Software Support for Discourse-Based Textual Information Analysis: A Systematic Literature Review and Software Guidelines in Practice

: The intrinsic characteristics of humanities research require technological support and software assistance that also necessarily goes through the analysis of textual narratives. When these narratives become increasingly complex, pragmatics analysis (i.e., at discourse or argumentation levels) assisted by software is a great ally in the digital humanities. In recent years, solutions have been developed from the information visualization domain to support discourse analysis or argumentation analysis of textual sources via software, with applications in political speeches, debates, online forums, but also in written narratives, literature or historical sources. This paper presents a wide and interdisciplinary systematic literature review (SLR), both in software-related areas and humanities areas, on the information visualization and the software solutions adopted to support pragmatics textual analysis. As a result of this review, this paper detects weaknesses in existing works on the ﬁeld, especially related to solutions’ availability, pragmatic framework dependence and lack of information sharing and reuse software mechanisms. The paper also provides some software guidelines for improving the detected weaknesses, exemplifying some guidelines in practice through their implementation in a new web tool, Viscourse . Viscourse is conceived as a complementary tool to assist textual analysis and to facilitate the reuse of informational pieces from discourse and argumentation text analysis tasks


Introduction
Information management in humanities disciplines necessarily involves natural language textual sources analysis at any level.Recently, digital humanities area is including initiatives and works on how to assist textual analysis tasks via software (see chapters 19-21 from [1] for some examples).
Mainly, this software assistance focuses on two different assistance directions: (1) natural language processing automatization solutions and (2) the development of visualization techniques to visualize results from automation tasks -from group 1-or to help on manual or semiautomatics textual analysis, including annotation tasks, adapted to humanistic disciplines.
Regarding the second kind of software assistance, there are several proposals at visualization level [2,3] for visualizing grammatical or lexical structures of the texts, dealing with morphology, lexicology or syntax levels of linguistic analysis.
However, as long as we focus on more conceptual or relational aspects of the textual sources (at a semantics or pragmatics levels), the previous group 2 of software assistance decreases.Although 1.
RQ1: What evidence is there that discourse and argumentation textual analysis is currently supported via information visualization software?2.
RQ2: What kind of support are these works providing and how is it implemented?We have defined seven categories for analyzing the kind of support provided by the main contribution presented in these works: • InfoVis technique: The support provided is mainly visual.For example, a new information visualization technique or its application to a new kind of discursive or argumentation textual information.

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Linguistic resource: The main support is provided by offering a new linguistic resource: a new corpus, annotation information, taxonomy, ontological information, etc.

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Complete software tool: The assistance is materialized as an entire new software tool.

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Application example: The support is provided by illustrating a new discursive or argumentation analysis in a new domain of application or corpora.

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Discourse metrics measurement: The support is provided by implementing software mechanisms to calculate discursive or argumentation standard metrics for helping in the textual analysis.

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Fully automatic analysis support: The support is provided by implementing automatic software solutions for visualizing automatic discursive or argumentation analysis: fully automatic parsers, new algorithms or machine learning techniques, etc.

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Survey/Empirical or Qualitative study: Works focused on qualitative analysis.

