Ning Yu, Sandra Kübler, Joshua Herring, Yu-Yin Hsu, Ross Israel, Charese Smiley
{"title":"LASSA: Emotion Detection via Information Fusion.","authors":"Ning Yu, Sandra Kübler, Joshua Herring, Yu-Yin Hsu, Ross Israel, Charese Smiley","doi":"10.4137/BII.S8949","DOIUrl":"https://doi.org/10.4137/BII.S8949","url":null,"abstract":"<p><p>DUE TO THE COMPLEXITY OF EMOTIONS IN SUICIDE NOTES AND THE SUBTLE NATURE OF SENTIMENTS, THIS STUDY PROPOSES A FUSION APPROACH TO TACKLE THE CHALLENGE OF SENTIMENT CLASSIFICATION IN SUICIDE NOTES: leveraging WordNet-based lexicons, manually created rules, character-based n-grams, and other linguistic features. Although our results are not satisfying, some valuable lessons are learned and promising future directions are identified.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"71-6"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8949","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P Sheth
{"title":"Discovering Fine-grained Sentiment in Suicide Notes.","authors":"Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P Sheth","doi":"10.4137/BII.S8963","DOIUrl":"https://doi.org/10.4137/BII.S8963","url":null,"abstract":"<p><p>This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"137-45"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Rule-based and lightly supervised methods to predict emotions in suicide notes.","authors":"Ted Pedersen","doi":"10.4137/BII.S8953","DOIUrl":"https://doi.org/10.4137/BII.S8953","url":null,"abstract":"<p><p>This paper describes the Duluth systems that participated in the Sentiment Analysis track of the i2b2/VA/Cincinnati Children's 2011 Challenge. The top Duluth system was a rule-based approach derived through manual corpus analysis and the use of measures of association to identify significant ngrams. This performed in the median range of systems, attaining an F-measure of 0.45. The second system was automatically derived from the most frequent bigrams unique to one or two emotions. It achieved an F-measure of 0.36. The third system was the union of the first two, and reached an F-measure of 0.44.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"185-93"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8953","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander Pak, Delphine Bernhard, Patrick Paroubek, Cyril Grouin
{"title":"A combined approach to emotion detection in suicide notes.","authors":"Alexander Pak, Delphine Bernhard, Patrick Paroubek, Cyril Grouin","doi":"10.4137/BII.S8969","DOIUrl":"https://doi.org/10.4137/BII.S8969","url":null,"abstract":"<p><p>In this paper, we present the system we have developed for participating in the second task of the i2b2/VA 2011 challenge dedicated to emotion detection in clinical records. On the official evaluation, we ranked 6th out of 26 participants. Our best configuration, based upon a combination of both a machine-learning based approach and manually-defined transducers, obtained a 0.5383 global F-measure, while the distribution of the other 26 participants' results is characterized by mean = 0.4875, stdev = 0.0742, min = 0.2967, max = 0.6139, and median = 0.5027. Combination of machine learning and transducer is achieved by computing the union of results from both approaches, each using a hierarchy of sentiment specific classifiers.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"105-14"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8969","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Combining Lexico-semantic Features for Emotion Classification in Suicide Notes.","authors":"Bart Desmet, Véronique Hoste","doi":"10.4137/BII.S8960","DOIUrl":"10.4137/BII.S8960","url":null,"abstract":"<p><p>This paper describes a system for automatic emotion classification, developed for the 2011 i2b2 Natural Language Processing Challenge, Track 2. The objective of the shared task was to label suicide notes with 15 relevant emotions on the sentence level. Our system uses 15 SVM models (one for each emotion) using the combination of features that was found to perform best on a given emotion. Features included lemmas and trigram bag of words, and information from semantic resources such as WordNet, SentiWordNet and subjectivity clues. The best-performing system labeled 7 of the 15 emotions and achieved an F-score of 53.31% on the test data.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"125-8"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3409478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hui Yang, Alistair Willis, Anne de Roeck, Bashar Nuseibeh
