dataesr/scientific-paragraphs-categorization huggingface dataset:
Starting from this position, we study the main features that characterize these techniques and evaluate their effectiveness using large-scale real-world datasets that comprise sentiment reviews. We focus on the feature engineering process and explore the impact of using semantic and taxonomic relationships, as well as different priorities for word types (Noun (NN), Adjective (ADJ), Adverb (ADV), Verb (VV)) to perform sentence-level SA. The main contributions of our proposition are summarized: -Explore existing SA techniques and evaluate their effectiveness, as well as efficiency using three publicly-available datasets. -Exploiting knowledge triplets encoded in generic knowledge repositories and measuring their impact on the quality of the utilized SA models. The rest of this paper is organized into four sections.
Therefore, as reported in Reference  [6] , working on document level is expensive and inaccurate in some scenarios. This is mainly because the whole document is treated as a single unit and the entire content of the document is classified as positive or negative towards a specific issue. This problem also appears when working at the paragraph level, as a paragraph may contain two sentences with different sentiment polarities. Therefore, other researchers have focused on SA at sentence level  [7] ,  [8]. In Reference  [7] , Pak and Paroubek analyzed a dataset of financial reports at sentence level using multiple sentence embedding models. They used models based on Siamese CBow and fastText. However, as reported by Basha and Rajput in  [9] , using a lexicon with predefined term polarities doesn't reflect any contextual sensitive polarities at the sentence level.
The results confirmed that the NLP techniques had a significant impact on the quality of the employed sentiment analyzers. Results confirmed that the NB classifier has outperformed the SVM and MaxE classifiers, however, it is not clear which combination of features should be used in what scenarios i. e. , sentiment classification tasks. In a recent work proposed in Reference  [14]  by Sohrabi and Hemmatian, Fatemeh, the authors used a dataset of social media posts obtained from Twitter to conduct a comparative study between several machine learning SA techniques (decision tree, neural network, SVM, and W-Bayes network) at sentence level. The authors used both the RapidMiner software package and Python programing language to make this comparison. In the proposed methodology, researchers used a series of NLP techniques, including normalization, removing stopwords, and tokenization.
For feature weighting, the authors used Word2vec and TF-IDF methods. The results confirmed significant variations in the accuracy of the utilized SAs after using the proposed NLP techniques. In  [15] , Krouska proposed a methodology to apply SA on three well known datasets, namely Obama-McCain debate (OMD), health care reform (HCR) and Stanford twitter sentiment gold standard (STS-Gold). Researchers compared several classification techniques using a variety of NLP techniques, including removing stop words, stimming, and n-gram tokenization. Results confirmed that n-grams captured by extrinsic resources has proved to improve the quality of the SAs.
Data acquisition and cleansing Sources of sentiment sentences can vary on the Web. Twitter and other social media websites are among the most commonly referred to sources  [2]. For experimental evaluation purposes, we use the wellknown internet movie database (IMDB) movie reviews dataset, which is publicly available at Kaggle. In addition, we collected the 10,662 LightSide dataset, and 300 other generic movie reviews from Twitter. The first task after the data acquisition is to clean the raw data as it normally contains some special characters, including hashtags, consecutive white spaces, URLs, and unnecessary symbols. In addition, there is a set of emoticons that we cleaned using a pre-defined set of icon representations. After this step, we proceed to the second phase of data processing, that is tokenization and feature extraction.
Stopwords removal, word stemming and lemmatization Raw text of sentiment sentences usually contains a lot of words that are frequently repeated. Such words have no significant contribution in determining the sentiment orientation of sentences. Moreover, they may have a negative impact on determining the polarity of sentences as reported in  [3]. These words are Int J Artif Intell ISSN: 2252-8938  A hybrid composite features based sentence level sentiment analyzer (Mohammed Maree) 287 referred to as stopwords. Removing such words is a crucial step in the data pre-processing phase. Some researchers divided stopwords into two types, these are: 1) general stopwords and 2) domain-specific stopwords  [16]. General stopwords such as (the, a, on, of, …) are considered as stopwords regardless of the domain of the dataset.
