prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What learning models are used on the dataset?
Context 1: Introduction
Abusive language refers to any type of insult, vulgarity, or profanity that debases the target; it also can be anything that causes aggravation BIBREF0 , BIBREF1   Abusive language is often reframed as, but not limited to, offensive language BIBREF2 , cyberbullying BIBREF3 , othering language BIBREF4 , and hate speech BIBREF5  
Recently, an increasing number of users have been subjected to harassment, or have witnessed offensive behaviors online BIBREF6   Major social media companies (i e  Facebook, Twitter) have utilized multiple resources—artificial intelligence, human reviewers, user reporting processes, etc —in effort to censor offensive language, yet it seems nearly impossible to successfully resolve the issue BIBREF7 , BIBREF8  
The major reason of the failure in abusive language detection comes from its subjectivity and context-dependent characteristics BIBREF9   For instance, a message can be regarded as harmless on its own, but when taking previous threads into account it may be seen as abusive, and vice versa  This aspect makes detecting abusive language extremely laborious even for human annotators; therefore it is difficult to build a large and reliable dataset BIBREF10  
Previously, datasets openly available in abusive language detection research on Twitter ranged from 10K to 35K in size BIBREF9 , BIBREF11   This quantity is not sufficient to train the significant number of parameters in deep learning models  Due to this reason, these datasets have been mainly studied by traditional machine learning methods  Most recently, Founta et al  founta2018large introduced Hate and Abusive Speech on Twitter, a dataset containing 100K tweets with cross-validated labels  Although this corpus has great potential in training deep models with its significant size, there are no baseline reports to date 
This paper investigates the efficacy of different learning models in detecting abusive language  We compare accuracy using the most frequently studied machine learning classifiers as well as recent neural network models  Reliable baseline results are presented with the first comparative study on this dataset  Additionally, we demonstrate the effect of different features and variants, and describe the possibility for further improvements with the use of ensemble models 
Related Work
The research community introduced various approaches on abusive language detection  Razavi et al  razavi2010offensive applied Naïve Bayes, and Warner and Hirschberg warner2012detecting used Support Vector Machine (SVM), both with word-level features to classify offensive language  Xiang et al  xiang2012detecting generated topic distributions with Latent Dirichlet Allocation BIBREF12 , also using word-level features in order to classify offensive tweets 
More recently, distributed word representations and neural network models have been widely applied for abusive language detection  Djuric et al  djuric2015hate used the Continuous Bag Of Words model with paragraph2vec algorithm BIBREF13 to more accurately detect hate speech than that of the plain Bag Of Words models  Badjatiya et al  badjatiya2017deep implemented Gradient Boosted Decision Trees classifiers using word representations trained by deep learning models  Other researchers have investigated character-level representations and their effectiveness compared to word-level representations BIBREF14 , BIBREF15  
As traditional machine learning methods have relied on feature engineering, (i e  n-grams, POS tags, user information) BIBREF1 , researchers have proposed neural-based models with the advent of larger datasets  Convolutional Neural Networks and Recurrent Neural Networks have been applied to detect abusive language, and they have outperformed traditional machine learning classifiers such as Logistic Regression and SVM BIBREF15 , BIBREF16   However, there are no studies investigating the efficiency of neural models with large-scale datasets over 100K 
Methodology
This section illustrates our implementations on traditional machine learning classifiers and neural network based models in detail  Furthermore, we describe additional features and variant models investigated 
Traditional Machine Learning Models
We implement five feature engineering based machine learning classifiers that are most often used for abusive language detection  In data preprocessing, text sequences are converted into Bag Of Words (BOW) representations, and normalized with Term Frequency-Inverse Document Frequency (TF-IDF) values  We experiment with word-level features using n-grams ranging from 1 to 3, and character-level features from 3 to 8-grams  Each classifier is implemented with the following specifications:
Naïve Bayes (NB): Multinomial NB with additive smoothing constant 1
Logistic Regression (LR): Linear LR with L2 regularization constant 1 and limited-memory BFGS optimization
Support Vector Machine (SVM): Linear SVM with L2 regularization constant 1 and logistic loss function
Random Forests (RF): Averaging probabilistic predictions of 10 randomized decision trees
Gradient Boosted Trees (GBT): Tree boosting with learning rate 1 and logistic loss function
Neural Network based Models
Along with traditional machine learning approaches, we investigate neural network based models to evaluate their efficacy within a larger dataset  In particular, we explore Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variant models  A pre-trained GloVe BIBREF17 representation is used for word-level features 
CNN: We adopt Kim's kim2014convolutional implementation as the baseline  The word-level CNN models have 3 convolutional filters of different sizes [1,2,3] with ReLU activation, and a max-pooling layer  For the character-level CNN, we use 6 convolutional filters of various sizes [3,4,5,6,7,8], then add max-pooling layers followed by 1 fully-connected layer with a dimension of 1024 
Park and Fung park2017one proposed a HybridCNN model which outperformed both word-level and character-level CNNs in abusive language detection  In order to evaluate the HybridCNN for this dataset, we concatenate the output of max-pooled layers from word-level and character-level CNN, and feed this vector to a fully-connected layer in order to predict the output 
All three CNN models (word-level, character-level, and hybrid) use cross entropy with softmax as their loss function and Adam BIBREF18 as the optimizer 
RNN: We use bidirectional RNN BIBREF19 as the baseline, implementing a GRU BIBREF20 cell for each recurrent unit  From extensive parameter-search experiments, we chose 1 encoding layer with 50 dimensional hidden states and an input dropout probability of 0 3  The RNN models use cross entropy with sigmoid as their loss function and Adam as the optimizer 
For a possible improvement, we apply a self-matching attention mechanism on RNN baseline models BIBREF21 so that they may better understand the data by retrieving text sequences twice  We also investigate a recently introduced method, Latent Topic Clustering (LTC) BIBREF22   The LTC method extracts latent topic information from the hidden states of RNN, and uses it for additional information in classifying the text data 
Feature Extension
While manually analyzing the raw dataset, we noticed that looking at the tweet one has replied to or has quoted, provides significant contextual information  We call these, “context tweets"  As humans can better understand a tweet with the reference of its context, our assumption is that computers also benefit from taking context tweets into account in detecting abusive language 
As shown in the examples below, (2) is labeled abusive due to the use of vulgar language  However, the intention of the user can be better understood with its context tweet (1) 
(1) I hate when I'm sitting in front of the bus and somebody with a wheelchair get on 
INLINEFORM0 (2) I hate it when I'm trying to board a bus and there's already an as**ole on it 
Similarly, context tweet (3) is important in understanding the abusive tweet (4), especially in identifying the target of the malice 
(3) Survivors of #Syria Gas Attack Recount `a Cruel Scene' 
