## 2 State of the Art
### Negation in Sentiment Analysis  
In this article, we are presenting influence modifiers such as negation cues and AnA-words on the overall sentiment value of the tweet. The prevailing approach employed for modifier detection involves two steps: cue detection and scope detection. Modifier cues refer to linguistic elements or signals in the text that provide indications or clues about the sentiment or emotional intensity associated with certain words or phrases. These cues can modify or influence the sentiment expressed in a sentence by providing additional information or context. They can be adverbs, adjectives, intensifiers, negations, or other linguistic devices that impact the polarity or strength of the sentiment expressed. For example, consider the following sentence: _\"The movie was not good\"_. In this case, the modifier cue _\"not\"_ negates the positive sentiment typically associated with the word _\"good\"_, resulting in a negative sentiment overall. The scope of the cues refers to the extent or range within which the modifier cues influence the sentiment expressed in a text. It represents the boundaries or limitations of the impact that the cues have on the sentiment. The scope of the cues can vary depending on the specific linguistic construction or context in which they are used. It can range from affecting a single word or phrase to encompassing an entire sentence or paragraph. For example, in the sentence \"I somewhat like this movie\", the scope of the modifier cue \"somewhat\" is limited to the word \"like\". It indicates a moderate or mild level of liking, modifying the intensity of the sentiment expressed only for that specific word. On the other hand, in the sentence _\"I don't like this movie at all\"_, the scope of the modifier cue _\"at all\"_ extends to the entire sentence. It intensifies the negation and indicates a strong dislike for the movie as a whole. Previous research has extensively explored the concept of negation and its scope within the domain of sentiment analysis. Scholars, such as [PERSON] and [PERSON] in [58], have conducted thorough investigations into content negators. These negators, often expressed through verbs like 'denied', 'hampered', 'lacked', and others, have been the subject of their study, shedding light on their role and impact in sentiment analysis. Building upon this foundation, recent studies have further advanced our understanding of negation in NLP, such as [59] where an LSTM-based deep neural network model was proposed to handle negation terms. Negation terms can be used in implicit or explicit form, and even in morphological form (where denotation of the negation is given with prefix or suffix form) [60]. In the given article _What's Great and What's Not: Learning to Classify the Scope of Negation for Improved Sentiment Analysis_, [PERSON] et al. focused on the explicit form of the negation terms. The approach presented resembles the work by [PERSON] and [PERSON] [61], where machine-learned cue prediction, used by [PERSON] and [PERSON], is replaced by a lexicon specifically focused on explicit negation cues. Moreover, they have employed a single CRF model to effectively predict the scope of negation. An \(F_{1}\) score of 80% was achieved for the negation scope detection on a product review corpus. The same approach was given by [PERSON] et al. [62] except that the latter used a tweet corpus for training and testing with some Twitter-specific modifier cues to the lexicon such as 'dnt' or 'cudnt'. An improvement in sentiment classification was shown. [PERSON] et al. [63] delve into the challenge of determining sentiment polarity in sentences containing negation terms such as \"not.\" To address this, they employ linguistic rules to accurately identify the scope of each negation term. The size of the scope of a negation expression plays a crucial role in determining the extent to which negation words such as 'never', 'no', and 'not' affect a sequence of words in a sentence [64].