On the other hand, domain-specific stopwords are those words that appear to be of little significance in deriving the meaning of a given sentence in a particular domain  [19]. Such words can be identified through the utilization of the tf. idf model in the same manner as described in  [19]. For example, as far as the movie reviews dataset is concerned, words such as, movie, actor, and film appear very frequently in the sentiment sentences. These words are specific stopwords to this field and can be regarded as stopwords when they appear redundantly in sentences  [18]. Stemming, on the other hand, is one of the common morphological analysis processes that is mainly concerned with the removal of derivational affixes in the hope of achieving a common base form for a given word. The goal of this process is to reduce inflectional forms and achieve a common base form for words in sentiment sentences.
It is a rule-based stemmer that consists of five phases of word reductions that are applied sequentially. Each rule group is applied to the longest suffix. One of main limitations of this stemmer is that it may return the same form for two words with different sentiment polarities. For instance, the word "Dependability" which has a positive polarity and the word "Dependent" which has a negative polarity are both stemmed to "Depend". Similarly, Snowball stemmer, which was developed based on Porter's stemmer, shares many of the limitations that are inherent in Porter stemmer. However, this technique has demonstrated to produce more promising stemming results against those produced by Porter. In addition, as reported by Rao in  [24] , the performance of Snowball is higher than Porter and it can be applied on many languages other than English.
Accordingly, in the context of our research work, we attempt to investigate the impact on combining multiple features, including semantically-related features, on the quality of a number of machines learning based sentiment classification methods. In particular, we employ the well-known TF-IDF feature weighting technique with the combination of n-gram tokens and semantic features extracted from WordNet. As such, a sentiment sentence is assigned a weight based on  (1). The unit we use to represent each sentence is D. We use the variable D to denote sentences in the dataset to maintain consistency with the general terms used to define the TF-IDF equation.
TF-IDF (t) = (1+ log10 TF(t)) * log10 (N/DF(t)), (1) Where: -N: is the number of sentiment sentences in the corpus. -DF: represents the number of sentences that contain term t. -TF: is the number of occurrences of term t in sentence D. It is important to point out here that the TF-IDF feature weighting technique is applied on composite features extracted from pre-processed text of sentiment sentences. A wide range of machine learning (supervised, semi-supervised and un-supervised) techniques have been developed and utilized for sentiment analysis purposes  [29] ,  [30]. The input to any of the utilized methods is a set of composite features generated based on the techniques discussed in the previous section. As such, the quality of the utilized sentiment classifier will be highly dependent on the quality of the selected features, especially when dealing with highly skewed datasets.
RESULTS AND DISCUSSION To perform the experiments and develop the proposed NLP pipeline, we used Python program language. This language was also utilized for pre-processing the three publicly-available datasets that are presented in Table  1. Before proceeding with the discussion on the conducted experiments, we present some details about the used datasets. In our experiments, we classified the reviews using four machine learning algorithms. These are: SVMs, NB, LR, and RF classifiers. And we have used accuracy metric to compare between these classifiers. Accuracy in this context is the ratio of correctly classified reviews to the total reviews. We also used the natural language toolkit (NLTK) to apply some NLP techniques. Using the NLTK library, we employed two types of tokenizers namely: wordpunct and casual tokenizers.
We have also used unigrams features and n-grams features (uni-grams and bi-grams). Moreover, we have updated NLTK stopword list to filter out sentences. It is important to point out that we removed all negation words from the predefined stopwords list. This is because such words have an important impact on identified correct sentiment orientations. We also enriched the list with additional words that appear frequently in the datasets (domainspecific stopwords). Example of such words are: story, movies, watching, acting, character, and film. Accordingly, we exploited a number of NLP pipelines to study the impact of each pipeline phases on the quality of each classifier. Tables  2 3 illustrate the variations on the accuracy results when utilizing each pipeline. In our experiments we have used 19 different combinations of NLP techniques.
Each combination was applied twice, using Wordpunct tokenizers. Each combination of NLP techniques produced different set of features. Which means different results for the sentiment reviews polarity. Based on the results in Tables  2 3 , we notice that all classifiers are positively affected when we used n-grams, snowball stemmer, sentence enrichment with adjective synonyms, lemmatization, and when we removed unwanted signs and numbers. Also, when we have a combination of them. This means that these NLP techniques have an important and positive impact in determining the polarity of reviews. Also, we note that when we do synonyms enrichment to adjectives, we obtain better results than when perform noun-based enrichment. When we used the Wordpunct_tokenizer with the IMDB dataset, we obtained the best result (90.