INLINEFORM0 (4) Who the HELL is “LIKE" ING this post? Sick people    
Huang et al  huang2016modeling used several attributes of context tweets for sentiment analysis in order to improve the baseline LSTM model  However, their approach was limited because the meta-information they focused on—author information, conversation type, use of the same hashtags or emojis—are all highly dependent on data 
In order to avoid data dependency, text sequences of context tweets are directly used as an additional feature of neural network models  We use the same baseline model to convert context tweets to vectors, then concatenate these vectors with outputs of their corresponding labeled tweets  More specifically, we concatenate max-pooled layers of context and labeled tweets for the CNN baseline model  As for RNN, the last hidden states of context and labeled tweets are concatenated 
Dataset
Hate and Abusive Speech on Twitter BIBREF10 classifies tweets into 4 labels, “normal", “spam", “hateful" and “abusive"  We were only able to crawl 70,904 tweets out of 99,996 tweet IDs, mainly because the tweet was deleted or the user account had been suspended  Table shows the distribution of labels of the crawled data 
Data Preprocessing
In the data preprocessing steps, user IDs, URLs, and frequently used emojis are replaced as special tokens  Since hashtags tend to have a high correlation with the content of the tweet BIBREF23 , we use a segmentation library BIBREF24 for hashtags to extract more information 
For character-level representations, we apply the method Zhang et al  zhang2015character proposed  Tweets are transformed into one-hot encoded vectors using 70 character dimensions—26 lower-cased alphabets, 10 digits, and 34 special characters including whitespace 
Training and Evaluation
In training the feature engineering based machine learning classifiers, we truncate vector representations according to the TF-IDF values (the top 14,000 and 53,000 for word-level and character-level representations, respectively) to avoid overfitting  For neural network models, words that appear only once are replaced as unknown tokens 
Since the dataset used is not split into train, development, and test sets, we perform 10-fold cross validation, obtaining the average of 5 tries; we divide the dataset randomly by a ratio of 85:5:10, respectively  In order to evaluate the overall performance, we calculate the weighted average of precision, recall, and F1 scores of all four labels, “normal”, “spam”, “hateful”, and “abusive” 
Empirical Results
As shown in Table , neural network models are more accurate than feature engineering based models (i e  NB, SVM, etc ) except for the LR model—the best LR model has the same F1 score as the best CNN model 
Among traditional machine learning models, the most accurate in classifying abusive language is the LR model followed by ensemble models such as GBT and RF  Character-level representations improve F1 scores of SVM and RF classifiers, but they have no positive effect on other models 
For neural network models, RNN with LTC modules have the highest accuracy score, but there are no significant improvements from its baseline model and its attention-added model  Similarly, HybridCNN does not improve the baseline CNN model  For both CNN and RNN models, character-level features significantly decrease the accuracy of classification 
The use of context tweets generally have little effect on baseline models, however they noticeably improve the scores of several metrics  For instance, CNN with context tweets score the highest recall and F1 for “hateful" labels, and RNN models with context tweets have the highest recall for “abusive" tweets 
Discussion and Conclusion
While character-level features are known to improve the accuracy of neural network models BIBREF16 , they reduce classification accuracy for Hate and Abusive Speech on Twitter  We conclude this is because of the lack of labeled data as well as the significant imbalance among the different labels  Unlike neural network models, character-level features in traditional machine learning classifiers have positive results because we have trained the models only with the most significant character elements using TF-IDF values 
Variants of neural network models also suffer from data insufficiency  However, these models show positive performances on “spam" (14%) and “hateful" (4%) tweets—the lower distributed labels  The highest F1 score for “spam" is from the RNN-LTC model (0 551), and the highest for “hateful" is CNN with context tweets (0 309)  Since each variant model excels in different metrics, we expect to see additional improvements with the use of ensemble models of these variants in future works 
In this paper, we report the baseline accuracy of different learning models as well as their variants on the recently introduced dataset, Hate and Abusive Speech on Twitter  Experimental results show that bidirectional GRU networks with LTC provide the most accurate results in detecting abusive language  Additionally, we present the possibility of using ensemble models of variant models and features for further improvements 
Acknowledgments
K  Jung is with the Department of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul, Korea  This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (No  2016M3C4A7952632), the Technology Innovation Program (10073144) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) 
We would also like to thank Yongkeun Hwang and Ji Ho Park for helpful discussions and their valuable insights 
Context 2: Introduction
Performance appraisal (PA) is an important HR process, particularly for modern organizations that crucially depend on the skills and expertise of their workforce  The PA process enables an organization to periodically measure and evaluate every employee's performance  It also provides a mechanism to link the goals established by the organization to its each employee's day-to-day activities and performance  Design and analysis of PA processes is a lively area of research within the HR community BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3  
The PA process in any modern organization is nowadays implemented and tracked through an IT system (the PA system) that records the interactions that happen in various steps  Availability of this data in a computer-readable database opens up opportunities to analyze it using automated statistical, data-mining and text-mining techniques, to generate novel and actionable insights / patterns and to help in improving the quality and effectiveness of the PA process BIBREF4 , BIBREF5 , BIBREF6   Automated analysis of large-scale PA data is now facilitated by technological and algorithmic advances, and is becoming essential for large organizations containing thousands of geographically distributed employees handling a wide variety of roles and tasks 
A typical PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers  In most PA processes, the communication includes the following steps: (i) in self-appraisal, an employee records his/her achievements, activities, tasks handled etc ; (ii) in supervisor assessment, the supervisor provides the criticism, evaluation and suggestions for improvement of performance etc ; and (iii) in peer feedback (aka INLINEFORM0 view), the peers of the employee provide their feedback  There are several business questions that managers are interested in  Examples:
In this paper, we develop text mining techniques that can automatically produce answers to these questions  Since the intended users are HR executives, ideally, the techniques should work with minimum training data and experimentation with parameter setting  These techniques have been implemented and are being used in a PA system in a large multi-national IT company 
The rest of the paper is organized as follows  Section SECREF2 summarizes related work  Section SECREF3 summarizes the PA dataset used in this paper  Section SECREF4 applies sentence classification algorithms to automatically discover three important classes of sentences in the PA corpus viz , sentences that discuss strengths, weaknesses of employees and contain suggestions for improving her performance  Section SECREF5 considers the problem of mapping the actual targets mentioned in strengths, weaknesses and suggestions to a fixed set of attributes  In Section SECREF6 , we discuss how the feedback from peers for a particular employee can be summarized  In Section SECREF7 we draw conclusions and identify some further work 