3.
RQ3: Does the software support offered in these works present any weakness or deficiency reported in the study itself or detected as a result of the review?4.
RQ4: Is it possible to identify some software guidelines for improving the existing information visualization software solutions for supporting or assisting discourse and argumentation textual analysis tasks?
Figure 1 details the SLR process performed.First, we identified the main research repositories for finding relevant work that answer our research questions.Then, we eliminated duplications, defining a filtered inclusion/exclusion strategy.Finally, we analyzed the title and abstract of the resultant publications.In this step, we also applied some quality assessment criteria as a checklist to the works obtained.This quality assessment step ensured that the works reviewed have no important bias and fit the scope and relevance criteria.The resultant set of publications constitutes the relevant work done on visualization software assistance (labeled as group 2 in the Introduction) for supporting or assisting discourse and argumentation textual analysis tasks.
Information 2019, 10, x FOR PEER REVIEW 3 of 20 • Fully automatic analysis support: The support is provided by implementing automatic software solutions for visualizing automatic discursive or argumentation analysis: fully automatic parsers, new algorithms or machine learning techniques, etc. • Survey/Empirical or Qualitative study: Works focused on qualitative analysis.3. RQ3: Does the software support offered in these works present any weakness or deficiency reported in the study itself or detected as a result of the review?4. RQ4: Is it possible to identify some software guidelines for improving the existing information visualization software solutions for supporting or assisting discourse and argumentation textual analysis tasks? Figure 1 details the SLR process performed.First, we identified the main research repositories for finding relevant work that answer our research questions.Then, we eliminated duplications, defining a filtered inclusion/exclusion strategy.Finally, we analyzed the title and abstract of the resultant publications.In this step, we also applied some quality assessment criteria as a checklist to the works obtained.This quality assessment step ensured that the works reviewed have no important bias and fit the scope and relevance criteria.The resultant set of publications constitutes the relevant work done on visualization software assistance (labeled as group 2 in the Introduction) for supporting or assisting discourse and argumentation textual analysis tasks.

Sources, Search Strategies and Filtered Criteria
In order to answer the research questions, we have defined a combined search strategy in two different kinds of sources.On the one hand, we have searched in four international and well-known digital libraries of research publications.Specifically, we have chosen Science Direct [5], Springer Link [6], ACM library [7] and IEEE Xplore [8] due to the accessibility, degree of reliability and relevance in software engineering.On the other hand, we have also performed the same searches in Digital Humanities relevant repositories that contain works on software engineering and discourse or argumentation textual analysis.Specifically, we have reviewed ACL Anthology [9] and RST repository (Rhetorical Structure Theory) [10][11][12][13][14] as main sources for works on computational linguistics in the area.Moreover, we have included the main two research journals on Digital Humanities on an international level: Digital Scholarship in the Humanities (henceforth DSH) [15] and Digital Humanities Quarterly (henceforth DHQ) [16].
Relevant terms for extracting computational information visualization solutions on assisting discourse or argumentation textual analysis are included as keywords in the queries, such as "discourse analysis", "argument mining", "information visualization", "software tool", etc.Note that, in order to deal with a manageable number of publications, we also add in this first phase of the SLR filters for the date of publication and publisher.Due to some repositories also acting as hubs of other repositories publications (e.g., ACM library), it is necessary to limit the searches to their own resources in order to avoid duplications.Table 1 summarizes repositories, queries performed, and the number of initial results achieved.

Sources, Search Strategies and Filtered Criteria
In order to answer the research questions, we have defined a combined search strategy in two different kinds of sources.On the one hand, we have searched in four international and well-known digital libraries of research publications.Specifically, we have chosen Science Direct [5], Springer Link [6], ACM library [7] and IEEE Xplore [8] due to the accessibility, degree of reliability and relevance in software engineering.On the other hand, we have also performed the same searches in Digital Humanities relevant repositories that contain works on software engineering and discourse or argumentation textual analysis.Specifically, we have reviewed ACL Anthology [9] and RST repository (Rhetorical Structure Theory) [10][11][12][13][14] as main sources for works on computational linguistics in the area.Moreover, we have included the main two research journals on Digital Humanities on an international level: Digital Scholarship in the Humanities (henceforth DSH) [15] and Digital Humanities Quarterly (henceforth DHQ) [16].
Relevant terms for extracting computational information visualization solutions on assisting discourse or argumentation textual analysis are included as keywords in the queries, such as "discourse analysis", "argument mining", "information visualization", "software tool", etc.Note that, in order to deal with a manageable number of publications, we also add in this first phase of the SLR filters for the date of publication and publisher.Due to some repositories also acting as hubs of other repositories publications (e.g., ACM library), it is necessary to limit the searches to their own resources in order to avoid duplications.Table 1 summarizes repositories, queries performed, and the number of initial results achieved.Query: ("discourse analysis" OR "argument mining") AND ("information visualization" OR "visualization" OR "visual analytics") AND ("software" OR "tool")

TOTAL 1480
As Table 1 shows, the preliminary set of publications consists of 1480 works.Following the SLR guidelines, a set of inclusion/exclusion criteria are defined, focusing on original and recent works on information visualization implemented via software solutions for supporting discourse or argumentation analysis tasks.Thus, refinement criteria were applied as:

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The year of publication between 2010 and 2020.

