COMMIT at SemEval-2017 Task 5: Ontology-based Method for Sentiment Analysis of Financial Headlines
This paper describes our submission to Task 5 of SemEval 2017, Fine-Grained Sentiment Analysis on Financial Microblogs and News, where we limit ourselves to performing sentiment analysis on news headlines only (track 2). The approach presented in this paper uses a Support Vector Machine to do the required regression, and besides unigrams and a sentiment tool, we use various ontology-based features. To this end we created a domain ontology that models various concepts from the financial domain. This allows us to model the sentiment of actions depending on which entity they are affecting (e.g., {}decreasing debt{'} is positive, but {}decreasing profit{'} is negative). The presented approach yielded a cosine distance of 0.6810 on the official test data, resulting in the 12th position.
Code (0)
등록된 구현이 없습니다.
Tasks
PositionSentiment AnalysisSimilar Papers 제목 키워드 기반
COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory
A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models
This work focuses on sentence-level aspect-based sentiment analysis for restaurant reviews. A two-stage sentiment analysis algorithm is proposed. In this method, first a lexicalized domain ontology is used to predict …
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)SentenceSentiment AnalysisSemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEv…
ClassificationSentiment AnalysisSentiment Classificationzero-shot-classification+1DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis
This report describes our participation to SemEval-2017 Task 4: Sentiment Analysis in Twitter, specifically in subtasks A, B, and C. The approach for text sentiment classification is based on a Majority Vote scheme and c…
BIG-bench Machine LearningGeneral ClassificationInformation RetrievalSentiment Analysis+2SemEval-2015 Task 10: Sentiment Analysis in Twitter
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of…
Sentiment Analysis