43%). In particular, when we used SVM classifier with uni-grams and bi-grams with lemmatization. Both the NB and LR classifiers produced (86. 83%) and (90. 11%) accuracy results when we used n-grams feature with sentence enrichment by Adj synonyms and lemmatization. The RF classifier produced the best result (86. 20%) when we used n-grams with stopwords removal. Considering the Sentiment Sentences dataset that is used by the authors of  [2] , we obtained the best result (78. 08%) when using the SVM classifier with Snowball stemmer. Also, the RF classifier produced the best predictions (70. 33%) when we used Snowball stemmer. As far as the NB and LR classifiers are concerned, they produced the best results (77. 77%) and (76. 49%), respectively when we using n-grams with Snowball stemmer. For the LightSide's Movie_Reviews dataset, we obtained the best result (82.
22%) when we used the RF classifier with n_grams and Adjective synonyms combination. Also, the SVM classifier produced the best result (74. 44%) when we used n_grams, sentence enrichment with Adjective synonyms and lemmatization. The NB classifier produced the best result (57. 78%) when using Snowball stemmer with Stopwords removal. While the LR produced best result (70. 00%) when we used sentence enrichment with Adjective synonyms. When using the Casual_tokenizer, we find that for the IMDB dataset, we got the best result (90. 50%) with the SVM classifier when applied on the original text. On the other hand, the NB classifier produced the best result (86. 91%) when using n-gram features with sentence enrichment by Adj synonyms and lemmatization. Both the LR and RF classifiers produced the best results (90.
06%) and (86. 13%) when we used n-grams with stopwords removal. In Sentiment_sentences dataset We got the best result (77. 64%) when with the SVM classifier when employing the Snowball stemmer. Also, both the NB and LR classifiers produced the best results (77. 58%) and (76. 52%), respectively when we used Snowball stemmer. For the RF classifier, it produced the best result (69. 95%) when we used the Snowball stemmer with Stopwords removal. For the LightSide's Movie_Reviews dataset, we obtained the best result (75. 56%) when using the RF classifier with n_grams and sentence enrichment with Adjective synonyms. Also, the LR classifier produced the best result (70. 00%) when we used n_grams with sentence enrichment with Adjective synonyms. On the other hand, the NB classifier produced the best result (58.
Comparison with other SA models In this section, we present a comparison between the results that we obtained when using the IMDB dataset with respect to similar previous works, namely  [26] ,  [31]. The researchers used a group of supervised learning algorithms such as the SVM, NB, K-nearest neighbor (KNN) and Maximum Entropy. The results obtained by the researchers are shown in Table  4. As shown in this table, the Maximum Entropy classifier proved to outperform the rest of the classifiers as it produced the highest accuracy result which is 83. 93%. It was followed by the SVM classifier with an accuracy of 67. 60%. The authors of  [26]  proposed models to classify the IMDB reviews by using six layers in neural network architecture, and they utilized word weights to predict sentiment classes.
CONCLUSION Current lexicon-based and machine learning based sentiment analysis approaches are still hindered by limitations, such as the insufficient knowledge about words' prior polarities and their contextual polarities in sentences, and the manual training required for predicting the sentiment classes of sentences, which is time consuming, domain-dependent and error-prone. In this article, we experimentally evaluated four probabilistic and machine learning based sentiment classifiers, namely SVM, NB, LR and RFs. We evaluated the quality of each of these techniques in predicting the sentiment orientation using three real-world datasets that comprised a total of 60,962 sentiment sentences. Our main goal was to study the impact of a variety of NLP pipelines on the quality of the predicted sentiment orientations.
We also compared that with the utilization of lexical semantic knowledge captured by WordNet. Findings demonstrate that there is an impact of varying the employed NLP pipelines and the incorporation of semantic relationships on the quality of the sentiment analyzers. In particular, we concluded that coupling lemmatization and knowledge-based n-gram features proved to produce higher accuracy rates when applied on the IMDB dataset. With this coupling, the accuracy of the SVM classifier has improved to be 90. 43%. For the three other classifiers (NB, RF and LR), the quality of the sentiment classification has also improved to be 86. 83%, 90. 11%, 86. 20%, respectively. It is important to highlight the fact that the used pipeline phases are of less complexity in terms of configuring their hyper-parameters, as well as possibly computational cost when compared with other deep learning models.
INTRODUCTION 2] [3] [4] [5] [6]  Which patients truly benefit from PARPi maintenance remains controversial. Although BRCA mutation and, to a lesser extent, other measures of homologous recombination deficiency (HRD) are undoubtedly associated with PARPi activity,  [7] [8] [9] [10]  data suggest that the efficacy of these drugs cannot be unequivocally explained by these biomarkers, and there are still knowledge gaps in the understanding of determinants of PARPi efficacy. Among potential predictive biomarkers, sensitivity to platinum-based chemotherapy is very relevant. ] [12]  Cancer InterGroup meta-analysis data set, have indicated that cancer antigen (CA)-125 elimination rate constant K (KELIM), a modeled kinetic parameter on the basis of CA-125 measurements during the first 100 days of adjuvant or neoadjuvant chemotherapy, represents a reproducible indicator of tumor-intrinsic chemosensitivity in the first-line setting.
Relevance The tumor primary chemosensitivity, assessable by elimination rate constant K score (KELIM calculable online), may be a complementary parameter to integrate with BRCA mutation and HR status for understanding the prognosis and the benefit to expect from maintenance with the poly (adenosine diphosphate-ribose) polymerase inhibitor veliparib. To be included in the present analysis, patients had to have at least three available values of CA-125 during the first 100 days of adjuvant chemotherapy after primary debulking surgery (PDS) or before the date of IDS. The following data were collected: age, somatic or germline BRCA mutation status (BRCA mutation or BRCA wild-type), HR status as determined by the Myriad BRACAnalysis CDx or myChoice HRD CDx assay (HRD: BRCA-mutated or GIS $ 33; or HRproficient (HRP): BRCA wild-type and GIS , 33), disease stage (III or IV), completeness of surgery on the basis of postoperative lesions assessed by the surgeon (complete surgery with no residual lesions or incomplete surgery with microscopic residual lesions between 0 and 1 cm or with macroscopic residual lesions.
Patient Characteristics One thousand one hundred forty patients were enrolled in the VELIA study. Among 854 patients with $ 3 available measurements of CA-125 during the first three cycles of neoadjuvant or adjuvant chemotherapy, 700 patients (81. 9%) received PDS and 154 patients (18. 1%) received IDS (Data Supplement, online only and Table  1 ). The distribution of patients treated with the 3-weekly or the weekly carboplatinpaclitaxel regimens was well balanced between patients with favorable or unfavorable KELIM. The median number of CA-125 measurements per patient was four in the PDS cohort and three in the IDS cohort. Key demographic and clinical characteristics of the 854 patients are given in Table  1.
2 months for those who had unfavorable KELIM (hazard ratio, 0. 61; 95% CI, 0. 42 to 0. 87). In the control arm, the median PFS was 20. 9 months for patients with favorable KELIM and 15. 4 months for patients with unfavorable KELIM (hazard ratio, 0. 69; 95% CI, 0. 49 to 0. 95; Fig 1). In multivariate analysis, KELIM status was a significant independent prognostic covariate (hazard ratio, 0. 67; 95% CI, 0. 52 to 0. 85), along with the treatment arm, surgery outcomes on the basis of postoperative lesions, and HR status (Data Supplement). Similar data of the prognostic value of KELIM were observed in the IDS cohort, with larger CIs because of the lower number of patients (Fig  1.
