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. Which toolkits do they use?
Context 1: Introduction
As social media, specially Twitter, takes on an influential role in presidential elections in the U S , natural language processing of political tweets BIBREF0 has the potential to help with nowcasting and forecasting of election results as well as identifying the main issues with a candidate – tasks of much interest to journalists, political scientists, and campaign organizers BIBREF1  As a methodology to obtain training data for a machine learning system that analyzes political tweets, BIBREF2 devised a crowdsourcing scheme with variable crowdworker numbers based on the difficulty of the annotation task  They provided a dataset of tweets where the sentiments towards political candidates were labeled both by experts in political communication and by crowdworkers who were likely not domain experts  BIBREF2 revealed that crowdworkers can match expert performance relatively accurately and in a budget-efficient manner  Given this result, the authors envisioned future work in which groundtruth labels would be crowdsourced for a large number of tweets and then used to design an automated NLP tool for political tweet analysis 
The question we address here is: How accurate are existing NLP tools for political tweet analysis? These tools would provide a baseline performance that any new machine learning system for political tweet analysis would compete against  We here explore whether existing NLP systems can answer the questions "What sentiment?" and "Towards whom?" accurately for the dataset of political tweets provided by BIBREF2  In our analysis, we include NLP tools with publicly-available APIs, even if the tools were not specifically designed for short texts like tweets, and, in particular, political tweets 
Our experiments reveal that the task of entity-level sentiment analysis is difficult for existing tools to answer accurately while the recognition of the entity, here, which politician, was easier 
NLP Toolkits
NLP toolkits typically have the following capabilities: tokenization, part-of-speech (PoS) tagging, chunking, named entity recognition and sentiment analysis  In a study by BIBREF3, it is shown that the well-known NLP toolkits NLTK BIBREF4, Stanford CoreNLP BIBREF5, and TwitterNLP BIBREF6 have tokenization, PoS tagging and NER modules in their pipelines  There are two main approaches for NER: (1) rule-based and (2) statistical or machine learning based  The most ubiquitous algorithms for sequence tagging use Hidden Markov Models BIBREF7, Maximum Entropy Markov Models BIBREF7, BIBREF8, or Conditional Random Fields BIBREF9  Recent works BIBREF10, BIBREF11 have used recurrent neural networks with attention modules for NER 
Sentiment detection tools like SentiStrength BIBREF12 and TensiStrength BIBREF13 are rule-based tools, relying on various dictionaries of emoticons, slangs, idioms, and ironic phrases, and set of rules that can detect the sentiment of a sentence overall or a targeted sentiment  Given a list of keywords, TensiStrength (similar to SentiStrength) reports the sentiment towards selected entities in a sentence, based on five levels of relaxation and five levels of stress 
Among commercial NLP toolkits (e g , BIBREF14, BIBREF15, BIBREF16), we selected BIBREF17 and BIBREF18 for our experiments, which, to the best of our knowledge, are the only publicly accessible commercial APIs for the task of entity-level sentiment analysis that is agnostic to the text domain  We also report results of TensiStrength BIBREF13, TwitterNLP BIBREF6, BIBREF19, CogComp-NLP BIBREF20, and Stanford NLP NER BIBREF21 
Dataset and Analysis Methodology
We used the 1,000-tweet dataset by BIBREF2 that contains the named-entities labels and entity-level sentiments for each of the four 2016 presidential primary candidates Bernie Sanders, Donald Trump, Hillary Clinton, and Ted Cruz, provided by crowdworkers, and by experts in political communication, whose labels are considered groundtruth  The crowdworkers were located in the US and hired on the BIBREF22 platform  For the task of entity-level sentiment analysis, a 3-scale rating of "negative," "neutral," and "positive" was used by the annotators 
BIBREF2 proposed a decision tree approach for computing the number of crowdworkers who should analyze a tweet based on the difficulty of the task  Tweets are labeled by 2, 3, 5, or 7 workers based on the difficulty of the task and the level of disagreement between the crowdworkers  The model computes the number of workers based on how long a tweet is, the presence of a link in a tweet, and the number of present sarcasm signals  Sarcasm is often used in political tweets and causes disagreement between the crowdworkers  The tweets that are deemed to be sarcastic by the decision tree model, are expected to be more difficult to annotate, and hence are allocated more crowdworkers to work on 
We conducted two sets of experiments  In the first set, we used BIBREF23, BIBREF17, and BIBREF18, for entity-level sentiment analysis; in the second set, BIBREF17, BIBREF19, BIBREF24, BIBREF25, and BIBREF26, BIBREF18 for named-entity recognition 
