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One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transforming positive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.

Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long ShortTerm Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning, and deep learning. It addresses and tries to identify the gaps in the current studies, laying the foundation for future studies in this field. [ABSTRACT FROM AUTHOR]

Abstract Revisit intention is a key indicator of business performance, studied in many fields including hospitality. This work employs big data analytics to investigate revisit intention patterns from tourists’ electronic word of mouth (eWOM) using text classification, negation detection, and topic modelling. The method is applied on publicly available hotel reviews that are labelled automatically based on consumers’ intention to revisit a hotel or not. Topics discussed in revisit-annotated reviews are automatically extracted and used as features during the training of two Extreme Gradient Boosting models (XGBoost), one for each of two hotel categories (2/3 and 4/5 stars). The emerging patterns from the trained XGBoost models are identified using an explainable machine learning technique, namely SHAP (SHapley Additive exPlanations). Results show how topics discussed by tourists in reviews relate with revisit/non revisit intention. The proposed method can help hoteliers make more informed decisions on how to improve their services and thus increase customer revisit occurrences.

Abstract When developing models for clinical information retrieval and decision support systems, the discrete outcomes required for training are often missing. These labels need to be extracted from free text in electronic health records. For this extraction process one of the most important contextual properties in clinical text is negation, which indicates the absence of findings. We aimed to improve large scale extraction of labels by comparing three methods for negation detection in Dutch clinical notes. We used the Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based models. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall. In addition, we systematically categorized the classification errors for each model, which can be used to further improve model performance in particular applications. Combining the three models naively was not beneficial in terms of performance. We conclude that the biLSTM and RoBERTa-based models in particular are highly accurate accurate in detecting clinical negations, but that ultimately all three approaches can be viable depending on the use case at hand.

Revisit intention is a key indicator of business performance, studied in many fields including hospitality. This work employs big data analytics to investigate revisit intention patterns from tourists' electronic word of mouth (eWOM) using text classification, negation detection, and topic modelling. The method is applied on publicly available hotel reviews that are labelled automatically based on consumers' intention to revisit a hotel or not. Topics discussed in revisit-annotated reviews are automatically extracted and used as features during the training of two Extreme Gradient Boosting models (XGBoost), one for each of two hotel categories (2/3 and 4/5 stars). The emerging patterns from the trained XGBoost models are identified using an explainable machine learning technique, namely SHAP (SHapley Additive exPlanations). Results show how topics discussed by tourists in reviews relate with revisit/non revisit intention. The proposed method can help hoteliers make more informed decisions on how to improve their services and thus increase customer revisit occurrences. [ABSTRACT FROM AUTHOR]

When developing models for clinical information retrieval and decision support systems, the discrete outcomes required for training are often missing. These labels need to be extracted from free text in electronic health records. For this extraction process one of the most important contextual properties in clinical text is negation, which indicates the absence of findings. We aimed to improve large scale extraction of labels by comparing three methods for negation detection in Dutch clinical notes. We used the Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based models. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall. In addition, we systematically categorized the classification errors for each model, which can be used to further improve model performance in particular applications. Combining the three models naively was not beneficial in terms of performance. We conclude that the biLSTM and RoBERTa-based models in particular are highly accurate accurate in detecting clinical negations, but that ultimately all three approaches can be viable depending on the use case at hand. [ABSTRACT FROM AUTHOR]

Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena, and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in an end-to-end manner. We describe our approach, a cascading and hierarchical neural architecture with selective sharing of Long Short-term Memory layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks, and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups. [ABSTRACT FROM AUTHOR]

Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, and different date formats cause ambiguity that has to be solved to reconstruct the patient's natural history. In this paper, we concentrate on extracting from clinical narratives the cancer diagnosis and relating it to the diagnosis date. To address this challenge, a hybrid approach that combines deep learning-based and rule-based methods is proposed. The approach integrates three steps: (i) lung cancer named entity recognition, (ii) negation and speculation detection, and (iii) relating the cancer diagnosis to a valid date. In particular, we apply the proposed approach to extract the lung cancer diagnosis and its diagnosis date from clinical narratives written in Spanish. Results obtained show an F-score of 90% in the named entity recognition task, and a 89% F-score in the task of relating the cancer diagnosis to the diagnosis date. Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes. [ABSTRACT FROM AUTHOR]

