JOINT_FORCES : unite competing sentiment classifiers with random forest

In this paper, we describe how we created a meta-classifier to detect the message-level sentiment of tweets. We participated in SemEval-2014 Task 9B by combining the results of several existing classifiers using a random forest. The results of 5 other teams from the competition as well as from 7 general purpose commercial classifiers were used to train the algorithm. This way, we were able to get a boost of up to 3.24 F1 score points

Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams

We describe a classifier to predict the message-level sentiment of English microblog messages from Twitter. This paper describes the classifier submitted to the SemEval-2014 competition (Task 9B). Our approach was to build up on the system of the last year’s winning approach by NRC Canada 2013 (Mohammad et al., 2013), with some modifications and additions of features, and additional sentiment lexicons. Furthermore, we used a sparse (l1-regularized) SVM, instead of the more commonly used l2-regularization, resulting in a very sparse linear classifier

A Twitter corpus and benchmark resources for german sentiment analysis

In this paper we present SB10k, a new corpus for sentiment analysis with approx.10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art bench-marks for sentiment analysis in German:we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly available

SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

In this paper, we propose a classifier for predicting message-level sentiments of English micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Severyn and Moschitti, 2015a; Severyn and Moschitti, 2015b). We leverage large amounts of data with distant supervision to train an ensemble of 2-layer convolutional neural networks whose predictions are combined using a random forest classifier. Our approach was evaluated on the datasets of the SemEval-2016 competition (Task 4) outperforming all other approaches for the Message Polarity Classification task

Potential and limitations of commercial sentiment detection tools

In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. In addition to the quality analysis (measured by various metrics), we also investigate the effect of increasing text length on the performance. Finally, we show that combining all tools using machine learning techniques increases the overall performance significantly

Potential and Limitations of Commercial Sentiment Detection Tools

Author names in alphabetic order Abstract. In this paper, we analyze the quality of several commercial tools for sentiment detection. All tools are tested on nearly 30,000 short texts from various sources, such as tweets, news, reviews etc. In addition to the quality analysis (measured by various metrics), we also investigate the effect of increasing text length on the performance. Finally, we show that combining all tools using machine learning techniques increases the overall performance significantly

Back to the Future : Time-Travelling-Debugger als Alternative zu klassischen Debuggern

Time-Travelling-Debugger versprechen das Paradies für Software-Entwickler: Frei im Code vorwärts und rückwärts navigieren, nachträglich Log-Statements einfügen, an beliebige Zeitpunkte in der Code-Ausführung springen. Wir nehmen diese vielversprechenden Tools genauer unter die Lupe und beantworten folgende Fragen: Wie funktioniert die Technologie? Wo kann man einen Time-Travelling-Debugger am besten einsetzen? Und welche Einschränkungen gibt es in der Praxis?

Swiss-Chocolate: Sentiment Detection using Sparse SVMs and Part-Of-Speech n-Grams.

We describe a classifier to predict the message-level sentiment of English micro-blog messages from Twitter. This pa-per describes the classifier submitted to the SemEval-2014 competition (Task 9B). Our approach was to build up on the sys-tem of the last year’s winning approach by NRC Canada 2013 (Mohammad et al., 2013), with some modifications and addi-tions of features, and additional sentiment lexicons. Furthermore, we used a sparse (`1-regularized) SVM, instead of the more commonly used `2-regularization, result-ing in a very sparse linear classifier.