Related Work
We first review some work related to sentence classification  Semantically classifying sentences (based on the sentence's purpose) is a much harder task, and is gaining increasing attention from linguists and NLP researchers  McKnight and Srinivasan BIBREF7 and Yamamoto and Takagi BIBREF8 used SVM to classify sentences in biomedical abstracts into classes such as INTRODUCTION, BACKGROUND, PURPOSE, METHOD, RESULT, CONCLUSION  Cohen et al  BIBREF9 applied SVM and other techniques to learn classifiers for sentences in emails into classes, which are speech acts defined by a verb-noun pair, with verbs such as request, propose, amend, commit, deliver and nouns such as meeting, document, committee; see also BIBREF10   Khoo et al  BIBREF11 uses various classifiers to classify sentences in emails into classes such as APOLOGY, INSTRUCTION, QUESTION, REQUEST, SALUTATION, STATEMENT, SUGGESTION, THANKING etc  Qadir and Riloff BIBREF12 proposes several filters and classifiers to classify sentences on message boards (community QA systems) into 4 speech acts: COMMISSIVE (speaker commits to a future action), DIRECTIVE (speaker expects listener to take some action), EXPRESSIVE (speaker expresses his or her psychological state to the listener), REPRESENTATIVE (represents the speaker's belief of something)  Hachey and Grover BIBREF13 used SVM and maximum entropy classifiers to classify sentences in legal documents into classes such as FACT, PROCEEDINGS, BACKGROUND, FRAMING, DISPOSAL; see also BIBREF14   Deshpande et al  BIBREF15 proposes unsupervised linguistic patterns to classify sentences into classes SUGGESTION, COMPLAINT 
There is much work on a closely related problem viz , classifying sentences in dialogues through dialogue-specific categories called dialogue acts BIBREF16 , which we will not review here  Just as one example, Cotterill BIBREF17 classifies questions in emails into the dialogue acts of YES_NO_QUESTION, WH_QUESTION, ACTION_REQUEST, RHETORICAL, MULTIPLE_CHOICE etc 
We could not find much work related to mining of performance appraisals data  Pawar et al  BIBREF18 uses kernel-based classification to classify sentences in both performance appraisal text and product reviews into classes SUGGESTION, APPRECIATION, COMPLAINT  Apte et al  BIBREF6 provides two algorithms for matching the descriptions of goals or tasks assigned to employees to a standard template of model goals  One algorithm is based on the co-training framework and uses goal descriptions and self-appraisal comments as two separate perspectives  The second approach uses semantic similarity under a weak supervision framework  Ramrakhiyani et al  BIBREF5 proposes label propagation algorithms to discover aspects in supervisor assessments in performance appraisals, where an aspect is modelled as a verb-noun pair (e g  conduct training, improve coding) 
Dataset
In this paper, we used the supervisor assessment and peer feedback text produced during the performance appraisal of 4528 employees in a large multi-national IT company  The corpus of supervisor assessment has 26972 sentences  The summary statistics about the number of words in a sentence is: min:4 max:217 average:15 5 STDEV:9 2 Q1:9 Q2:14 Q3:19 
Sentence Classification
The PA corpus contains several classes of sentences that are of interest  In this paper, we focus on three important classes of sentences viz , sentences that discuss strengths (class STRENGTH), weaknesses of employees (class WEAKNESS) and suggestions for improving her performance (class SUGGESTION)  The strengths or weaknesses are mostly about the performance in work carried out, but sometimes they can be about the working style or other personal qualities  The classes WEAKNESS and SUGGESTION are somewhat overlapping; e g , a suggestion may address a perceived weakness  Following are two example sentences in each class 
STRENGTH:
WEAKNESS:
SUGGESTION:
Several linguistic aspects of these classes of sentences are apparent  The subject is implicit in many sentences  The strengths are often mentioned as either noun phrases (NP) with positive adjectives (Excellent technology leadership) or positive nouns (engineering strength) or through verbs with positive polarity (dedicated) or as verb phrases containing positive adjectives (delivers innovative solutions)  Similarly for weaknesses, where negation is more frequently used (presentations are not his forte), or alternatively, the polarities of verbs (avoid) or adjectives (poor) tend to be negative  However, sometimes the form of both the strengths and weaknesses is the same, typically a stand-alone sentiment-neutral NP, making it difficult to distinguish between them; e g , adherence to timing or timely closure  Suggestions often have an imperative mood and contain secondary verbs such as need to, should, has to  Suggestions are sometimes expressed using comparatives (better process compliance)  We built a simple set of patterns for each of the 3 classes on the POS-tagged form of the sentences  We use each set of these patterns as an unsupervised sentence classifier for that class  If a particular sentence matched with patterns for multiple classes, then we have simple tie-breaking rules for picking the final class  The pattern for the STRENGTH class looks for the presence of positive words / phrases like takes ownership, excellent, hard working, commitment, etc  Similarly, the pattern for the WEAKNESS class looks for the presence of negative words / phrases like lacking, diffident, slow learner, less focused, etc  The SUGGESTION pattern not only looks for keywords like should, needs to but also for POS based pattern like “a verb in the base form (VB) in the beginning of a sentence” 
We randomly selected 2000 sentences from the supervisor assessment corpus and manually tagged them (dataset D1)  This labelled dataset contained 705, 103, 822 and 370 sentences having the class labels STRENGTH, WEAKNESS, SUGGESTION or OTHER respectively  We trained several multi-class classifiers on this dataset  Table TABREF10 shows the results of 5-fold cross-validation experiments on dataset D1  For the first 5 classifiers, we used their implementation from the SciKit Learn library in Python (scikit-learn org)  The features used for these classifiers were simply the sentence words along with their frequencies  For the last 2 classifiers (in Table TABREF10 ), we used our own implementation  The overall accuracy for a classifier is defined as INLINEFORM0 , where the denominator is 2000 for dataset D1  Note that the pattern-based approach is unsupervised i e , it did not use any training data  Hence, the results shown for it are for the entire dataset and not based on cross-validation 
Comparison with Sentiment Analyzer
We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS  We used an implementation of sentiment analyzer from TextBlob to get a polarity score for each sentence  Table TABREF13 shows the distribution of positive, negative and neutral sentiments across the 3 class labels STRENGTH, WEAKNESS and SUGGESTION  It can be observed that distribution of positive and negative sentiments is almost similar in STRENGTH as well as SUGGESTION sentences, hence we can conclude that the information about sentiments is not much useful for our classification problem 
Discovering Clusters within Sentence Classes