Developing predictive modeling in medicine requires additional features from unstructured clinical texts. In Russia, there are no instruments for natural language processing to cope with problems of medical records. This paper is devoted to a module of negation detection. The corpus-free machine learning method is based on gradient boosting classifier is used to detect whether a disease is denied, not mentioned or presented in the text. The detector classifies negations for five diseases and shows average F-score from 0.81 to 0.93. The benefits of negation detection have been demonstrated by predicting the presence of surgery for patients with the acute coronary syndrome. [ABSTRACT FROM AUTHOR]

This work focuses on the creation of a system to detect negated medical entities in electronic health records (EHRs) written in Spanish. The importance of this task rests on the influence that the negation can have in the automatic understanding of information given that it inverts the truth value of a clause. We explore a novel continuous characterization as an alternative to previous negation extraction approaches based on discrete characterizations. The aim is to increase the ability of the characterization to generalize over discrete features. We also included other features that could be useful for the negation detection task. In addition, the negation detection is approached as a named entity recognition task where we want to find only the negated entities. EHRs are represented by the corresponding embeddings. In addition, this approach is compared with a traditional discrete characterization based on words. These representations are employed by a supervised classifier such as conditional random fields to infer the predictive model. The approach is assessed on health records from different hospitals, namely IxaMed-GS and IULA. The best performance is achieved by virtue of the embedding-based characterization, leading to an f-measure of 75.3 and 81.6 for the IxaMed-GS and IULA corpus, respectively. With this work, we prove that the use of embedding-based representations can also be useful for the detection of negated medical entities. [ABSTRACT FROM AUTHOR]

The importance of determining sentiment for short text increases with the rise in the number of comments on social networks. The presence of negation in these texts affects their sentiment, because it has a greater range of action in proportion to the length of the text. In this paper, we examine how the treatment of negation impacts the sentiment of tweets in the Serbian language. The grammatical rules that influence the change of polarity are processed. We performed an analysis of the effect of the negation treatment on the overall process of sentiment analysis. A statistically significant relative improvement was obtained (up to 31.16% or up to 2.65%) when the negation was processed using our rules with the lexicon-based approach or machine learning methods. By applying machine learning methods, an accuracy of 68.84% was achieved on a set of positive, negative and neutral tweets, and an accuracy of as much as 91.13% when applied to the set of positive and negative tweets. [ABSTRACT FROM AUTHOR]

Extracting biomedical relations such as drug-drug interaction (DDI) from text is an important task in biomedical NLP. Due to the large number of complex sentences in biomedical literature, researchers have employed some sentence simplification techniques to improve the performance of the relation extraction methods. However, due to difficulty of the task, there is no noteworthy improvement in the research literature. This paper aims to explore clause dependency related features alongside to linguistic-based negation scope and cues to overcome complexity of the sentences. The results show by employing the proposed features combined with a bag of words kernel, the performance of the used kernel methods improves. Moreover, experiments show the enhanced local context kernel outperforms other methods. The proposed method can be used as an alternative approach for sentence simplification techniques in biomedical area which is an error-prone task.

The aim of this work is to develop a framework to cope with the negative context in French clinical reports and assess the effect of negation identi cation on the performance of medical information retrieval. The proposed work significantly improves the performance of information retrieval done on French clinical reports where the precision improves by 10%.

Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN , a novel C ontrastive L earning-based G ated S tructured A ttention N etwork for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple's context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the C hinese M edical K nowledge N egation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection. [ABSTRACT FROM AUTHOR]

Background and objectives Researchers have developed effective methods to index free-text clinical notes into structured database, in which negation detection is a critical but challenging step. In Chinese clinical records, negation detection is particularly challenging because it may depend on upstream Chinese information processing components such as word segmentation [1]. Traditionally, negation detection was carried out mostly using rule-based methods, whose comprehensiveness and portability were usually limited. Our objectives in this paper are to: 1) Construct a large Chinese clinical notes corpus with negation annotated; 2) develop a negation detection tool for Chinese clinical notes; 3) evaluate the performance of character and word embedding features in Chinese clinical natural language processing. Methods In this paper, we construct a Chinese clinical corpus consisting of admission and discharge summaries, and propose sequence labeling based systems for negation and scope detection. Our systems rely on features from bag of characters, bag of words, character embedding and word embedding. For scopes, we introduce an additional feature to handle nested scopes with multiple negations. Results The two annotators reached an agreement of 0.79 measured by Kappa in manual annotation. In cue detection, our systems are able to achieve a performance as high as 99.0% measured by F score, which significantly outperform its rule-based counterpart (79% F). The best system uses word embedding as features, which yields precision of 99.0% and recall of 99.1%. In scope detection, our system is able to achieve a performance of 94.6% measured by F score. Conclusions Our study provides a state-of-the-art negation-detecting tool for Chinese clinical free-text notes; Experimental results demonstrate that word embedding is effective in identifying negations, and that nested scopes can be identified effectively by our method. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) and Relation Extraction (RE) are two of the most studied tasks in biomedical Natural Language Processing (NLP). The detection of specific terms and entities and the relationships between them are key aspects for the development of more complex automatic systems in the biomedical field. In this work, we explore transfer learning techniques for incorporating information about negation into systems performing NER and RE. The main purpose of this research is to analyse to what extent the successful detection of negated entities in separate tasks helps in the detection of biomedical entities and their relationships. Three neural architectures are proposed in this work, all of them mainly based on Bidirectional Long Short-Term Memory (Bi-LSTM) networks and Conditional Random Fields (CRFs). While the first architecture is devoted to detecting triggers and scopes of negated entities in any domain, two specific models are developed for performing isolated NER tasks and joint NER and RE tasks in the biomedical domain. Then, weights related to negation detection learned by the first architecture are incorporated into those last models. Two different languages, Spanish and English, are taken into account in the experiments. Performance of the biomedical models is analysed both when the weights of the neural networks are randomly initialized, and when weights from the negation detection model are incorporated into them. Improvements of around 3.5% of F-Measure in the English language and more than 7% in the Spanish language are achieved in the NER task, while the NER+RE task increases F-Measure scores by more than 13% for the NER submodel and around 2% for the RE submodel. The obtained results allow us to conclude that negation-based transfer learning techniques are appropriate for performing biomedical NER and RE tasks. These results highlight the importance of detecting negation for improving the identification of biomedical entities and their relationships. The explored techniques show robustness by maintaining consistent results and improvements across different tasks and languages. [Display omitted] [ABSTRACT FROM AUTHOR]

Objective To detect negations of medical entities in free-text pathology reports with different approaches, and evaluate their performances. Methods and material Three different approaches were applied for negation detection: the lexicon-based approach was a rule-based method, relying on trigger terms and termination clues; the syntax-based approach was also a rule-based method, where the rules and negation patterns were designed using the dependency output from the Stanford parser; the machine-learning-based approach used a support vector machine as a classifier to build models with a number of features. A total of 284 English pathology reports of lymphoma were used for the study. Results The machine-learning-based approach had the best overall performance on the test set with micro-averaged F -score of 82.56%, while the syntax-based approach performed worst with 78.62% F -score. The lexicon-based approach attained an overall average precision of 89.74% and recall of 76.09%, which were significantly better than the results achieved by Negation Tagger with a similar approach. Discussion The lexicon-based approach benefitted from being customized to the corpus more than the other two methods. The errors in negation detection with the syntax-based approach producing poorest performance were mainly due to the poor parsing results, and the errors with the other methods were probably because of the abnormal grammatical structures. Conclusions A machine-learning-based approach has potential advantages for negation detection, and may be preferable for the task. To improve the overall performance, one of the possible solutions is to apply different approaches to each section in the reports. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to present the role and contribution of natural language processing techniques, in particular negation detection and word sense disambiguation in the process of Semantic Annotation of Archaeological Grey Literature. Archaeological reports contain a great deal of information that conveys facts and findings in different ways. This kind of information is highly relevant to the research and analysis of archaeological evidence but at the same time can be a hindrance for the accurate indexing of documents with respect to positive assertions. Design/methodology/approach - The paper presents a method for adapting the biomedicine oriented negation algorithm NegEx to the context of archaeology and discusses the evaluation results of the new modified negation detection module. A particular form of polysemy, which is inflicted by the definition of ontology classes and concerning the semantics of small finds in archaeology, is addressed by a domain specific word-sense disambiguation module. Findings - The performance of the negation dection module is compared against a "Gold Standard" that consists of 300 manually annotated pages of archaeological excavation and evaluation reports. The evaluation results are encouraging, delivering overall 89 per cent precision, 80 per cent recall and 83 per cent F-measure scores. The paper addresses limitations and future improvements of the current work and highlights the need for ontological modelling to accommodate negative assertions. Originality/value - The discussed NLP modules contribute to the aims of the OPTIMA pipeline delivering an innovative application of such methods in the context of archaeological reports for the semantic annotation of archaeological grey literature with respect to the CIDOC-CRM ontology. [ABSTRACT FROM AUTHOR]

