Dimitrios Effrosynidis


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DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets
Dimitrios Effrosynidis | Georgios Peikos | Symeon Symeonidis | Avi Arampatzis
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the approach that was developed for SemEval 2018 Task 2 (Multilingual Emoji Prediction) by the DUTH Team. First, we employed a combination of pre-processing techniques to reduce the noise of tweets and produce a number of features. Then, we built several N-grams, to represent the combination of word and emojis. Finally, we trained our system with a tuned LinearSVC classifier. Our approach in the leaderboard ranked 18th amongst 48 teams.


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DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis
Symeon Symeonidis | Dimitrios Effrosynidis | John Kordonis | Avi Arampatzis
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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 combined supervised machine learning methods with classical linguistic resources, including bag-of-words and sentiment lexicon features.

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DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles
Symeon Symeonidis | John Kordonis | Dimitrios Effrosynidis | Avi Arampatzis
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We present the system developed by the team DUTH for the participation in Semeval-2017 task 5 - Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B. Our approach to determine the sentiment of Microblog Messages and News Statements & Headlines is based on linguistic preprocessing, feature engineering, and supervised machine learning techniques. To train our model, we used Neural Network Regression, Linear Regression, Boosted Decision Tree Regression and Decision Forrest Regression classifiers to forecast sentiment scores. At the end, we present an error measure, so as to improve the performance about forecasting methods of the system.