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Original publications written in English language.

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Only original publications: papers in journals and full papers in conferences (also edited as chapters), excluding workshops.

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Only publications with scope on Computer Science (Software Engineering, Information Visualization and Computational Linguistics included) and Linguistics/Discourse/Argumentation-related areas.

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Only those publications that have associated original software/existing software use/demonstrator/ tools that provide visual support for discursive/argumentation textual analysis.
Applying these criteria, we obtained an intermediate reduced pool of 58 different publications.Finally, a step of quality assessment is applied to this intermediate set (see next section).

Quality Assessment
Due to the heterogeneity and diverse source of the publications included in the presented SLR, we systematically applied the following checklist as a quality assessment mechanism, answering yes (Y) or no (N) to the following questions: • Q1: Are the study goals clearly stated and related to textual analysis assistance, and are the software proposals clearly detailed?• Q2: Are the studies proposing an original software or an original application of existing software for assisting textual analysis through visual software resources?• Q3: Is the proposal validated with real text analysis cases?• Q4: Is the proposal dealing with textual discursive/argumentation analysis information?
• Q5: Is the proposal offering some software mechanisms for promoting the reuse and sharing of the information generated during their use?
Each publication of the intermediate set is evaluated applying the checklist (yes = 1 point, no = 0 point).Thus, each publication obtains a quality assessment score point (Qn) from Qn = 0 (minimum score) to Qn = 5 (maximum score).
We have decided that only publications that meet the following two quality requirements are finally included in the final set: -Publications with Qn greater than 3, that is, publications with at least three affirmative answers to the quality questions.
-Publications with an affirmative answer to question 2. This implies that their contribution in assistance is through information visualization mechanisms, which is the main area of this study.
Table A1 shows the final quality assessment evaluation.Only publications marked with a fine gray color in the table are included in the SLR final set repository.After the quality assessment phase, SLR final set repository is composed of 17 publications.The next section presents and deeply discusses the SLR findings and the guidelines extracted.

Systematic Literature Review Results
Multiple readings can be extracted from the SLR performed.In the first place, an important process of reducing the number of publications has been carried out, with a search results of 1480 publications, an intermediate SLR set of 58 publications and a final SLR repository of 17 publications.This refining process has been influenced by two aspects.
First, the selected keywords or combinations of them (multi-words) used in the search queries are commonly used in numerous disciplines, presenting polysemy in their use within the different research communities.Thus, the initial number of queries results is quite high for an SLR process, finding that many of the initially retrieved publications did not meet any of the quality assessment criteria proposed by our SLR process.Therefore, subsequent quality assessment process allowed us to focus on works that specifically proposed software solutions to assist in textual analysis on a discursive and argumentation level.
Secondly, many of the publications reviewed, although they were relevant applying keywords, did not cover the full scope of the work.These works are shown in a white background color in Table A1 and they correspond to:

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Fully automatic solutions and approaches, such as automatics parsers, automatic detection or prediction methods or tools, all of which are referred to in Table A1.Because our goal is focusing on software assistance tools, we have not included the works whose main contribution is based on a full automation of tasks, both at the level of detection of discursive or argumentation structures and at the level of automatic generation of visualizations that do not allow interaction of the end user.