Background: In order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus. We created a Dutch clinical corpus containing four types of anonymized clinical documents: entries from general practitioners, specialists’ letters, radiology reports, and discharge letters. Using a Dutch list of medical terms extracted from the Unified Medical Language System, we identified medical terms in the corpus with exact matching. The identified terms were annotated for negation, temporality, and experiencer properties. To adapt the ConText algorithm, we translated English trigger terms to Dutch and added several general and document specific enhancements, such as negation rules for general practitioners’ entries and a regular expression based temporality module. Results: The ContextD algorithm utilized 41 unique triggers to identify the contextual properties in the clinical corpus. For the negation property, the algorithm obtained an F-score from 87% to 93% for the different document types. For the experiencer property, the F-score was 99% to 100%. For the historical and hypothetical values of the temporality property, F-scores ranged from 26% to 54% and from 13% to 44%, respectively. Conclusions: The ContextD showed good performance in identifying negation and experiencer property values across all Dutch clinical document types. Accurate identification of the temporality property proved to be difficult and requires further work. The anonymized and annotated Dutch clinical corpus can serve as a useful resource for further algorithm development. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Typically detected via electrocardiograms (ECGs), QT interval prolongation is a known risk factor for sudden cardiac death. Since medications can promote or exacerbate the condition, detection of QT interval prolongation is important for clinical decision support. We investigated the accuracy of natural language processing (NLP) for identifying QT prolongation from cardiologist-generated, free-text ECG impressions compared to corrected QT (QTc) thresholds reported by ECG machines. Methods: After integrating negation detection to a locally developed natural language processor, the KnowledgeMap concept identifier, we evaluated NLP-based detection of QT prolongation compared to the calculated QTc on a set of 44,318 ECGs obtained from hospitalized patients. We also created a string query using regular expressions to identify QT prolongation. We calculated sensitivity and specificity of the methods using manual physician review of the cardiologist-generated reports as the gold standard. To investigate causes of “false positive” calculated QTc, we manually reviewed randomly selected ECGs with a long calculated QTc but no mention of QT prolongation. Separately, we validated the performance of the negation detection algorithm on 5000 manually categorized ECG phrases for any medical concept (not limited to QT prolongation) prior to developing the NLP query for QT prolongation. Results: The NLP query for QT prolongation correctly identified 2364 of 2373 ECGs with QT prolongation with a sensitivity of 0.996 and a positive predictive value of 1.000. There were no false positives. The regular expression query had a sensitivity of 0.999 and positive predictive value of 0.982. In contrast, the positive predictive value of common QTc thresholds derived from ECG machines was 0.07–0.25 with corresponding sensitivities of 0.994–0.046. The negation detection algorithm had a recall of 0.973 and precision of 0.982 for 10,490 concepts found within ECG impressions. Conclusion: NLP and regular expression queries of cardiologists’ ECG interpretations can more effectively identify QT prolongation than the automated QTc intervals reported by ECG machines. Future clinical decision support could employ NLP queries to detect QTc prolongation and other reported ECG abnormalities. [Copyright &y& Elsevier]