In the experiments that we conducted with TwitterNLP for named-entity recognition, we worked with the default values of the model  Furthermore, we selected the 3-class Stanford NER model, which uses the classes “person,” “organization,” and “location” because it resulted in higher accuracy compared to the 7-class model  For CogComp-NLP NER we used Ontonotes 5 0 NER model BIBREF27  For spaCy NER we used the `en_core_web_lg' model 
We report the experimental results for our two tasks in terms of the correct classification rate (CCR)  For sentiment analysis, we have a three-class problem (positive, negative, and neutral), where the classes are mutually exclusive  The CCR, averaged for a set of tweets, is defined to be the number of correctly-predicted sentiments over the number of groundtruth sentiments in these tweets  For NER, we consider that each tweet may reference up to four candidates, i e , targeted entities  The CCR, averaged for a set of tweets, is the number of correctly predicted entities (candidates) over the number of groundtruth entities (candidates) in this set 
Results and Discussion
The dataset of 1,000 randomly selected tweets contains more than twice as many tweets about Trump than about the other candidates  In the named-entity recognition experiment, the average CCR of crowdworkers was 98 6%, while the CCR of the automated systems ranged from 77 2% to 96 7%  For four of the automated systems, detecting the entity Trump was more difficult than the other entities (e g , spaCy 72 7% for the entity Trump vs  above 91% for the other entities)  An example of incorrect NER is shown in Figure FIGREF1 top  The difficulties the automated tools had in NER may be explained by the fact that the tools were not trained on tweets, except for TwitterNLP, which was not in active development when the data was created BIBREF1 
In the sentiment analysis experiments, we found that a tweet may contain multiple sentiments  The groundtruth labels contain 210 positive sentiments, 521 neutral sentiments, and 305 negative sentiments to the candidates  We measured the CCR, across all tweets, to be 31 7% for Rosette Text Analytics, 43 2% for Google Cloud, 44 2% for TensiStrength, and 74 7% for the crowdworkers  This means the difference between the performance of the tools and the crowdworkers is significant – more than 30 percent points 
Crowdworkers correctly identified 62% of the neutral, 85% of the positive, and 92% of the negative sentiments  Google Cloud correctly identified 88% of the neutral sentiments, but only 3% of the positive, and 19% of the negative sentiments  TensiStrength correctly identified 87 2% of the neutral sentiments, but 10 5% of the positive, and 8 1% of the negative sentiments  Rosette Text Analytics correctly identified 22 7% of neutral sentiments, 38 1% of negative sentiments and 40 9% of positive sentiments  The lowest and highest CCR pertains to tweets about Trump and Sanders for both Google Cloud and TensiStrength, Trump and Clinton for Rosette Text Analytics, and Clinton and Cruz for crowdworkers  An example of incorrect ELS analysis is shown in Figure FIGREF1 bottom 
Conclusions and Future Work
Our results show that existing NLP systems cannot accurately perform sentiment analysis of political tweets in the dataset we experimented with  Labeling by humans, even non-expert crowdworkers, yields accuracy results that are well above the results of existing automated NLP systems  In future work we will therefore use a crowdworker-labeled dataset to train a new machine-learning based NLP system for tweet analysis  We will ensure that the training data is balanced among classes  Our plan is to use state-of-the-art deep neural networks and compare their performance for entity-level sentiment analysis of political tweets 
Acknowledgments
Partial support of this work by the Hariri Institute for Computing and Computational Science & Engineering at Boston University (to L G ) and a Google Faculty Research Award (to M B  and L G ) is gratefully acknowledged  Additionally, we would like to thank Daniel Khashabi for his help in running the CogComp-NLP Python API and Mike Thelwal for his help with TensiStrength  We are also grateful to the Stanford NLP group for clarifying some of the questions we had with regards to the Stanford NER tool 
Context 3: Introduction
Many research attempts have proposed novel features that improve the performance of learning algorithms in particular tasks  Such features are often motivated by domain knowledge or manual labor  Although useful and often state-of-the-art, adapting such solutions on NLP systems across tasks can be tricky and time-consuming BIBREF0   Therefore, simple yet general and powerful methods that perform well across several datasets are valuable BIBREF1  
An approach that has become extremely popular lately in NLP tasks, is to train word embeddings in an unsupervised way  These embeddings are dense vectors that project words or short text spans like phrases in a vector space where dimensions are supposed to capture text properties  Such embeddings can then be used either as features with off-the-shelf algorithms like Support Vector Machines, or to initialize deep learning systems BIBREF2   However, as shown in BIBREF3 linear architectures perform better in high-dimensional discrete spaces compared to continuous ones  The latter is probably the main reason of the high performance of the vector space model BIBREF4 in tasks like text classification with linear models like SVMs  Using linear algorithms, while taking advantage of the expressiveness of text embeddings is the focus of this work 