After identifying sentences in each class, we can now answer question (1) in Section SECREF1   From 12742 sentences predicted to have label STRENGTH, we extract nouns that indicate the actual strength, and cluster them using a simple clustering algorithm which uses the cosine similarity between word embeddings of these nouns  We repeat this for the 9160 sentences with predicted label WEAKNESS or SUGGESTION as a single class  Tables TABREF15 and TABREF16 show a few representative clusters in strengths and in weaknesses, respectively  We also explored clustering 12742 STRENGTH sentences directly using CLUTO BIBREF19 and Carrot2 Lingo BIBREF20 clustering algorithms  Carrot2 Lingo discovered 167 clusters and also assigned labels to these clusters  We then generated 167 clusters using CLUTO as well  CLUTO does not generate cluster labels automatically, hence we used 5 most frequent words within the cluster as its labels  Table TABREF19 shows the largest 5 clusters by both the algorithms  It was observed that the clusters created by CLUTO were more meaningful and informative as compared to those by Carrot2 Lingo  Also, it was observed that there is some correspondence between noun clusters and sentence clusters  E g  the nouns cluster motivation expertise knowledge talent skill (Table TABREF15 ) corresponds to the CLUTO sentence cluster skill customer management knowledge team (Table TABREF19 )  But overall, users found the nouns clusters to be more meaningful than the sentence clusters 
PA along Attributes
In many organizations, PA is done from a predefined set of perspectives, which we call attributes  Each attribute covers one specific aspect of the work done by the employees  This has the advantage that we can easily compare the performance of any two employees (or groups of employees) along any given attribute  We can correlate various performance attributes and find dependencies among them  We can also cluster employees in the workforce using their supervisor ratings for each attribute to discover interesting insights into the workforce  The HR managers in the organization considered in this paper have defined 15 attributes (Table TABREF20 )  Each attribute is essentially a work item or work category described at an abstract level  For example, FUNCTIONAL_EXCELLENCE covers any tasks, goals or activities related to the software engineering life-cycle (e g , requirements analysis, design, coding, testing etc ) as well as technologies such as databases, web services and GUI 
In the example in Section SECREF4 , the first sentence (which has class STRENGTH) can be mapped to two attributes: FUNCTIONAL_EXCELLENCE and BUILDING_EFFECTIVE_TEAMS  Similarly, the third sentence (which has class WEAKNESS) can be mapped to the attribute INTERPERSONAL_EFFECTIVENESS and so forth  Thus, in order to answer the second question in Section SECREF1 , we need to map each sentence in each of the 3 classes to zero, one, two or more attributes, which is a multi-class multi-label classification problem 
We manually tagged the same 2000 sentences in Dataset D1 with attributes, where each sentence may get 0, 1, 2, etc  up to 15 class labels (this is dataset D2)  This labelled dataset contained 749, 206, 289, 207, 91, 223, 191, 144, 103, 80, 82, 42, 29, 15, 24 sentences having the class labels listed in Table TABREF20 in the same order  The number of sentences having 0, 1, 2, or more than 2 attributes are: 321, 1070, 470 and 139 respectively  We trained several multi-class multi-label classifiers on this dataset  Table TABREF21 shows the results of 5-fold cross-validation experiments on dataset D2 
Precision, Recall and F-measure for this multi-label classification are computed using a strategy similar to the one described in BIBREF21   Let INLINEFORM0 be the set of predicted labels and INLINEFORM1 be the set of actual labels for the INLINEFORM2 instance  Precision and recall for this instance are computed as follows: INLINEFORM3
It can be observed that INLINEFORM0 would be undefined if INLINEFORM1 is empty and similarly INLINEFORM2 would be undefined when INLINEFORM3 is empty  Hence, overall precision and recall are computed by averaging over all the instances except where they are undefined  Instance-level F-measure can not be computed for instances where either precision or recall are undefined  Therefore, overall F-measure is computed using the overall precision and recall 
Summarization of Peer Feedback using ILP
The PA system includes a set of peer feedback comments for each employee  To answer the third question in Section SECREF1 , we need to create a summary of all the peer feedback comments about a given employee  As an example, following are the feedback comments from 5 peers of an employee 
The individual sentences in the comments written by each peer are first identified and then POS tags are assigned to each sentence  We hypothesize that a good summary of these multiple comments can be constructed by identifying a set of important text fragments or phrases  Initially, a set of candidate phrases is extracted from these comments and a subset of these candidate phrases is chosen as the final summary, using Integer Linear Programming (ILP)  The details of the ILP formulation are shown in Table TABREF36   As an example, following is the summary generated for the above 5 peer comments 
humble nature, effective communication, technical expertise, always supportive, vast knowledge
Following rules are used to identify candidate phrases:
Various parameters are used to evaluate a candidate phrase for its importance  A candidate phrase is more important:
A complete list of parameters is described in detail in Table TABREF36  
There is a trivial constraint INLINEFORM0 which makes sure that only INLINEFORM1 out of INLINEFORM2 candidate phrases are chosen  A suitable value of INLINEFORM3 is used for each employee depending on number of candidate phrases identified across all peers (see Algorithm SECREF6 )  Another set of constraints ( INLINEFORM4 to INLINEFORM5 ) make sure that at least one phrase is selected for each of the leadership attributes  The constraint INLINEFORM6 makes sure that multiple phrases sharing the same headword are not chosen at a time  Also, single word candidate phrases are chosen only if they are adjectives or nouns with lexical category noun attribute  This is imposed by the constraint INLINEFORM7   It is important to note that all the constraints except INLINEFORM8 are soft constraints, i e  there may be feasible solutions which do not satisfy some of these constraints  But each constraint which is not satisfied, results in a penalty through the use of slack variables  These constraints are described in detail in Table TABREF36  
The objective function maximizes the total importance score of the selected candidate phrases  At the same time, it also minimizes the sum of all slack variables so that the minimum number of constraints are broken 
INLINEFORM0 : No  of candidate phrases INLINEFORM1 : No  of phrases to select as part of summary
INLINEFORM0 INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 INLINEFORM5 INLINEFORM6 INLINEFORM7 INLINEFORM8
INLINEFORM0 and INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 INLINEFORM5 INLINEFORM6
INLINEFORM0 (For determining number of phrases to select to include in summary)
Evaluation of auto-generated summaries
We considered a dataset of 100 employees, where for each employee multiple peer comments were recorded  Also, for each employee, a manual summary was generated by an HR personnel  The summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE BIBREF22 unigram score  For comparing performance of our ILP-based summarization algorithm, we explored a few summarization algorithms provided by the Sumy package  A common parameter which is required by all these algorithms is number of sentences keep in the final summary  ILP-based summarization requires a similar parameter K, which is automatically decided based on number of total candidate phrases  Assuming a sentence is equivalent to roughly 3 phrases, for Sumy algorithms, we set number of sentences parameter to the ceiling of K/3  Table TABREF51 shows average and standard deviation of ROUGE unigram f1 scores for each algorithm, over the 100 summaries  The performance of ILP-based summarization is comparable with the other algorithms, as the two sample t-test does not show statistically significant difference  Also, human evaluators preferred phrase-based summary generated by our approach to the other sentence-based summaries 
Conclusions and Further Work
