# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 4 Applications
### Review Domain  
Table 4 summarizes the performance of negation scope detection systems in the review domain. The results show that the BERT-based model [74] outperforms the other proposed systems when evaluated on the SFU review corpus [40]. However, BiLSTM networks are still proving their efficiency in such tasks. By contrast the negation systems in other languages, such as Spanish, still do not perform well in detecting negation scope. [PERSON] et al. tested the detection of cross-domain negation on two forms of the Spanish language [17], but doing so reduced the system's performance. Therefore, it is inapplicable to handle negation across domains or language variations. Negation is of great significance since it directly changes the polarity of a text, especially one expressing an opinion on a product, trend, or service [107; 108]. Negation cues affect the contextual polarity of words, yet it does not mean that every sentiment word is inverted. It is considered one of the dominant linguistic methods to change a text's polarity in the SA task [31]. For example, [PERSON] et al. [31] investigated the problem of determining the polarity of sentiment in movie reviews when negation words occur. Their approach defined a list of negation cues and studied their effect at several window sizes. As a result, a significant improvement in the classification of the documents was observed. Some research works have addressed the negation problem in SA or opinion retrieval tasks, and most focused on rule-based methods. For example, [PERSON] and [PERSON] introduced the idea of valence shifters, where a token may strengthen, weaken, or invert the polarity of sentiment as in'so', 'rarely', and 'not' [109]. Moreover, [PERSON] et al. introduced the identification of the scope of a negation term to determine the affected sentence's clause [110]. However, these proposed rules did not report their impact on detecting the negation cues or scopes. [PERSON] et al. designed a pre-processing algorithm to identify the words with negations and to replace them with negator tags plus their lemmatized forms [2]. By including this negation detection algorithm, the results classification system's performance improved by 1% on average in terms of F1 score. The improvement in performance is limited, as it handles only the explicit form of negation. Other research works have used a lexicon of common negation cues with supervised learning techniques to reveal the negation scope. [PERSON] et al. [44] described a system that can identify the scope of negation in free English texts. A CRF model was built and then evaluated on an SA task. In contrast to this work [44], which addressed the scope of a negation term within various window sizes, [PERSON] et al. [111] used a binary SVM classifier to identify the true negation cues in the sentence's syntactic structure to detect the scope. Although these systems dramatically improved the F1 score by 20% and 5% on average, again they handle only explicit negation. Negation in English has been detected by both simple sets of rules and sophisticated approaches; however, it is a language-dependent phenomenon, and the negation models for English cannot be used on text in the Arabic language, for example, as the syntactic structure of negation in Arabic differs from that in English. Few Arabic studies have addressed negation's effect on the SA task, but simple rule-based approaches are clearly insufficient to handle every possible instance of negation in the various Arabic language categories and dialects [112]. The frequency of negation terms in the Arabic Sentiment Analysis (ASA) task has been considered a classification feature, but its effect on the classification process has gone unreported [113; 114]. [PERSON] and [PERSON] assumed that negating terms always invert the polarity of a word preceded by an inverter [115].