In this paper, we explore a hybrid approach, that uses text embeddings as a proxy to create features  Motivated by the argument that text embeddings manage to encode the semantics of text, we explore how clustering text embeddings can impact the performance of different NLP tasks  Although such an approach has been used in different studies during feature engineering, the selection of word vectors and the number of clusters remain a trial-end-error procedure  In this work we present an empirical evaluation across diverse tasks to verify whether and when such features are useful 
Word clusters have been used as features in various tasks like Part-of-Speech tagging and NER  Owoputi et al  Owoputi13 use Brown clusters BIBREF5 in a POS tagger showing that this type of features carry rich lexical knowledge as they can substitute lexical resources like gazetteers  Kiritchenko et al  KiritchenkoZM14 discusses their use on sentiment classification while Hee et al  HeeLH16 incorporate them in the task of irony detection in Twitter  Ritter et al  Ritter2011 inject also word clusters in a NER tagger  While these works show that word clusters are beneficial no clear guidelines can be concluded of how and when to use them 
In this work, we empirically demonstrate that using different types of embeddings on three NLP tasks with twitter data we manage to achieve better or near to the state-of-the art performance on three NLP tasks: (i) Named Entity Recognition (NER) segmentation, (ii) NER classification, (iii) fine-grained sentiment analysis and (iv) fine-grained sentiment quantification  For each of the three tasks, we achieve higher performance than without using features which indicates the effectiveness of the cluster membership features  Importantly, our evaluation compared to previous work BIBREF6 who focus on old and well studied datasets uses recent and challenging datasets composed by tweets  The obtained results across all the tasks permits us to reveal important aspects of the use of word clusters and therefore provide guidelines  Although our obtained scores are state-of-the-art, our analysis reveals that the performance in such tasks is far from perfect and, hence, identifies that there is still much space for improvement and future work 
Word Clusters
Word embeddings associate words with dense, low-dimensional vectors  Recently, several models have been proposed in order to obtain these embeddings  Among others, the skipgram (skipgram) model with negative sampling BIBREF7 , the continuous bag-of-words (cbow) model BIBREF7 and Glove (glove) BIBREF8 have been shown to be effective  Training those models requires no annotated data and can be done using big amounts of text  Such a model can be seen as a function INLINEFORM0 that projects a word INLINEFORM1 in a INLINEFORM2 -dimensional space: INLINEFORM3 , where INLINEFORM4 is predefined  Here, we focus on applications using data from Twitter, which pose several difficulties due to being particularly short, using creative vocabulary, abbreviations and slang 
For all the tasks in our experimental study, we use 36 millions English tweets collected between August and September 2017  A pre-processing step has been applied to replace URLs with a placeholder and to pad punctuation  The final vocabulary size was around 1 6 millions words  Additionally to the in-domain corpus we collected, we use GloVe vectors trained on Wikipedia articles in order to investigate the impact of out-of-domain word-vectors 
We cluster the embeddings with INLINEFORM0 -Means  The k-means clusters are initialized using “k-means++” as proposed in BIBREF9 , while the algorithm is run for 300 iterations  We try different values for INLINEFORM1   For each INLINEFORM2 , we repeat the clustering experiment with different seed initialization for 10 times and we select the clustering result that minimizes the cluster inertia 
Experimental Evaluation
We evaluate the proposed approach for augmenting the feature space in four tasks: (i) NER segmentation, (ii) NER classification, (iii) fine-grained sentiment classification and (iv) fine-grained sentiment quantification  The next sections present the evaluation settings we used  For each of the tasks, we use the designated training sets to train the learning algorithms, and we report the scores of the evaluation measures used in the respective test parts 
Named-Entity Recognition in Twitter
NER concerns the classification of textual segments in a predefined set of categories, like persons, organization and locations

ground_truth: BIBREF17, BIBREF18, TensiStrength BIBREF13, TwitterNLP BIBREF6, BIBREF19, CogComp-NLP BIBREF20, Stanford NLP NER BIBREF21

BIBREF23, BIBREF17, BIBREF18, BIBREF19, BIBREF24, BIBREF25, BIBREF26

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 2: 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 
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 
Context 3:  We use the data of the last competition in NER for Twitter which released as a part of the 2nd Workshop on Noisy User-generated Text BIBREF10   More specifically, the organizers provided annotated tweets with 10 named-entity types (person, movie, sportsteam, product etc ) and the task comprised two sub-tasks: 1) the detection of entity bounds and 2) the classification of an entity into one of the 10 types  The evaluation measure for both sub-tasks is the F INLINEFORM0 measure 