In this paper, we presented an analysis of the text generated in Performance Appraisal (PA) process in a large multi-national IT company  We performed sentence classification to identify strengths, weaknesses and suggestions for improvements found in the supervisor assessments and then used clustering to discover broad categories among them  As this is non-topical classification, we found that SVM with ADWS kernel BIBREF18 produced the best results  We also used multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance  Logistic Regression classifier was observed to produce the best results for this topical classification  Finally, we proposed an ILP-based summarization technique to produce a summary of peer feedback comments for a given employee and compared it with manual summaries 
The PA process also generates much structured data, such as supervisor ratings  It is an interesting problem to compare and combine the insights from discovered from structured data and unstructured text  Also, we are planning to automatically discover any additional performance attributes to the list of 15 attributes currently used by HR 
Context 3: Introduction
Recently, people have started looking at online forums either as a primary or secondary source of counseling services BIBREF0  BIBREF1 reported that over the first five years of operation (2011-2016), ReachOut com – Ireland's online youth mental health service – 62% of young people would visit a website for support when going through a tough time  With the expansion of the Internet, there has been a substantial growth in the number of users looking for psychological support online 
The importance of the on-line life of patients has been recognized in research as well  BIBREF2 stated that the online life of patients constitutes a major influence on their self-definition  Furthermore, according to BIBREF3, the social networking activities of an individual, offer an important reflection of their personality  While dealing with patients suffering from psychological problems, it is important that therapists do not ignore this pivotal source of information which can provide deep insights into their patients' mental conditions 
Acceptance of on-line support groups (OSG) by Mental Health Professionals is still not established BIBREF4  Since OSG can have double-edged effects on patients and the presence of professionals is often limited, we argue that their properties should be further studied  According to BIBREF5 OSG effectiveness is hard to assess, while some studies showed OSG's potential to change participants' attitudes, no such effect was observed in other studies (see Related Work Section for more details)  Furthermore the scope of previous work on analysis of users' behaviour in OSG has been limited by the fact that they relied on expert annotation of posts and comments BIBREF6 
We present a novel approach for automatically analysing online conversations for the presence of therapeutic factors of group therapy defined by BIBREF7 as “the actual mechanisms of effecting change in the patient”  The authors have identified 11 therapeutic factors in group therapy: Universality, Altruism, Instillation of Hope, Guidance, Imparting information, Developing social skills, Interpersonal learning, Cohesion, Catharsis, Existential factors, Imitative behavior and Corrective recapitulation of family of origin issues  In this paper, we focus on 3 therapeutic factors: Universality, Altruism and Instillation of Hope (listed below), as we believe that these can be approximated by using established NLP techniques (e g  Sentiment Analysis, Dialogue Act tagging etc ) 
Universality: the disconfirmation of a user's feelings of uniqueness of their mental health condition 
Altruism: others offer support, reassurance, suggestions and insight 
Instillation of Hope: inspiration provided to participants by their peers 
The selected therapeutic factors are analysed in terms of illocutionary force and attitude  Due to the multi-party and asynchronous nature of on-line social media conversations, prior to the analysis, we extract conversation threads among users – an essential prerequisite for any kind of higher-level dialogue analysis BIBREF10  Afterwards, the illocutionary force is identified using Dialogue Act tagging, whereas the attitude by using Sentiment Analysis  The quantitative analysis is then performed on these processed conversations 
Ideally, the analysis would require experts to annotate each post and comment on the presence of therapeutic factors  However, due to time and cost demands of this task, it is feasible to analyse only a small fraction of the available data  Compared to previous studies (e g  BIBREF6) that analysed few tens of conversations and several thousand lines of chat; using the proposed approach – application of Dialogue Acts and Sentiment Analysis – we were able to automatically analyse approximately 300 thousands conversations (roughly 1 5 million comments)  The rest of the paper is structured as follows  In Section 2 we introduce related work  Next, in Section 3 we describe the pre-processing pipeline and the methodology to perform thread extraction on asynchronous multi-party conversations  In Section 4 we provide the describe the final dataset used for the analysis, and in Section 5 we present the results of our analysis  Finally, in Section 6 we provide concluding remarks and future research directions 
Related Work
On-line support groups have been analyzed for various factors before  For instance, BIBREF11 analysed stress reduction in on-line support group chat-rooms, and the effects of on-line social interactions  Such studies mostly relied on questionnaires and were based on a small number of users  Nevertheless, in BIBREF11, the author showed that social support facilitates coping with distress, improves mood and expedites recovery from it  These findings highlight that, overall, on-line discussion boards appear to be therapeutic and constructive for individuals suffering alcohol-abuse 
Application of NLP to the analysis of mental health-related conversation has been studied as well (e g  BIBREF12, BIBREF13)  BIBREF6 applied sentiment-analysis combined with extensive turn-level annotation to investigate stress reduction in on-line support group chat-rooms, showing that sentiment-analysis is a good predictor of entrance stress level  Furthermore, similar to our setting, they applied automatic thread-extraction to determine conversation threads 
BIBREF14 have shown that on-line support group therapy increased the quality of life of patients with metastatic breast cancer  Since many original posters reported the benefits of group therapy on patients BIBREF15, BIBREF2, BIBREF16, BIBREF17, BIBREF18, BIBREF7, we evaluate the effect of the user interaction using sentiment scores of comments in on-line support groups 
According to BIBREF6, users with high incoming stress tend to request less information from others, as a percentage of their time, and share much more information, in absolute terms  In addition, high information sharing has been shown to be a good predictor of stress reduction at the end of the chat BIBREF6  Regarding information sharing, we rely on Dialogue Acts BIBREF19 to model the speaker's intention in producing an utterance  In particular, we are interested in Dialogue Act label that is defined to represent descriptive, narrative, or personal information – the statement 
Dialogue Acts have been applied to the analysis of spoken BIBREF20, BIBREF21 as well as on-line written synchronous conversations BIBREF22  We apply Dialogue Act tag set defined in BIBREF22 to the analysis of our on-line asynchronous conversations  We argue that Dialogue Acts can be used to analyse user behaviour in social media and verify the presence of therapeutic factors 
Methodology
We select the three therapeutic factors – Universality, Altruism and Instillation of Hope – that can be best approximated using NLP techniques: Sentiment Analysis and Dialogue Act tagging  We discuss each one of the selected therapeutic factors and the identified necessary conditions  The listed conditions, however, are not sufficient to attribute the presence of a therapeutic factor with high confidence, which only can be obtained using expert annotation  Our analysis focuses on the structure of conversations; though content plays an important role as well 
