TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news

no code implementations • SEMEVAL 2017 • Leon Rotim, Martin Tutek, Jan {\v{S}}najder

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.

Feature Engineering regression +2

Comparison of Short-Text Sentiment Analysis Methods for Croatian

no code implementations • WS 2017 • Leon Rotim, Jan {\v{S}}najder

We focus on the task of supervised sentiment classification of short and informal texts in Croatian, using two simple yet effective methods: word embeddings and string kernels.

General Classification Sentiment Analysis +4