TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Leon Rotim, Martin Tutek, Jan Šnajder · 2017
This paper describes our system for finegrained sentiment scoring of news headlines submitted to SemEval 2017 task 5, subtask 2. Our system uses a feature-light method that consists of a Support Vector Regression (SVR) with various kernels and word embedding vectors as features.Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions, with a cosine score of 0.733.