dataesr/scientific-paragraphs-categorization huggingface dataset:
Together, we can build a more robust understanding of LTP science and realizing new applications. registering or paying a fee. (3) The databases are dynamic and contributors are free to make changes as they see fit. Consequences of these guidelines are that data for the same processes can be listed in multiple databases and that different versions of the same data can be accessed on the website. Hence users are strongly encouraged to reference data downloaded from LXCat in all publications making use of the data. The database contributors retain ownership and are responsible for the contents and maintenance of the individual databases. LXCat does not authorize third parties (commercial interests, in particular) to redistribute data from the LXCat site. The following guidelines are prescribed for use of data on LXCat by third parties: 1.
• LXCat must be notified in advance and a Memorandum Of Understanding (MOU) must be prepared between the LXCat team, the contributors who agree to have their data set redistributed within a software or any other shareable format and the MOU must be signed by all parties. • A statement must be clearly visible with the redistributed data saying that "The data were downloaded from [database name], www. lxcat. net, [retrieval date]. LXCat is an open-access website with databases contributed by members of the scientific community. ".
Figure11. Swarm parameters calculated using the online Boltzmann solver for a pure CO 2 gas using the Biagi database [80, 198]  and comparison with experimental swarm measurements [133, 135,  fromDutton [132], ETHZ [134], Heidelberg [154], LAPLACE [149]  and UNAM [150] databases as a function of reduced electric field. The computed quantities are: (a) characteristic electron energy, (b) product of the gas number density and electron diffusion coefficient, (c) product of the gas number density and electron mobility coefficient, (d) Townsend coefficients.
2 H 2 , C 2 H 4 , C 2 H 6 , CCl 2 F 2 , CCl 4 , CF 4 , CH 4 , CO 2 , H 2 O, HCl, N 2 O, NH 3 , NO, Si 2 H 6 , SiH 4 , and SO 2. • IST-Lisbon [35]: This database contains data compiled from the literature from different authors, resulting in complete sets of cross-sections, validated against measured swarm parameters by solving the two-term homogeneous electron Boltzmann equation [101]. To improve predictions at low E/N, some sets need to be completed with rotational cross-sections, also available in the database. Boltzmann solvers assembled by W. L. Morgan for the species He, Ne, Ar, Xe, Kr, C, C 2 H 2 , C 2 H 4 , C 2 H 6 , C 3 H 8 , CF 4 , CH, CH 2 , CH 3 , CH 4 , Cl 2 , CO, CO 2 , F 2 , H, H 2 , H 2 O, HCl, N, N 2 , NH 3 , NO, O, O 2 , O 3 , SF 6 , and SiH 4. • NGFSRDW [111]: A set of excitation cross-section to individual fine-structure levels of Ar and Kr (a collection of 148 records added since 2016) calculated by Al Stauffer and co-workers.
• Phelps [112]: A compilation of atomic and molecular data, assembled and evaluated by A. V. Phelps and collaborators, including the species He, Ne, Ar, Xe, CO, CO 2 , H 2 , H 2 O, Mg, N 2 , Na, NO, O 2 , and SF 6. This database includes the species BF 3 , C 2 , C 2 H 2 , C 2 OH 6 , C 3 , C 3 H 4 , C 3 N, CaF, CF, CF 2 , CF 4 , CH, CH 4 , CNH, CO, CO 2 , CONH 3 , COS, CS, F 2 O, H 2 , H 2 O, H 2 S, CH 4 , HBr, HCHO, HCN, HCP, Kr, N 2 , N 2 O, NH 3 , NO 2 , O 2 , O 3 , PH 3 , SiF 2 , SiH 4 , SiO, and SO 2.
Different electron-swarm experiments were setup by Bernhard Schmidt and co-workers which allowed to measure electron transport parameters in pure electric and perpendicular crossed electric and magnetic fields. This database contains only those data for B=0. Additional swarm data for non-zero B fields will be made available in tabular and graphical form when resources become available. The following gases and gas mixtures are present: C 2 H 4 , C 2 H 6 , C 2 H 6 O, C 3 H 6 , C 3 H 8 , CH 4 , CO 2 , D 2 , H 2 , N 2 , Ar:C 2 H 6 O, C 4 H 10 :Ar, C 4 H 10 :He, CO 2 :Ar, CO 2 :Ne, H 2 :Ar, H 2 :Kr, H 2 :Xe, and N 2 :Ar. These data were collected by Malte Hildebrandt, presently at the Paul Scherrer Institute in Switzerland, from publications and theses in the Heidelberg group and were made available to the LXCat team for inclusion in LXCat.