In clinical practice guidelines (CPGs) the medical information is stored in a narrative way. A large part of this information occurs in a negated form. The detection of negation in CPGs is an important task since it helps medical personnel to identify not occurring symptoms and diseases as well as treatment actions that should not be accomplished. We developed algorithms capable of Negation Detection in this kind of medical documents. According to our results, we are convinced that the involvement of syntactical methods can improve Negation Detection, not only in medical writings but also in arbitrary narrative texts. [ABSTRACT FROM AUTHOR]

The technique of information retrieval has been widely used in electronic medical record (EMR) systems. It's a pity that most existing methods have not considered the structures and language features of Chinese EMRs, which affects the performance of retrieval. To improve accuracy and comprehensiveness, we propose an improved algorithm of Chinese EMR retrieval. First, the weights of fields in Chinese EMRs are assigned based on the corresponding importance in clinical applications. Second, negative relations in EMRs are detected, and the retrieval scores of negative terms are adjusted accordingly. Third, the retrieval results are re-ranked by expansion terms and time information to enhance the recall without decreasing precision. Experiment results show that the improved algorithm increases the precision and recall significantly, which shows that the algorithm takes a full account of the characteristics of Chinese EMRs and fits the needs for clinical applications. [ABSTRACT FROM AUTHOR]

The context of medical conditions is an important feature to consider when processing clinical narratives. NegEx and its extension ConText became the most well-known rule-based systems that allow determining whether a medical condition is negated, historical or experienced by someone other than the patient in English clinical text. In this paper, we present a French adaptation and enrichment of FastContext which is the most recent, n-trie engine-based implementation of the ConText algorithm. We compiled an extensive list of French lexical cues by automatic and manual translation and enrichment. To evaluate French FastContext, we manually annotated the context of medical conditions present in two types of clinical narratives: (i)death certificates and (ii)electronic health records. Results show good performance across different context values on both types of clinical notes (on average 0.93 and 0.86 F1, respectively). Furthermore, French FastContext outperforms previously reported French systems for negation detection when compared on the same datasets and it is the first implementation of contextual temporality and experiencer identification reported for French. Finally, French FastContext has been implemented within the SIFR Annotator: a publicly accessible Web service to annotate French biomedical text data (http://bioportal.lirmm.fr/annotator). To our knowledge, this is the first implementation of a Web-based ConText-like system in a publicly accessible platform allowing non-natural-language-processing experts to both annotate and contextualize medical conditions in clinical notes. [ABSTRACT FROM AUTHOR]

This work deals with negation detection in the context of clinical texts. Negation detection is a key for decision support systems since negated events (detection of absence of some events) help ascertain current medical conditions. For artificial intelligence, negation detection is a valuable point as it can revert the meaning of a part of a text and, accordingly, influence other tasks such as medical dosage adjustment, the detection of adverse drug reactions or hospital acquired diseases. We focus on negated medical events such as disorders, findings and allergies. From Natural Language Processing (NLP) background, we refer to them as negated medical entities. A novelty of this work is that we approached this task as Named Entity Recognition (NER) with the restriction that just negated medical entities must be recognized (in an attempt to help distinguish them from non-negated ones). Our study is driven with Electronic Health Records (EHRs) written in Spanish. A challenge to cope with is the lexical variability (alternative medical forms, abbreviations, etc.). To this end, we employed an approach based on deep learning. Specifically, the system combines character embeddings to cope with out-of-vocabulary (OOV) words, Long Short-Term Memory (LSTM) networks to model contextual representations and it makes use of Conditional Random Fields (CRF) to classify each medical entity as either negated or not given the contextual dense representation. Moreover, we explored both embeddings created from words and embeddings created from lemmas. The best results were obtained with the lemmatized embeddings. Apparently, this approach reinforced the capability of the LSTMs to cope with the high lexical variability. The f-measure for exact-match was 65.1 and 82.4 for the partial-match. [ABSTRACT FROM AUTHOR]