182 research outputs found
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SemEval-2007 Task 14: Affective Text
The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, the authors describe the data set used in the evaluation and the results obtained by the participating systems
A rule-based approach to implicit emotion detection in text
Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text
General Purpose Textual Sentiment Analysis and Emotion Detection Tools
Textual sentiment analysis and emotion detection consists in retrieving the
sentiment or emotion carried by a text or document. This task can be useful in
many domains: opinion mining, prediction, feedbacks, etc. However, building a
general purpose tool for doing sentiment analysis and emotion detection raises
a number of issues, theoretical issues like the dependence to the domain or to
the language but also pratical issues like the emotion representation for
interoperability. In this paper we present our sentiment/emotion analysis
tools, the way we propose to circumvent the di culties and the applications
they are used for.Comment: Workshop on Emotion and Computing (2013
Overview of the IGGSA 2016 Shared Task on Source and Target Extraction from Political Speeches
We present the second iteration of IGGSA’s Shared Task on Sentiment Analysis for German. It resumes the STEPS task of IGGSA’s 2014 evaluation campaign: Source, Subjective Expression and Target Extraction from Political Speeches. As before, the task is focused on fine-grained sentiment analysis, extracting sources and targets with their associated subjective expressions from a corpus of speeches given in the Swiss parliament. The second iteration exhibits some differences, however; mainly the use of an adjudicated gold standard and the availability of training data. The shared task had 2 participants submitting 7 runs for the full task and 3 runs for each of the subtasks. We evaluate the results and compare them to the baselines provided by the previous iteration. The shared task homepage can be found at http://iggsasharedtask2016.github.io/
Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Rating scales are a widely used method for data annotation; however, they
present several challenges, such as difficulty in maintaining inter- and
intra-annotator consistency. Best-worst scaling (BWS) is an alternative method
of annotation that is claimed to produce high-quality annotations while keeping
the required number of annotations similar to that of rating scales. However,
the veracity of this claim has never been systematically established. Here for
the first time, we set up an experiment that directly compares the rating scale
method with BWS. We show that with the same total number of annotations, BWS
produces significantly more reliable results than the rating scale.Comment: In Proceedings of the Annual Meeting of the Association for
Computational Linguistics (ACL), Vancouver, Canada, 201
Extending the EmotiNet Knowledge Base to Improve the Automatic Detection of Implicitly Expressed Emotions from Text
Sentiment analysis is one of the recent, highly dynamic fields in Natural
Language Processing. Most existing approaches are based on word-level
analysis of texts and are mostly able to detect only explicit expressions of
sentiment. However, in many cases, emotions are not expressed by using
words with an affective meaning (e.g. happy), but by describing real-life
situations, which readers (based on their commonsense knowledge) detect
as being related to a specic emotion. Given the challenges of detecting
emotions from contexts in which no lexical clue is present, in this article we
present a comparative analysis between the performance of well-established
methods for emotion detection (supervised and lexical knowledge-based) and
a method we propose and extend, which is based on commonsense knowledge
stored in the EmotiNet knowledge base. Our extensive evaluations show
that, in the context of this task, the approach based on EmotiNet is the
most appropriate.JRC.G.2-Global security and crisis managemen
IGGSA Shared Tasks on German Sentiment Analysis (GESTALT)
We present the German Sentiment Analysis Shared Task (GESTALT) which consists of two main tasks: Source, Subjective Expression and Target Extraction from Political Speeches (STEPS) and Subjective Phrase and Aspect Extraction from Product Reviews (StAR). Both tasks focused on fine-grained sentiment analysis, extracting aspects and targets with their associated subjective expressions in the German language. STEPS focused on political discussions from a corpus of speeches in the Swiss parliament. StAR fostered the analysis of product reviews as they are available from the website Amazon.de. Each shared task led to one participating submission, providing baselines for future editions of this task and highlighting specific challenges. The shared task homepage can be found at https://sites.google.com/site/iggsasharedtask/
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