{"title":"A hybrid model for automatic emotion recognition in suicide notes.","authors":"Hui Yang, Alistair Willis, Anne de Roeck, Bashar Nuseibeh","doi":"10.4137/BII.S8948","DOIUrl":"https://doi.org/10.4137/BII.S8948","url":null,"abstract":"<p><p>We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"17-30"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30823724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kim Luyckx, Frederik Vaassen, Claudia Peersman, Walter Daelemans
{"title":"Fine-grained emotion detection in suicide notes: a thresholding approach to multi-label classification.","authors":"Kim Luyckx, Frederik Vaassen, Claudia Peersman, Walter Daelemans","doi":"10.4137/BII.S8966","DOIUrl":"https://doi.org/10.4137/BII.S8966","url":null,"abstract":"<p><p>We present a system to automatically identify emotion-carrying sentences in suicide notes and to detect the specific fine-grained emotion conveyed. With this system, we competed in Track 2 of the 2011 Medical NLP Challenge,14 where the task was to distinguish between fifteen emotion labels, from guilt, sorrow, and hopelessness to hopefulness and happiness.Since a sentence can be annotated with multiple emotions, we designed a thresholding approach that enables assigning multiple labels to a single instance. We rely on the probability estimates returned by an SVM classifier and experimentally set thresholds on these probabilities. Emotion labels are assigned only if their probability exceeds a certain threshold and if the probability of the sentence being emotion-free is low enough. We show the advantages of this thresholding approach by comparing it to a naïve system that assigns only the most probable label to each test sentence, and to a system trained on emotion-carrying sentences only.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"61-9"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical and similarity methods for classifying emotion in suicide notes.","authors":"Kirk Roberts, Sanda M Harabagiu","doi":"10.4137/BII.S8958","DOIUrl":"https://doi.org/10.4137/BII.S8958","url":null,"abstract":"<p><p>In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"195-204"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8958","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maria Liakata, Jee-Hyub Kim, Shyamasree Saha, Janna Hastings, Dietrich Rebholz-Schuhmann
{"title":"Three hybrid classifiers for the detection of emotions in suicide notes.","authors":"Maria Liakata, Jee-Hyub Kim, Shyamasree Saha, Janna Hastings, Dietrich Rebholz-Schuhmann","doi":"10.4137/BII.S8967","DOIUrl":"https://doi.org/10.4137/BII.S8967","url":null,"abstract":"<p><p>We describe our approach for creating a system able to detect emotions in suicide notes. Motivated by the sparse and imbalanced data as well as the complex annotation scheme, we have considered three hybrid approaches for distinguishing between the different categories. Each of the three approaches combines machine learning with manually derived rules, where the latter target very sparse emotion categories. The first approach considers the task as single label multi-class classification, where an SVM and a CRF classifier are trained to recognise fifteen different categories and their results are combined. Our second approach trains individual binary classifiers (SVM and CRF) for each of the fifteen sentence categories and returns the union of the classifiers as the final result. Finally, our third approach is a combination of binary and multi-class classifiers (SVM and CRF) trained on different subsets of the training data. We considered a number of different feature configurations. All three systems were tested on 300 unseen messages. Our second system had the best performance of the three, yielding an F1 score of 45.6% and a Precision of 60.1% whereas our best Recall (43.6%) was obtained using the third system.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"175-84"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Labeling emotions in suicide notes: cost-sensitive learning with heterogeneous features.","authors":"Jonathon Read, Erik Velldal, Lilja Ovrelid","doi":"10.4137/BII.S8930","DOIUrl":"https://doi.org/10.4137/BII.S8930","url":null,"abstract":"<p><p>This paper describes a system developed for Track 2 of the 2011 Medical NLP Challenge on identifying emotions in suicide notes. Our approach involves learning a collection of one-versus-all classifiers, each deciding whether or not a particular label should be assigned to a given sentence. We explore a variety of features types-syntactic, semantic and surface-oriented. Cost-sensitive learning is used for dealing with the issue of class imbalance in the data.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"5 Suppl. 1","pages":"99-103"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S8930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30824239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}