Multi-label Classification of User Reactions in Online News
The increase in the number of Internet users and the strong interaction brought by Web 2.0 made the Opinion Mining an important task in the area of natural language processing. Although several methods are capable of performing this task, few use multi-label classification, where there is a group of true labels for each example. This type of classification is useful for situations where the opinions are analyzed from the perspective of the reader, this happens because each person can have different interpretations and opinions on the same subject. This paper discuss the efficiency of problem transformation methods combined with different classification algorithms for the task of multi-label classification of reactions in news texts. To do that, extensive tests were carried out on two news corpora written in Brazilian Portuguese annotated with reactions. A new corpus called BFRC-PT is presented. In the tests performed, the highest number of correct predictions was obtained with the Classifier Chains method combined with the Random Forest algorithm. When considering the class distribution, the best results were obtained with the Binary Relevance method combined with the LSTM and Random Forest algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOpinion MiningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts
The Facebook network allows its users to record their reactions to text via a typology of emotions. This network, taken at scale, is therefore a prime data set of annotated sentiment data. This paper uses millions of suc…
Binary ClassificationSentiment AnalysisJointly Learning to Detect Emotions and Predict Facebook Reactions
The growing ubiquity of Social Media data offers an attractive perspective for improving the quality of machine learning-based models in several fields, ranging from Computer Vision to Natural Language Processing. In thi…
Emotion ClassificationThe Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews
The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named entities and the effectiveness of pharm…
General Classificationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3A Weakly Supervised Approach for Classifying Stance in Twitter Replies
Conversations on social media (SM) are increasingly being used to investigate social issues on the web, such as online harassment and rumor spread. For such issues, a common thread of research uses adversarial reactions,…
Echoes of Discord: Forecasting Hater Reactions to Counterspeech
Hate speech (HS) erodes the inclusiveness of online users and propagates negativity and division. Counterspeech has been recognized as a way to mitigate the harmful consequences. While some research has investigated the …