Universality consists in the disconfirmation of patients' belief of uniqueness of their disease  This therapeutic factor is shown to be a powerful source of relief for the patient, according to BIBREF7  From this definition, we can draw the following conditions that are applicable to our environment:
improvement of original poster's sentiment: we hypothesize that the discovery that other people passed through similar issues leads to a higher sentiment score;
posts containing negative personal experiences: to disconfirm the belief of uniqueness users have to share their story;
comments containing negative statements: to disconfirm the patient's feelings of uniqueness, the commenting user must tell a similar negative personal experience  This condition requires two sub-conditions: high presence of statements in comments and the presence of negative comments replying to negative posts 
Instillation of Hope is based on inspiration provided to participants by their peers  Through the inspiration provided by their peers, patients can increase their expectation on the therapy outcome  BIBREF7 in several studies have demonstrated that a high expectation of help before the start of a therapy is significantly correlated with a positive therapy outcome  The author states that many patients pointed out the importance of having observed the improvement of others  Therefore, the three main conditions are the following:
improvement of original poster's sentiment: we hypothesize that instillation of hope leads to a higher sentiment score;
posts containing negative personal experiences: hope can be instilled in someone who shares a negative personal experience;
comments containing positive personal experiences: in order to instill hope, commenting posters must show to original posters an overall positive personal experience  To detect positive personal experience, we require the presence of statements in comments and a positive sentiment of comments replying to negative posts 
Altruism consists of peers offering support, reassurance, suggestions and insight, since they share similar problems with one another BIBREF7  The experience of finding that a patient can be of value to others is refreshing and boosts self-esteem BIBREF7  However, in the current study we focus on testing whether commenting posters are altruists or not  We do not test whether the altruistic behavior leads to an improvement on the altruist itself  For these reasons, we define three main conditions:
improvement of original poster's sentiment: we hypothesize that supportive and reassuring statements improve the sentiment score of the original poster;
posts contains negative personal experiences: users offer support, reassurance and suggestion when facing a negative personal experience of the original poster;
comments containing positive statements: either supportive or reassuring statements show by definition a positive intended emotional communication  Thus comments to the post should consist of positive sentiment statements 
Consequently, a conversation containing the aforementioned therapeutic factors should satisfy the following conditions in terms of NLP: Sentiment Analysis and Dialogue Acts 
original posters have a higher sentiment score at the end of the thread than at the beginning;
the original post consists mostly of polarised statements;
the presence of a significant amount of statements in comments, since both support and sharing similar negative experiences can be represented as statements;
both negative and positive statements in comments lead to higher final sentiment score of the original poster 
Datasets
We verify the presence of therapeutic factors in two social media datasets: OSG and Twitter  The first dataset is crawled from an on-line support groups website, and the second dataset consists of a small sample of Twitter conversation threads  Since the former consists of multi-threaded conversations, we apply a pre-processing to extract conversation threads to provide a fair comparison with the Twitter dataset  An example conversation from each data source is presented in Figure FIGREF19 
Datasets ::: Twitter
We have downloaded 1,873 Twitter conversation threads, roughly 14k tweets, from a publicly available resource that were previously pre-processed and have conversation threads extracted  A conversation in the dataset consists of at least 4 tweets  Even though, according to BIBREF23, Twitter is broadly applicable to public health research, our expectation is that it contains less therapeutic conversations in comparison to specialized on-line support forums 
Datasets ::: OSG
Our data has been developed by crawling and pre-processing an OSG web forum  The forum has a great variety of different groups such as depression, anxiety, stress, relationship, cancer, sexually transmitted diseases, etc  Each conversation starts with one post and can contain multiple comments  Each post or comment is represented by a poster, a timestamp, a list of users it is referencing to, thread id, a comment id and a conversation id  The thread id is the same for comments replying to each other, otherwise it is different  The thread id is increasing with time  Thus, it provides ordering among threads; whereas the timestamp provides ordering in the thread 
Each conversation can belong to multiple groups  Consequently, the dataset needs to be processed to remove duplicates  The dataset resulting after de-duplication contains 295 thousand conversations, each conversation contains on average 6 comments  In total, there are 1 5 million comments  Since the created dataset is multi-threaded, we need to extract conversation threads, to eliminate paths not relevant to the original post 
Datasets ::: OSG ::: Conversation Thread Extraction
The thread extraction algorithm is heuristic-based and consists of two steps: (1) creation of a tree, based on a post written by a user and the related comments and (2) transformation of the tree into a list of threads 
The tree creation is an extension of the approach of BIBREF24, where first a graph of conversation is constructed  In the approach, direct replies to a post are attached to the first nesting level and subsequent comments to increasing nesting levels  In our approach, we also exploit comments' features

ground_truth: Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Random Forests (RF), Gradient Boosted Trees (GBT),  Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)

Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Random Forests (RF), Gradient Boosted Trees (GBT), CNN, RNN

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What summarization algorithms did the authors experiment with?
Context 4: Introduction
Performance appraisal (PA) is an important HR process, particularly for modern organizations that crucially depend on the skills and expertise of their workforce  The PA process enables an organization to periodically measure and evaluate every employee's performance  It also provides a mechanism to link the goals established by the organization to its each employee's day-to-day activities and performance  Design and analysis of PA processes is a lively area of research within the HR community BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3  
The PA process in any modern organization is nowadays implemented and tracked through an IT system (the PA system) that records the interactions that happen in various steps  Availability of this data in a computer-readable database opens up opportunities to analyze it using automated statistical, data-mining and text-mining techniques, to generate novel and actionable insights / patterns and to help in improving the quality and effectiveness of the PA process BIBREF4 , BIBREF5 , BIBREF6   Automated analysis of large-scale PA data is now facilitated by technological and algorithmic advances, and is becoming essential for large organizations containing thousands of geographically distributed employees handling a wide variety of roles and tasks 
A typical PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers  In most PA processes, the communication includes the following steps: (i) in self-appraisal, an employee records his/her achievements, activities, tasks handled etc ; (ii) in supervisor assessment, the supervisor provides the criticism, evaluation and suggestions for improvement of performance etc ; and (iii) in peer feedback (aka INLINEFORM0 view), the peers of the employee provide their feedback  There are several business questions that managers are interested in  Examples:
In this paper, we develop text mining techniques that can automatically produce answers to these questions  Since the intended users are HR executives, ideally, the techniques should work with minimum training data and experimentation with parameter setting  These techniques have been implemented and are being used in a PA system in a large multi-national IT company 
The rest of the paper is organized as follows  Section SECREF2 summarizes related work  Section SECREF3 summarizes the PA dataset used in this paper  Section SECREF4 applies sentence classification algorithms to automatically discover three important classes of sentences in the PA corpus viz , sentences that discuss strengths, weaknesses of employees and contain suggestions for improving her performance  Section SECREF5 considers the problem of mapping the actual targets mentioned in strengths, weaknesses and suggestions to a fixed set of attributes  In Section SECREF6 , we discuss how the feedback from peers for a particular employee can be summarized  In Section SECREF7 we draw conclusions and identify some further work 
Related Work
We first review some work related to sentence classification  Semantically classifying sentences (based on the sentence's purpose) is a much harder task, and is gaining increasing attention from linguists and NLP researchers  McKnight and Srinivasan BIBREF7 and Yamamoto and Takagi BIBREF8 used SVM to classify sentences in biomedical abstracts into classes such as INTRODUCTION, BACKGROUND, PURPOSE, METHOD, RESULT, CONCLUSION  Cohen et al  BIBREF9 applied SVM and other techniques to learn classifiers for sentences in emails into classes, which are speech acts defined by a verb-noun pair, with verbs such as request, propose, amend, commit, deliver and nouns such as meeting, document, committee; see also BIBREF10   Khoo et al  BIBREF11 uses various classifiers to classify sentences in emails into classes such as APOLOGY, INSTRUCTION, QUESTION, REQUEST, SALUTATION, STATEMENT, SUGGESTION, THANKING etc  Qadir and Riloff BIBREF12 proposes several filters and classifiers to classify sentences on message boards (community QA systems) into 4 speech acts: COMMISSIVE (speaker commits to a future action), DIRECTIVE (speaker expects listener to take some action), EXPRESSIVE (speaker expresses his or her psychological state to the listener), REPRESENTATIVE (represents the speaker's belief of something)  Hachey and Grover BIBREF13 used SVM and maximum entropy classifiers to classify sentences in legal documents into classes such as FACT, PROCEEDINGS, BACKGROUND, FRAMING, DISPOSAL; see also BIBREF14   Deshpande et al  BIBREF15 proposes unsupervised linguistic patterns to classify sentences into classes SUGGESTION, COMPLAINT 
There is much work on a closely related problem viz , classifying sentences in dialogues through dialogue-specific categories called dialogue acts BIBREF16 , which we will not review here  Just as one example, Cotterill BIBREF17 classifies questions in emails into the dialogue acts of YES_NO_QUESTION, WH_QUESTION, ACTION_REQUEST, RHETORICAL, MULTIPLE_CHOICE etc 
We could not find much work related to mining of performance appraisals data  Pawar et al  BIBREF18 uses kernel-based classification to classify sentences in both performance appraisal text and product reviews into classes SUGGESTION, APPRECIATION, COMPLAINT  Apte et al  BIBREF6 provides two algorithms for matching the descriptions of goals or tasks assigned to employees to a standard template of model goals  One algorithm is based on the co-training framework and uses goal descriptions and self-appraisal comments as two separate perspectives  The second approach uses semantic similarity under a weak supervision framework  Ramrakhiyani et al  BIBREF5 proposes label propagation algorithms to discover aspects in supervisor assessments in performance appraisals, where an aspect is modelled as a verb-noun pair (e g  conduct training, improve coding) 
Dataset
In this paper, we used the supervisor assessment and peer feedback text produced during the performance appraisal of 4528 employees in a large multi-national IT company  The corpus of supervisor assessment has 26972 sentences  The summary statistics about the number of words in a sentence is: min:4 max:217 average:15 5 STDEV:9 2 Q1:9 Q2:14 Q3:19 
Sentence Classification
The PA corpus contains several classes of sentences that are of interest  In this paper, we focus on three important classes of sentences viz , sentences that discuss strengths (class STRENGTH), weaknesses of employees (class WEAKNESS) and suggestions for improving her performance (class SUGGESTION)  The strengths or weaknesses are mostly about the performance in work carried out, but sometimes they can be about the working style or other personal qualities  The classes WEAKNESS and SUGGESTION are somewhat overlapping; e g , a suggestion may address a perceived weakness  Following are two example sentences in each class 
STRENGTH:
WEAKNESS:
SUGGESTION:
Several linguistic aspects of these classes of sentences are apparent  The subject is implicit in many sentences  The strengths are often mentioned as either noun phrases (NP) with positive adjectives (Excellent technology leadership) or positive nouns (engineering strength) or through verbs with positive polarity (dedicated) or as verb phrases containing positive adjectives (delivers innovative solutions)  Similarly for weaknesses, where negation is more frequently used (presentations are not his forte), or alternatively, the polarities of verbs (avoid) or adjectives (poor) tend to be negative  However, sometimes the form of both the strengths and weaknesses is the same, typically a stand-alone sentiment-neutral NP, making it difficult to distinguish between them; e g , adherence to timing or timely closure  Suggestions often have an imperative mood and contain secondary verbs such as need to, should, has to  Suggestions are sometimes expressed using comparatives (better process compliance)  We built a simple set of patterns for each of the 3 classes on the POS-tagged form of the sentences  We use each set of these patterns as an unsupervised sentence classifier for that class  If a particular sentence matched with patterns for multiple classes, then we have simple tie-breaking rules for picking the final class  The pattern for the STRENGTH class looks for the presence of positive words / phrases like takes ownership, excellent, hard working, commitment, etc  Similarly, the pattern for the WEAKNESS class looks for the presence of negative words / phrases like lacking, diffident, slow learner, less focused, etc  The SUGGESTION pattern not only looks for keywords like should, needs to but also for POS based pattern like “a verb in the base form (VB) in the beginning of a sentence” 
We randomly selected 2000 sentences from the supervisor assessment corpus and manually tagged them (dataset D1)  This labelled dataset contained 705, 103, 822 and 370 sentences having the class labels STRENGTH, WEAKNESS, SUGGESTION or OTHER respectively  We trained several multi-class classifiers on this dataset  Table TABREF10 shows the results of 5-fold cross-validation experiments on dataset D1  For the first 5 classifiers, we used their implementation from the SciKit Learn library in Python (scikit-learn org)  The features used for these classifiers were simply the sentence words along with their frequencies  For the last 2 classifiers (in Table TABREF10 ), we used our own implementation  The overall accuracy for a classifier is defined as INLINEFORM0 , where the denominator is 2000 for dataset D1  Note that the pattern-based approach is unsupervised i e , it did not use any training data  Hence, the results shown for it are for the entire dataset and not based on cross-validation 
Comparison with Sentiment Analyzer
We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS

ground_truth: LSA, TextRank, LexRank and ILP-based summary.

LSA, TextRank, LexRank

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. How large is the Twitter dataset?