The following is an example of a tweet which contains two named entities  Note that named entities may span several words in the text:
INLINEFORM0 tonite     90 's music    oldskool night wiith INLINEFORM1
Our model for solving the task is a learning to search approach  More specifically we follow BIBREF11 which has been ranked 2nd among 10 participants in the aforementioned competition BIBREF10   The model uses handcrafted features like n-grams, part-of-speech tags, capitalization and membership in gazetteers  The algorithm used belongs to the family of learning to search for structured prediction tasks BIBREF12   These methods decompose the problem in a search space with states, actions and policies and then learn a hypothesis controlling a policy over the state-action space  The BIO encoding is used for attributing the corresponding labels to the tokens where B-type is used for the first token of the entity, I-type for inside tokens in case of multi-term entities and O for non entity tokens 
Tables TABREF6 and TABREF7 present the results for the different number of clusters across the three vector models used to induce the clusters  For all the experiments we keep the same parametrization for the learning algorithm and we present the performance of each run on the official test set 
Regarding the segmentation task we notice that adding word clusters as features improve the performance of the best model up to 1 1 F-score points while it boosts performance in the majority of cases  In only one case, for glove INLINEFORM0 vectors, there is a drop across all number of clusters used 
As for the number of clusters, the best results are generally obtained between 250 and 1000 classes for all word vector models  These dimensions seem to be sufficient for the three-class sub-task that we deal with  The different models of word vectors perform similarly and thus one cannot privilege a certain type of word vectors  Interestingly, the clusters learned on the Wikipedia GloVe vectors offer competitive performance with respect to the in-domain word vectors used for the other cases showing that one can rely to out-of-domain data for constructing such representations 
Concerning the classification task (Table TABREF7 ) we generally observe a drop in the performance of the tagger as we deal with 10 classes  This essentially corresponds to a multi-class problem with 21 classes: one for the non-entity type and two classes for each entity type  In this setting we notice that the best results are obtained in most cases for higher number of classes (1000 or 2000) possibly due to a better discriminatory power in higher dimensions  Note also, that in some cases the addition of word cluster features does not necessarily improve the performance  Contrary, it may degrade it as it is evident in the case of glove INLINEFORM0 word clusters  Like in the case of segmentation we do not observe a word vector model that clearly outperforms the rest  Finally, we note the same competitive performance of the Wikipedia word clusters and notably for the glove INLINEFORM1 clusters which obtain the best F1-score 
Fine-grained Sentiment Analysis
The task of fine grained sentiment classification consists in predicting the sentiment of an input text according to a five point scale (sentiment INLINEFORM0 {VeryNegative, Negative, Neutral, Positive, VeryPositive})  We use the setting of task 4 of SemEval2016 “Sentiment Analysis in Twitter” and the dataset released by the organizers for subtask 4 BIBREF13  
In total, the training (resp  test) data consist of 9,070 (resp  20,632) tweets 
The evaluation measure selected in BIBREF13 for the task in the macro-averaged Mean Absolute Error (MAE INLINEFORM0 )  It is a measure of error, hence lower values are better  The measure's goal is to take into account the order of the classes when penalizing the decision of a classifier  For instance, misclassifying a very negative example as very positive is a bigger mistake than classifying it as negative or neutral  Penalizing a classifier according to how far the predictions are from the true class is captured by MAE INLINEFORM1 BIBREF14   Also, the advantage of using the macro- version instead of the standard version of the measure is the robustness against the class imbalance in the data 
Learning algorithm To demonstrate the efficiency of cluster membership features we rely on the system of BIBREF15 which was ranked 1st among 11 participants and uses a Logistic Regression as a learning algorithm  We follow the same feature extraction steps which consist of extracting n-gram and character n-gram features, part-of-speech counts as well as sentiment scores using standard sentiment lexicons such as the Bing Liu's BIBREF16 and the MPQA lexicons BIBREF17   For the full description, we refer the interested reader to BIBREF15  
To evaluate the performance of the proposed feature augmentation technique, we present in Table TABREF10 the macro-averaged Mean Absolute Error scores for different settings on the official test set of BIBREF13 

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

1,873 Twitter conversation threads, roughly 14k tweets

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. Which hyperparameters were varied in the experiments on the four tasks?
