# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 4 Related Work  
### Sentiment Analysis  
Turney [29] extracted phrases containing adverbs and adjectives by focusing on consecutive words within the context. Patterns were applied to this phrase extraction to eliminate the influence of proper names. Excellent and poor were used to calculate the semantic orientation (SO) of the phrase. The final review score was determined by averaging the phrases' semantic orientation. The author noted that text from a particular domain has a distinct writing style that can mislead the final grade. Vanilla sentiment lexicon-based methods employ either the presence or absence of words or the scoring of individual words in the text [30], ultimately averaging the final score. Figure 1: Family tree of Slavic languages. The authors chose a list of verbs, adjectives, and nouns as a starting point and expanded it using Wordnet. Using the synsets from Wordnet, a word's polarity score was calculated. The final class was derived from emotionally charged words. Using negation, intensifiers, and diminishes, the lexicon-based technique [31] was investigated. The combination of positive and negative words inverts the overall evaluation. In contrast, negative phrases and negation result in a positive final evaluation. The use of modal operators establishes a context for the possibility of necessity. Therefore, realis and irrealis events should be treated differently, as irrealis situations do not necessarily reflect the true attitude of opinion holders toward a concept like they do in the realis context. Other linguistic structures mentioned by the authors include presuppositional items (such as it is barely sufficient), connectors, and irony. The earliest attempts were rule-based methods with a high degree of precision [32] that relied heavily on subjective lexicons and patterns. Results were obtained using two classifiers that relied on the presence and absence of subjective clues for subjective and objective classification. The initially classified sentences were then subjected to pattern extraction and iterated in a bootstrapping process to increase the classifier's lexicon size and coverage. The training dataset was used to train a naive Bayes classifier for ranking unlabeled text corpora and passed through the initial pattern extraction procedure to enhance the self-training procedure. Several sentiment lexicons include SentiWordnet [33], General Inquirer [34], Senti-Net [35], and AFFIN [36]. Traditional machine learning models such as naive Bayes and support vector machines (SVMs) have played essential roles in classification. These methods [6; 37] utilize feature engineering. [PERSON] and [PERSON] [37] used [PERSON]'s [29] features and lemma to conclude that the calculation of pointwise mutual information (PMI) could be supplemented with domain information when searching the web for the context window, assuming that domain information did not reduce the hit count. [PERSON] et al. [6] compiled a list of subjectivity clues and expanded it using additional lexicons, including General Inquirer, a dictionary, and a thesaurus. The methodology was based on the prior lexicon-based polarity classifier. This was refined through a two-step process based on intensive feature engineering to distinguish contextual polarity. [PERSON] et al. [38] conducted experiments with cascading sentences and document labels. Together, the document and sentences are trained for the classification task. The sentence classification feature space included unigram, bigram, trigram, and POS tags. The inference is performed using the Viterbi algorithm to calculate the document's final score based on the scores of its sentences. [PERSON] et al. [39] integrated phrase-level predictions into global belief recursive neural networks to provide feedback to words. This is accomplished by incorporating a backward pass that propagates from the parse tree's root to its leaves. The GB-RNN employs both forward and backward parent nodes, whereas the Bi-RNN employs only forward parent nodes.