Acknowledgments: The LXCat team would like to acknowledge the  Gaseous Electronics Conference  for having hosted annual workshops on the "Plasma Data Exchange Project" for three consecutive years (2011-2013) and since 2014, the LXCat evening workshop series. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the  U. S. Department of Energy  or the United States government.
2 ]Figure 4. 24 Figure 4. Differential scattering cross-section for elastic and inelastic scattering processes with argon ground state. (a) Experimental measurement of elastic electron/Ar collisions from ANU database [39, 40]  for different electron-impact energies with label from 1 to 8: 1 eV, 1. 5 eV, 2 eV, 3 eV, 3. 14 eV, 5 eV, 7. 5 eV and 10 eV; Theoretical calculations from the BSR database [41] [42] [43]  using the R-matrix method of differential cross-sections for electrons with ground state Ar: (b) elastic collision; (c) electronic excitation from ground state to Ar * 4s [1/2] 1 state; (d) electronic excitation from ground state to Ar * 4s[3/2] 2 state.
• Phelps [112]: A compilation of atomic and molecular data, assembled and evaluated by A. V. Phelps and collaborators, including the species He, Ne, Ar, Xe, CO, CO 2 , H 2 , H 2 O, Mg, N 2 , Na, NO, O 2 , and SF 6. This database includes the species BF 3 , C 2 , C 2 H 2 , C 2 OH 6 , C 3 , C 3 H 4 , C 3 N, CaF, CF, CF 2 , CF 4 , CH, CH 4 , CNH, CO, CO 2 , CONH 3 , COS, CS, F 2 O, H 2 , H 2 O, H 2 S, CH 4 , HBr, HCHO, HCN, HCP, Kr, N 2 , N 2 O, NH 3 , NO 2 , O 2 , O 3 , PH 3 , SiF 2 , SiH 4 , SiO, and SO 2. The following species are represented: He (replaced since 2016 [102]), Ar, CH 4 , H, H 2 , N (replaced since 2016 [103]), N 2 , O 2 , and added since 2016: CO [104], CO 2 [105], O [106], and O 2 vibrational states [107]. • Itikawa [108]: A set of recommended cross-sections based on experimental data for the species CO, CO 2 , H 2 , H 2 O, N 2 , NO (added since 2016 [109]), and O 2.
F. Biagi's FORTRAN code, MagBoltz, versions 8. 9 and later[81,82], the exact version depending on the species as listed in the database. The database contains cross-sections for rare gases He, Ne, Ar, Kr, and Xe, and molecules H 2 , D 2 , N 2 , O 2 , CO 2 and SF 6. The transcription of data for other gases is in progress. These data were compiled for use with a Monte Carlo simulation (or multi-term Boltzmann code), but their use in a 2-term Boltzmann solver gives reasonably accurate results in most cases. Please note that the LXCat data tables do not have the same energy grid as the original data in the MagBoltz code. • Biagi-v7. 1 [83]: This database contains cross-sections for the rare gases He, Ne, Ar, Kr, and Xe, transcribed by the LXCat team from the Magboltz code version 7. 1 by Stephen Biagi [81,82].
The excitation cross-sections are grouped into a few effective excitation levels. These cross-sections were compiled for use in a Monte Carlo (or multi-term Boltzmann) code. • Bordage [84]: These cross-sections for species CF 4 , CHF 3 , and Si(CH 3 ) 4 were derived by using a multi-term solution of the Boltzmann equation in the approximation of the hydrodynamic regime as described in [85,86]. • BSR [43]: The results in this database are from a semi-relativistic Breit-Pauli B-spline R-matrix (close-coupling) calculations [41,42]. It contains data for the species Ne, Ar, Kr, Xe, Be, C, N and F. Compared to 2016 the Ar data were extended with 469 records [87] and Be (201 records), C [88] (1178 records), N [89] (353 records), and F [Campbell of Flinders University, containing data for the species CO 2 , N 2 , NO, and O 2.