For example, KELIM could be assessed in patients with the CA-125 values observed during the first three to four cycles of adjuvant/neoadjuvant chemotherapy, and veliparib would be associated with chemotherapy and then given as a maintenance treatment in patients with favorable KELIM and BRCA mutation or HRD cancer. Of note, KELIM of patients is easily calculable online (for both neoadjuvant chemotherapy  29  and adjuvant chemotherapy  30  ). In addition, KELIM might be of interest for identifying the patients who are more likely to benefit from other PARPis. KELIM is being prospectively assessed in the phase III trial NIVARNA-1 comparing the efficacy of niraparib with/without bevacizumab in patients operated with complete PDS (ClinicalTrials. gov identifier: NCT05183984.
METHODOLOGY In order to conduct a systematic study on the correlation and interference between sports leadership and gender, with a particular focus on women, a methodology of systematic literature review (SLR) in four phases was employed to select articles published on the topic of gender equality in leadership in the sports sector from French and English databases. The first step involved reviewing French and English articles published between 2000 and 2022, identifying key terms related to this research and possible queries to refine the results: ("Leadership" OR "Management" OR "decision-making positions" OR "Barriers") AND ("Gender" OR "Sport" OR "Sport Organizations" OR "Women"). In total, 157 references were extracted from various databases, to which additional references such as unpublished theses, reports, and statements were added.
The second step was to select the leading indexed journals and digital databases, including Cairn, Scopus, ScienceDirect, and Web of Science. The third step involved the cleaning phase of the collected results in RIS format and storing them in the ZOTERO platform. This step included removing irrelevant articles, completing missing information for meta-analysis, eliminating duplicate articles, and keeping only documents published within the specified timeframe (2000-2022). In the end, 130 references were retained from the selected articles after removing duplicates, references unrelated to the research, and non-exploitable documents. Having gone through the titles and abstracts of all publications, a total of 80 articles were pinpointed that directly tackled the topic of women in sports leadership.
However, after meticulously scrutinizing these 80 references, 48 were excluded due to their lack of alignment with the research objective. These publications revolved around leadership styles or other subjects beyond the scope of the study, which is not concerned with the underrepresentation of women in leadership Qualitative and quantitative analysis of the corpus using NVivo. roles. Additionally, certain studies did not revolve around the sports context, leading to the removal of those not explicitly related to sports. In the end, 32 publications were selected (comprising 29 review articles and 3 thesis dissertations) for this literature review. Figure  1  illustrates the stages of the SLR methodology. The fourth and final stage entailed harnessing the outcomes from the third stage (cleaned RIS database) through NVIVO for qualitative and quantitative treatment.
C. Proposed TMO-OFDMA Scheme As mentioned earlier, in our proposed TMO-OFDMA scheme, TDMA scheduling is performed for a group of O-OFDMA-based users. A set of users transmit data during their allotted time window, as shown in Fig. 4. Here, considering the number of users Γ = 12, users are mapped into three different time slots for TMO-OFDMA. Each time-slot in TMO-OFDMA consists of 4 users transmitting data to their allocated time slots, unlike O-OFDMA,. To attain the same data rate as O-OFDMA, TMO-OFDMA users must finish their data transmissions within the same time frame as O-OFDMA. Denoting the total number of TMO-OFDMA slots by N , each slot in TMO-OFDMA must transmit N times the rate of the conventional O-OFDMA scheme. Although, this will increase the required transmission BW of the system, we will show the advantage of our proposed scheme particularly for such low data-rate IoT applications later in Sections IV-C and V.
C. TMO-OFDMA 1) MAI Effect: Here, we apply our proposed TMO-OFDMA scheme for the worst case MAI scenario as in the previous subsection. Focusing on the ACO signaling due to its energy efficiency, we compare the BER performance between the ACO-OFDMA and TM-ACO-OFDMA. For the TM-ACO-OFDMA, we transmit a group of users data into their dedicated two different time slots denoted by t SLOT1 and t SLOT2 , each with Γ = 8 users, as shown in Fig. 10. For each slot, we consider the same M = 256 as for the ACO-OFDMA, where the unallocated subcarriers are replaced by zeros, resulting in ξ DCO = 0. 5 for 16-QAM. Therefore, in order to transmit data at the same rate as the ACO-OFDMA scheme, the required BW per slot for TMO-ACO-OFDMA is B TM-ACO = 32 MHz. Figure  11  shows the BER performance result for ACO-OFDMA and TMO-ACO-OFDMA for both MAI and NMAI case.