jüngsten Fortschritte auf dem Gebiet der Verarbeitung natürlicher Sprache wurden mit Deep-Learning-Modellen erzielt. Dies führte zu einer Vielzahl neuer Forschungsfragen bezüglich der Stabilität solcher großen Systeme und ihrer Anwendbarkeit über gut untersuchte Aufgaben und Datensätze hinaus, wie z. B. die Informationsextraktion für Nicht-Standardsprachen, aber auch Textdomänen und Aufgaben, für die selbst im Englischen nur wenige Trainingsdaten zur Verfügung stehen. In dieser Arbeit gehen wir auf diese Herausforderungen ein und leisten wichtige Beiträge in Bereichen wie Repräsentationslernen und Transferlernen, indem wir neuartige Modellarchitekturen und Trainingsstrategien vorschlagen, um bestehende Beschränkungen zu überwinden, darunter fehlende Trainingsressourcen, ungesehene Domänen und Sprachbarrieren. Insbesondere schlagen wir Lösungen vor, um die Domänenlücke zwischen Repräsentationsmodellen zu schließen, z.B. durch domänenadaptives Vortrainieren oder unsere neuartige Meta-Embedding-Architektur zur Erstellung einer gemeinsamen Repräsentation mehrerer Embeddingmethoden. Unsere umfassende Evaluierung demonstriert die Leistungsfähigkeit unserer Methoden für verschiedene Klassifizierungsaufgaben auf Word und Satzebene und unterstreicht ihre Robustheit in anspruchsvollen, ressourcenarmen Umgebungen in verschiedenen Sprachen und Domänen

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop

Editor of the proceedings of EVALITA 2016

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal