## 1 Introduction
* Noisy content: social media content often has unusual spelling (e.g., 2 moro), irregular capitalization (e.g., all capital or all lowercase letters), emoticons (e.g., :-P) and idiosyncratic abbreviations (e.g., ROFL for \"Rolling On Floor Laughing\", ZOMG for \"Zombies Oh My God\"). Spelling and capitalization normalization methods have been developed [7], coupled with studiesof location-based linguistic variations in shortening styles in microtexts [8]. Emoticons are used as strong sentiment indicators in opinion mining algorithms. The Modern Greek language poses additional complications to sentiment analysis since the majority of preprocessing tools for Greek, such as the Part-of-Speech (POS) tagger, shallow syntactic parsers and polarity lexica, are not freely available. In the current work, we deal with modeling a sentiment analysis framework for Modern Greek, based on a simple, yet functional and effective idea. We propose an alternative approach that capitalizes on the power of existing induction techniques while enriching the language of representation, namely exploring new feature spaces. We bypass the need for extensive preprocessing tools and utilize a freely available translation API that is provided by Google(r), in order to augment the feature set of the training data. Nevertheless, since automatic translation of large sentences is often reported as portraying low accuracy, mainly due to the large degree of ambiguity, especially in the case of Modern Greek, we followed a simple approach, translating each Greek token individually, a process that rarely makes errors. The resulting feature set portrayed, as expected, an approximate doubling of the original size, which also poses certain difficulties to the majority of classification algorithms. Hence, we experimented with an ensemble classification algorithm known as Gradient Boosting Machines (GBM), which is theoretically proven to be able to cope with a large number of features [9]. According to the referenced work, \"a possible explanation why boosting performs well in the presence of high-dimensional data is that it does variable selection (assuming the base learner does variable selection) and it assigns variable amount of degrees of freedom to the selected predictor variables or terms\". Moreover, apart from the high-dimensional nature of our task at hand, a class imbalance issue appeared since the distribution of sentiment in Web 2.0 resources often portrays signs of imbalance. Imbalanced datasets correspond to domains where there are many more instances of some classes than others. For example, there are frequent occasions in political or religion discussions about a given topic over social media that generally present a skewed polarity distribution. In order to confront that phenomenon, a hybrid technique that performs a modification over a former technique [10] was applied. More specifically, we applied under-sampling to the instances that belong to the majority class and over-sampling to the minority class, respectively. We experimented with numerous well-known algorithms using the initial Greek-only feature set (obtained upon preprocessing with basic filters, such as tokenization and stemming), as well as the enhanced translated one and also some basic feature reduction techniques, such as principal component analysis [11] and feature selection. Through extensive experimental evaluations, we found that GBM are superior to any other implementation. Additionally, when coping with the class imbalance issue, experimental results justify the use of the bootstrapping method since traditional algorithms favor the majority class in their predictions. GBM was again the best choice in the latter case, i.e., the imbalanced dataset. This paper is organized as follows: Section 2 deals with related work on this domain, while Section 3 provides brief insight on GBM. Section 4 discusses the main rationale for using the translation of Greek terms as a feature generation technique.