# Towards Reliable Baselines for Document-Level Sentiment Analysis in the Czech and Slovak Languages
### Approaches  
In general, sentiment analysis can be carried out on the level of documents, sentences, or entities mentioned in the text and their aspects. Document-level is properly applicable only to documents which evaluate a single entity, such as product or movie reviews. Because we deal precisely with such documents in this article, from now on we will concentrate primarily on document-level sentiment analysis. [PERSON] and [PERSON] [10] broadly categorized document-level sentiment analysis approaches into four types: dictionary-based, statistical, semantic, and machine learning-based. In principle, each of these can be conceived either as a classification task, labelling documents as \"positive\", \"negative\", \"neutral\", etc., or as a regression task, trying to predict some sort of numeric or ordinal sentiment score for each document, e.g., on a scale from one to five stars. Of these, the most straightforward is the dictionary approach, which exploits pre-built sentiment lexicons and dictionaries, such as General Inquireer [16], WordNet-Affect [17], SentiWordNet [18], or Emotion lexicon [19]. In the dictionary approach, the polarity of a text is usually computed as a weighted average of the lexicon-provided polarities of its sentiment words, accounting also for their modifiers (negation, intensification, etc.). The weights used in this calculation may be static or computed dynamically, reflecting, e.g., the distance of a given sentiment word from the closest topic word [10]. It is also possible to evaluate the polarity of a text based on the polarity of its longer n-grams, as in [20]. In general, however, relying on universal polarity values from lexicons was shown to be unreliable, since sentiment words can change their polarity in different contexts, and sometimes even within the same context. This motivated the development of more advanced forms of sentiment analysis. The statistical approach aims to overcome such problems by constructing corpus-dependent sentiment lexicons. It is based on the observation that similar opinion words frequently appear together in a corpus. Conversely, if two words frequently appear together within the same context, they are likely to share the same polarity. Therefore, the polarity of an unknown word can be determined by calculating the relative frequency of its occurrence with another word, which invariantly preserves its polarity (a prototypical example of such a \"reference\" word is \"good\"). This fact motivated [PERSON] [2; 3] to exploit the pointwise mutual information (PMI) criterion originally proposed by [PERSON] and [PERSON] [21], and derive from it the sentiment polarity of a word as the difference between its PMI values relative to two opposing lists of words: positive words, such as \"good, excellent\", and negative words, such as \"bad, poor\". The semantic approach is akin to the statistical one in that it directly calculates sentiment polarity values for words (thus bypassing the need for external sentiment lexicons) except that it computes the similarity between words differently. Its defining principle is that semantically close words should receive similar sentiment polarity values. As with statistical methods, two sets of seed words with positive and negative sentiments are used as a starting point for bootstrapping the construction of a dictionary. This initial set is then iteratively expanded with their synonyms and antonyms, drawn from a suitable semantic dictionary such as WordNet [22; 23]. The sentiment polarity for an unknown word can be determined by the relative count of its positive and negative synonyms, or it may simply be discarded. Alternatively, [PERSON] et al. [24] used the relative shortest path distance of the \"synonym\" relation. In any case, it is important to account for the fact that the synonym's relevance decreases with its distance from the original word, so the same principle should be applied to assigning the polarity value to the original word.