• Hayashi [100]: These data sets were derived by comparing calculated swarm parameters, using as input the cross-sections sets in this database, with measurements. It contains data for the species Ar, Xe, C • Puech [113]: Cross-section sets assembled by V. Puech and colleagues in late 1980s and 1990s for the species Ne, Ar, Xe, C 2 H 4 , and C 3 H 6. • QUANTEMOL [114]: Data calculated and published by Quantemol Ltd. and associated researchers using Quantemol-N software/expert system [115]. • SIGLO [116]: A compilation of data used in-house by the group GREPHE at LAPLACE in Toulouse, taken from different sources and including the species He, Ne, Ar, Xe, Kr, Cl 2 , CO 2 , Cu, F 2 , HCl, Hg, N 2 , SF 6 , and SiH 4. • TRINITI [117]: Cross-sections retrieved from the EEDF software package for calculations of the electron energy distribution function [118], for the species He, Ne, Ar, Xe, BCl, BCl 2 , BCl 3 , CF 3 I, CF 4 , CH 4 , CO, CO 2 , Cs, F 2 , H, H 2 , H 2 O, HCl, N 2 , Na, NF 3 , O, O 2 , and SF 6.
• UT (University of Tartu) [155,156]: A compilation of molecular data for air, C 5 F 10 O, and their mixture determined by the Plasma Physics laboratory in University of Tartu, Estonia. [152,153]wing databases are new since 2016:• CDAP (State-to-state electron-impact excitation rate coefficients)[151]: State-to-state electron-impact excitation rate coefficients between argon neutral or xenon ionic states obtained by combination of plasma experiments and theoretical calculations. In particular, the data are used for collisional-radiative modeling and examined in the afterglow plasma of argon (CDAP method) or examined in the different regions of electric propulsion devices using xenon. Rate coefficients for both atoms and ions are given here, and can be used for plasma modeling and optical emission spectroscopy (OES)[152,153].
Online social networking platforms allow people to freely express their ideas, opinions, and emotions negatively or positively. Previous studies have examined user's sentiments on these platforms to study their behaviour in different contexts and purposes. The mechanism of collecting public opinion information has attracted researchers to automatically classify the polarity of public opinions based on the use of concise language in messages, such as tweets, by analyzing social media data. In this paper, we extend the preceding work  [1] , by proposing an unsupervised approach to automatically detect extreme opinions/posts in social networks. We have evaluated our performance on five different social network and media datasets. In this work, we use the semi-supervised approach BERT to check the accuracy of our classified dataset.
[5]. An unusual form of sentiment analysis is the detection and classification of extreme sentiments, which are the most negative and positive sentiments about a particular subject, object, or person  [6]. In a more general form, an extreme sentiment can be seen described as the worst or best opinion, judgment, or evaluation formed in one's mind about a particular thing or person. However, this paper considers an extreme sentiment only as a personal extreme positive or extreme negative sentiment. In the work we have recently developed, described in this paper, we have applied deep learning  [7]  models to detect extreme sentiments in text coming from social platforms. In particular, we propose to use pre-trained BERT  [8]  to improve the efficacy of the detection. BERT is based on a Semi-Supervised approach, pre-trained trained on a massive dataset.
Thus, in addition, we must use Transfer Learning  [9]  which is typically done for tasks where the dataset contains too little data to train a full-scale model from scratch. The model is then fine-tuned, where we unpack the basic model and re-train it on the new data with a very low learning rate. This can potentially lead to significant improvements by gradually adapting pre-trained trained features to the new data. The document is structured as follows: Section 2 discusses related work and in Section 3 we focus on BERT. Section 4 presents the experimental setup.
Wagh et al. [14]  designed a general sentiment classification to analyze whether a data label is available or unavailable in the target domain. The study analyzed the public dataset of four million tweets from Stanford University to predict the sentiment polarity in user opinions. SA using Hadoop, which rapidly runs large datasets on a real-time Hadoop cluster, was presented by Mane et al. [15]. It is a platform designed to solve large, unstructured, and complex big data problems using the divide-and-conquer approach to data processing. The study used a number-based approach to scale statements into several classes that assigned an appropriate range of different sentiments. SENTA  [16]  is an SA tool that offers many features to the end-user. The authors collect texts from Twitter and use SENTA to perform multiclass SA on the texts.