Context 3: 
The tree creation is performed without processing the content of comments, which allows us to process posts and comments of any length efficiently  The heuristic used in the process is based on three simplifying assumptions:
Unless there is a specific reference to another comment or a user, comments are attached to the original post 
When replying, the commenting poster is always replying to the original post or some other comment  Unless specified otherwise, it is assumed that it is a response to the previous (in time) post/comment 
Subsequent comments by the same poster are part of the same thread 
To evaluate the performance of the thread extraction algorithm, 2 annotators have manually constructed the trees for 100 conversations  The performance of the algorithm on this set of 100 conversations is evaluated using accuracy and standard Information Retrieval evaluation metrics of precision, recall, and F$_1$ measure  The results are reported in Table TABREF28 together with random and majority baselines  The turn-level percent agreement between the 2 annotators is 97 99% and Cohen's Kappa Coefficient is 83 80% 
Datasets ::: Data Representation
For both data sources, Twitter and OSG with extracted threads, posts and comments are tokenized and sentence split  Each sentence is passed through Sentiment Analysis and Dialogue Act tagging  Since a post or a comment can contain multiple sentences, therefore multiple Dialogue Acts, it is represented as as a one-hot encoding, where each position represents a Dialogue Act 
For Sentiment Analysis we use a lexicon-based sentiment analyser introduced by BIBREF25  For Dialogue Act tagging, on the other hand, we make use of a model trained on NPSChat corpus BIBREF22 following the approach of BIBREF26 
Analysis
As we mentioned in Section 3, the presence of each of the therapeutic conditions under analysis is a necessary for a conversation to be considered to have therapeutic factors  In this section we present the results of our analysis with respect to these conditions 
Analysis ::: Change in Sentiment score of Original Posters
The first condition which we test is the sentiment change in conversation threads, comparing the initial and final sentiment scores (i e  posts' scores) of the original poster  The results of the analysis are presented in Figure FIGREF33  In the figure we can observe that the distribution of the sentiment change in the two datasets is different  While in Twitter the amount of conversations that lead to the increase of sentiment score is roughly equal to the amount of conversations that lead to the decrease of sentiment score; the situation is different for OSG  In OSG, the amount of conversations that lead to the increase of sentiment score is considerably higher 
Figure FIGREF34 provides a more fine grained analysis, where we additionally analyse the sentiment change in nominal polarity terms – negative and positive  In OSG, the number of users that changed polarity from negative to positive is more than the double of the users that have changed the polarity from positive to negative  In Twitter, on the other hand, the users mostly changed polarity from positive to negative  Results of the analysis suggest that in OSG , sentiment increases and users tend to change polarity from negative to positive, whereas in Twitter sentiment tends to decrease  Verification of this condition alone indicates that the ratio of potentially therapeutic conversations in Twitter is lower 
Analysis ::: Structure of Posts and Comments
Table TABREF36 presents the distribution of automatically predicted per-sentence Dialogue Acts in the datasets  The most frequent tag is statement in both  In Table TABREF37, on the other hand, we present the distribution of post and comment structures in terms of automatically predicted Dialogue Act tags  The structure is an unordered set of tags in the post or comment  From the table we can observe that the distribution of tag sets is similar between posts and comments  In both cases the most common set is statement only  However, conversations containing only statement, emphasis or question posts and comments predominantly appear in Twitter  Which is expected due to the shorter length of Twitter posts and comments 
We can also observe that the original posters tend to ask more questions than the commenting posters – 19 83% for posts vs  11 21% for comments (summed)  This suggests that the original posters frequently ask either for suggestion or confirmation of their points of view or their disconfirmation  However, the high presence of personal experiences is supported by the high number of posts containing only statements 
High number of statement tags in comments suggests that users reply either with supporting or empathic statements or personal experience  However, 6 39% of comments contain accept and reject tags, which mark the degree to which a speaker accepts some previous proposal, plan, opinion, or statement BIBREF20  The described Dialogue Act tags are often used when commenting posters discuss original poster's point of view  For instance, “It's true  I felt the same ” – {Accept, Statement} or “Well no  You're not alone” – {Reject, Statement}  The datasets differ with respect to the distribution of these Dialogue Acts tags, they appear more frequently in OSG 
Analysis ::: Sentiment of Posts and Comments
Table TABREF39 presents the distribution of sentiment polarity in post and comment statements (i e  sentences tagged as statement)  For OSG, the predominant sentiment label of statements is positive and it is the highest for both posts and comments  However, the difference between the amounts of positive and negative statements is higher for the replying comments (34 5% vs  42 5%)  For Twitter, on the other hand, the predominant sentiment label of statements is neutral and the polarity distribution between posts and comments is very close  One particular observation is that the ratio of negative statements is higher in OSG for both posts and comments than in Twitter, which supports the idea of sharing negative experiences 
Further we analyze whether the sentiment of a comment (i e  the replying user) is affected by the sentiment of the original post (i e  the user being replied to), which will imply that the users adapt their behaviour with respect to the post's sentiment  For the analysis, we split the datasets into three buckets according to the posts' sentiment score – negative, neutral, or positive, and represent each conversation in terms of percentages of comments (replies) with each sentiment label  The buckets are then compared using t-test for statistically significant differences 
Table TABREF40 presents the distribution of sentiment labels with respect to the post's sentiment score  The patterns of distribution are similar across the datasets  We can observe that overall, replies tend to have a positive sentiment, which suggests that replying posters tend to have a positive attitude  However, the ratio of positive comments is higher for OSG than for Twitter 
The results of the Welch's t-test on OSG data reveal that there are statistically significant differences in the distribution of replying comments' sentiment between conversations with positive and negative starting posts  A positive post tends to get significantly more positive replies  Similarly, a negative post tends to get significantly more negative replies (both with $p < 0 01$) 
Table TABREF41 presents the distribution of the sentiment labels of the final text provided by the original poster with respect to the sentiment polarity of the comments  The results indicate that OSG participants are more supportive, as the majority of conversations end in a positive final sentiment regardless of the sentiment of comments  We can also observe that negative comments in OSG lead to positive sentiment, which supports the idea of sharing the negative experiences, thus presence of therapeutic factors  For Twitter, on the other hand, only positive comments lead to the positive final sentiments, whereas other comments lead predominantly to neutral final sentiments 
Our analysis in terms of sentiment and Dialogue Acts supports the presence of the three selected therapeutic factors – Universality, Altruism and Instillation of Hope – in OSG more than in Twitter  The main contributors to this conclusion are the facts that there is more positive change in the sentiment of the original posters in OSG (people seeking support) and that in OSG even negative and neutral comments are likely to lead to positive changes 
Conclusion
In this work, we propose a methodology to automatically analyse online social platforms for the presence of therapeutic factors (i e  Universality, Altruism and Instillation of Hope)  We evaluate our approach on two on-line platforms, Twitter and an OSG web forum  We apply NLP techniques of Sentiment Analysis and Dialogue Act tagging to automatically verify the presence of therapeutic factors, which allows us to analyse larger amounts of conversational data (as compared to previous studies) 
Our analysis indicates that OSG conversations satisfy higher number of conditions approximating therapeutic factors than Twitter conversations  Given this outcome, we postulate that users who join support group websites spontaneously seem to benefit from it  Indeed, as shown in Section SECREF5, the original posters who interact with others by replying to comments, have benefited from an improvement of their emotional state 
We would like to reemphasise that the conditions for the therapeutic factors are necessary but not sufficient; since our analysis focuses on the structure of conversations, being agnostic to the content  NLP, however, allows us to strengthen our approximations even further  Thus, the further extension of our work is also augmentation of our study with other language analysis metrics and their correlation with human annotation 
It should be noted that the proposed approach is an approximation of the tedious tasks of annotation of conversations by experts versed in the therapeutic factors and their associated theories  Even though we can use Sentiment Analysis to detect the existence of therapeutic factors, we cannot differentiate between Altruism and Instillation of Hope, as this requires differentiation between emotional state of the user and the intended emotional communication  Thus, the natural extensions of this work are differentiation between different therapeutic factors and comparison of the proposed analysis to the human evaluation 
Although we acknowledge that the proposed methodology does not serve as a replacement of manual analysis of OSG for the presence of therapeutic factors, we believe that it could facilitate and supplement this process  The method can serve as a tool for general practitioners and psychologists who can use it as an additional source of information regarding their patients’ condition and, in turn, offer a more personalised support that is better tailored to individual therapeutic needs 

ground_truth: 1,873 Twitter conversation threads, roughly 14k tweets

1,873 Twitter conversation threads, roughly 14k tweets