Context 3:  First, notice that the best score in the test data is achieved using cluster membership features, where the word embeddings are trained using the skipgram model  The achieved score improves the state-of-the art on the dataset, which to the best of our knowledge was by BIBREF15   Also, note that the score on the test data improves for each type of embeddings used, which means that augmenting the feature space using cluster membership features helps the sentiment classification task 
Note, also, that using the clusters produced by the out-of-domain embeddings trained on wikipedia that were released as part of BIBREF8 performs surprisingly well  One might have expected their addition to hurt the performance  However, their value probably stems from the sheer amount of data used for their training as well as the relatively simple type of words (like awesome, terrible) which are discriminative for this task  Lastly, note that in each of the settings, the best results are achieved when the number of clusters is within INLINEFORM0 as in the NER tasks  Comparing the performance across the different embeddings, one cannot claim that a particular embedding performs better  It is evident though that augmenting the feature space with feature derived using the proposed method, preferably with in-domain data, helps the classification performance and reduces MAE INLINEFORM1  
From the results of Table TABREF10 it is clear that the addition of the cluster membership features improves the sentiment classification performance  To better understand though why these clusters help, we manually examined a sample of the words associated with the clusters  To improve the eligibility of those results we first removed the hashtags and we filter the results using an English vocabulary  In Table TABREF11 we present sample words from two of the most characteristic clusters with respect to the task of sentiment classification  Notice how words with positive and negative meanings are put in the respective clusters 
Fine-Grained Sentiment Quantification
Quantification is the problem of estimating the prevalence of a class in a dataset  While classification concerns assigning a category to a single instance, like labeling a tweet with the sentiment it conveys, the goal of quantification is, given a set of instances, to estimate the relative frequency of single class  Therefore, sentiment quantification tries to answer questions like “Given a set of tweets about the new iPhone, what is the fraction of VeryPositive ones?”  In the rest, we show the effect of the features derived from the word embeddings clusters in the fine-grained classification problem, which was also part of the SemEval-2016 “Sentiment Analysis in Twitter” task BIBREF13  
Learning Algorithm To perform the quantification task, we rely on a classify and count approach, which was shown effective in a related binary quantification problem BIBREF15   The idea is that given a set of instances on a particular subject, one first classifies the instances and then aggregates the counts  To this end, we use the same feature representation steps and data with the ones used for fine grained classification (Section 3 2)  Note that the data of the task are associated with subjects (described in full detail at BIBREF13 ), and, hence, quantification is performed for the tweets of a subject  For each of the five categories, the output of the approach is a 5-dimensional vector with the estimated prevalence of the categories 
The evaluation measure for the problem is the Earth Movers Distance (EMD) BIBREF18   EMD is a measure of error, hence lower values are better  It assumes ordered categories, which in our problem is naturally defined  Further assuming that the distance of consecutive categories (e g , Positive and VeryPositive) is 1, the measure is calculated by: INLINEFORM0
where INLINEFORM0 is number of categories (five in our case) and INLINEFORM1 and INLINEFORM2 are the true and predicted prevalence respectively BIBREF19  
Results Table TABREF13 presents the results of augmenting the feature set with the proposed features  We use Logistic Regression as a base classifier for the classify and count approach  Notice the positive impact of the features in the performance in the task  Adding the features derived from clustering the embeddings consistently improves the performance  Interestingly, the best performance ( INLINEFORM0 ) is achieved using the out-of-domain vectors, as in the NER classification task  Also, notice how the approach improves over the state-of-the-art performance in the challenge ( INLINEFORM1 ) BIBREF13 , held by the method of BIBREF20   The improvement over the method of BIBREF20 however, does not necessarily mean that classify and count performs better in the task  It implies that the feature set we used is richer, that in turn highlights the value of robust feature extraction mechanisms which is the subject of this paper 
Conclusion
We have shown empirically the effectiveness of incorporating cluster membership features in the feature extraction pipeline of Named-Entity recognition, sentiment classification and quantification tasks  Our results strongly suggest that incorporating cluster membership features benefit the performance in the tasks  The fact that the performance improvements are consistent in the four tasks we investigated, further highlights their usefulness, both for practitioners and researchers 
Although our study does not identify a clear winner with respect to the type of word vectors (skipgram, cbow, or GloVe), our findings suggest that one should first try skip-gram embeddings of low dimensionality ( INLINEFORM0 ) and high number of clusters (e g , INLINEFORM1 ) as the results obtained using these settings are consistently competitive  Our results also suggest that using out-of-domain data, like Wikipedia articles in this case, to construct the word embeddings is a good practice, as the results we obtained with these vectors are also competitive  The positive of out-of-domain embeddings and their combination with in-domain ones remains to be further studied 

ground_truth: number of clusters, seed value in clustering, selection of word vectors, window size and dimension of embedding

different number of clusters, different embeddings