Sentiment Analysis Datasets SA development and tuning requires sizeable labelled training datasets, also known as the SA training dataset. The first step in developing the analysis requires an SA dataset with thousands of statements already labelled as positive, negative, or neutral. Finding training data is difficult because a human expert must determine and label the polarity of each statement in the training data. Using already available training data reduces the time and effort required to develop a new dataset. The work in  [19]  uses Sentiment 140  [20]  and SentiStrength on a prominent representative set of research articles, explicitly applying some techniques to sentiment analysis of articles circulating on Twitter. The dataset consists of two CVS files, one for testing and one for training.
Sentiment 140 provides a sentiment value per tweet on a scale from 0 (negative) to 4 (positive). The values have been converted into three sentiment categories: positive, negative, and neutral for better comparison. In our work, we chose the test file for the evaluation of our system. The authors of the paper  [21]  use the Twitter for Sentiment Analysis (T4SA)  [22]  visual dataset, which contains text and multimedia data to examine user sentiment. The authors collected Twitter data via a continuous tracker for six months and used it for a visual assessment of SA. The study of  [23] , which aims to detect users' opinions on movie reviews using the RTpolarity  [24]  dataset, classified 2000 comments into two different categories. In general, the comments mainly consist of sentences. The authors classify user sentiments at the sentence level and then classify all comments as opinions.
Ribeiro et al. [28]  present a sentence-level comparison of twenty-four popular sentiment analysis methods, based on a benchmark of eighteen labelled datasets. Performance was evaluated on two sentiment classification tasks: negative vs positive and three classes, namely negative, neutral and positive. However, these studies never compare the effectiveness of sentiment analysis methods or sentiment lexicons on the specific task of identifying extreme sentiments, i. e. , extremely positive and extremely negative sentiments. To the best of our knowledge, the present work is one of the few direct attempts to identify extreme sentiments, i. e. , extremely positive and/or extremely negative sentiments on social platforms, using BERT  [8].
ExtremeSentiLex In our previous work  [1] , we proposed an unsupervised approach for automatic detection of people's extreme sentiments on social networks. The approach is based on two steps: 1) Extreme Sentiment Generator (ESG) -we automatically build a standard lexicon consisting of extreme positive and negative sentiment terms, and extend that same lexicon with a method based on word embeddings; 2) Extreme Sentiment Classifier (ESC) -to validate the lexicon, using an unsupervised approach for automatic detection of extreme sentiments. We further evaluated our performance on five different social networks and media datasets (Section 2. 4). We designed and developed a prototype system composed of two components, i. e. , ESG and ESC. ESG, based on statistical methods, is applied on SentiWordNet 3.
0 and SenticNet 5 to generate a standard lexical resource known as ExtremeSentiLex that contains only extreme positive and negative terms. Additionally, we extend this new lexicon with new terms through the word embedding method  [29] , so we can study the behaviour of our tools when tested with more terms. Antiextremism agencies can also use these lexical resources to find an extreme opinion(s) on social networks to counter violent extremism. We embed the lexicons in the ESC and run them on the compilation of five different datasets, constituted of social network and media posts, (Section 2. 4). The purpose of this experimentation is to assess the performance of our tool, and this evaluation will validate our hypothesis that the ESC finds posts with extremely negative and positive sentiments in these datasets.
To obtain more objective results, we use a confusion matrix to calculate recall, precision, F1 score, and accuracy to check the performance of the ESC. In preceding work, we presented and discussed the initial results of each dataset individually. The arrangements for these tables are different, according to each dataset itself from the original settings. In our case, P -Positive, N -Negative and Neutral are the original polarity of the posts, EP are posts classified as positive extremes, EN means posts classified as negative extremes, and E+INC are posts classified as non-extreme or inconclusive. We concluded that the extended lexicon detects more extreme posts. There is an almost 2%-5% increase in each category for RT-polarity, Sentiment 140, TurntoIslam and Ansar1datasets. The significant increase is in the result of T4SA data, almost 22% to 24% for a total number of the extreme and total number of positive extreme and 1% of total negative extreme.
BERT is pre-trained on a massive corpus of unlabelled text, including Wikipedia (2,500 million words) and Book Corpus (800 million words). This pre-training step is half of the magic behind BERT's success. As the model is trained on a large text corpus, the model starts to pick up a more profound and intimate understanding of how the language works. This knowledge is the backbone that is useful for almost any NLP task. The most helpful feature of BERT is fine-tuning, whereby by adding just some of the additional output layers, we can create state-of-the-art models for various NLP tasks. BERT is currently being used by Google to optimize the interpretation of search engine queries. Initially, it was limited to the English language, but by December 2019, the model had already been rolled out in over 70 languages.
BERT takes an input of a sequence of up to 512 tokens and outputs the sequence representation. The sequence has one or two segments, where the first token of the sequence is always [CLS] and contains the particular classification embedding, and another special token [SEP] is used to separate the segments. BERT picks the final hidden state h of the first token [CLS] for text classification tasks to represent the complete sequence. In order to get the predicted probabilities from the trained model, a softmax classifier is added to the top of the BERT model. Firstly, the dataset is vectorized for feeding it to the classifier since it is originally in text format. Different models are available for vectorizing text but BERT learns contextual-embedding rather than learning context-free, such as in the case of word2vec.
Experimental Setup We use BERT for this experimental setup, a transformer-based machine learning technique for natural language processing. We use the datasets classified in  [1]  in the first phase for training while our target variable is polarity which we have calculated. We experiment on each dataset, and at the end, we combine all datasets to check overall performance. This comes in handy as the type of data can be a primary driver in determining the classification. So, the mixing of diverse data challenges the model and allow space for better insights. Loading Data: We load the classified dataset from Extremesentilex containing six files. We use all of these files for our experiment except the 1 Multilayer Perceptron. Detect Extreme Sentiments on Social Networks using BERT Sentiment140 training file because of missing the extreme negative class in polarity.
The files are in text format that is then loaded in pandas data frames. The primary data we need is in two columns, "message" and "polarity". We drop other columns as they are not needed. Preprocessing: In preprocessing, we use one-hot encoding for our polarity categorical data. Another extra step is necessary in the case of dataset file T4SA. This file is encoded and contains a byte string. We decode the file and clean the text containing special characters. Text split for longer text: BERT has a limitation of 512 max length for input characters. It means we cannot input data longer than 512 for training, while our dataset files contain longer text. Extreme Ansar1 contains 15325 characters long input feature, and extreme TurntoIslam has a maximum text length of 10034 characters. This data cannot be used directly as input for the training BERT model.
, predicting well for inconclusive and positive extreme while poorly with negative extreme. Negative extreme happens to be the essential kind of terms which we cannot ignore. So, we use undersampling and a mix of under and oversampling techniques to get the balanced dataset with an equal number of polarity classes. Train-Test split: After sampling and balancing the dataset, we use 80% of data for training while keeping 10 % for validating the training and 10% for testing the model. Training and Validation: We use the "bert-base-cased" pre-trained model as most of our data is in English. We run six epochs and a different number of batches for training the model with each dataset. The batch number varies as the higher number for batch size will cause GPU memory constraints. To overcome this limitation, we keep batch size around 6, 8 or 10 depending on the training data size.
We use validation data to analyse the training accuracy and loss. Testing: After training is complete, we run the test data on the trained model to check how well the model performs. The minimum difference between validation and test accuracy reflects the overall accuracy of our model. The results of each dataset are discussed in Section 5. Performance Metrics: We will use common performance metrics for evaluating our performing model. It includes accuracy, confusion matrix, F1 score, Precision and Recall. As usual, Accuracy is the fraction of correct predictions, the number of hits divided by total number of predictions. The confusion matrix, also known as error matrix, assesses the classification accuracy by calculating the confusion matrix with each row corresponding to the true class. It is displayed in table layout that allows visualization of the efficacy of a classification algorithm.
There are three types of averages namely micro, macro and weighted. Micro average evaluate metrics globally by computing the total true positives, false negatives and false positives. Macro average tally metrics for each label, and find their unweighted mean. The imbalanced labels are not taken into account. Weighted average determines metrics for each label. It find their average weighted by support. This changes macro average to reckon unbalanced label which can lead to F-score that is distinct from precision and recall. Support is the number of actual occurrences of the class in the specified dataset. Unbalanced support in the training data may indicate structural weaknesses in the scores of the reported classifiers and could indicate the need for stratified sampling or re-balancing. The support does not variate between the models, but rather diagnoses the evaluation process.