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Approaches based on some pieces of information considered discursive but that do not respond to an analysis of the complete structure of the discourse, such as application of topic modeling, statistical studies (basic analysis of frequencies of terms or similar descriptive statistics), works in metaphors, stems, taxonomies or ontologies, all of which are referred to in Table A1.Many of them also adopt an automation approach.
We present below the synthesis of evidence from our SLR.We begin with a general analysis of the results from the final SLR set.Next, we present the answers to the research questions previously defined.

Availability
It is important to highlight the small number of free-use tools available throughout the study area.Only 12 publications from the SLR intermediate repository (58 publications) present access at least to one demo for free or, in better cases, to software repositories or fully implemented free software tools (see last column in Table A1).Another small group of the rest could be accessed through institutional credentials, while most of them present a URL's with broken links in the publication, or they were never available online.

Visualization techniques employed
Regarding the visualization techniques used, most of them follow a principle of visualization based on the original text, many of which are based on discourse trees' [17] approaches or similar structures for argumentation.Some alternative proposals, such as conceptual recurrence plots [18] do not allow discursive or argumentation analysis based on the original text.The visualization is carried out after the textual analyses are completed and it is focused only on a specific metric (intervention turns, similarity between utterances, etc.).

Discursive and argumentation framework supported
Another interesting aspect extracting from the SLR is that most of the tools with a high quality score (score Qn = 5 or score Qn = 4) are particularly developed for supporting a specific framework of discursive or argumentation analysis, mainly RST-Rhetorical Structure Theory- [10] (such as [17,19]) or IAT-Inference Anchoring Theory- [20] (such as [21][22][23]), but also some ad hoc frameworks [24,25].Although, conceptually, these frameworks present similar ontologies (segments and relationships between them) and similar visual possibilities, most of the tools are conceived to assist in textual analysis using a single discursive or argumentation framework.This causes a dependency between the software tool and the pragmatic framework chosen, since the user must employ that framework to carry out their textual analyses.The dependence on a discursive or argumentation framework is repeating in all proposals.Thus, it is not possible to extend the theoretical framework for creating similar textual analyses with customization in discursive or argumentation schemas.

Sharing and reuse software mechanisms
Finally, we have analyzed the sharing and reuse mechanisms of the final 17 publications.Although most of these software proposals are focusing on the collaborative edition and analysis, only 8 software tools present sharing and reuse functionality, while 9 software tools reviewed the lack of any sharing and reuse planification of the information generated during the textual analyses.The most used mechanism is based on information exportation in standard file formats, mainly XML -eXtensible Markup Language -or derived formats (such as JSON, JavaScript Object Notation) as an interchange mechanism.Only the rstWeb tool [19] presents extra mechanisms allowing for the better reuse of the resultant analysis information.
In summary, this SLR shows that the discursive and argumentation aspects continue to present few software alternatives for textual analysis assistance, in comparison with the wide range of proposals in the lexical or grammatical levels of textual analysis, or in fully automatic approaches at any level.The main weaknesses detected in the existing software proposals are the dependency of one specific discursive or argumentation framework (problems to generalize the analysis), availability (problems accessing, using and keeping the software updated) and the lack of sharing and reuse mechanisms that allow for re-analyses, collaborative editing, and comparative reasoning of the textual analysis performed.The following section elaborates on these results, answering the research questions initially proposed.

RQ1: What evidence is there that discourse and argumentation textual analysis is currently supported via information visualization software?
Appendix A shows the SLR final repository, with all publications reviewed, their scores and availability.The SLR allows us to answer the RQ1 and RQ2 of this study, regarding evidence about visualization techniques that are applied to support discourse or argumentation textual analysis via software.While the lexical or grammatical levels of textual analysis are gaining in methods and software tools for their assistance, the discursive and argumentation aspects continue to present few software alternatives.There are only a few recent tools [19,[23][24][25] to assist in this textual analysis through an available information visualization software resource.
RQ2: What kind of support are these works providing and how is it implemented?
An interesting macro-analysis resultant of the SLR process corresponds to the kind of software support provided by the publications attending our categorization defined in RQ2.The Table A1 "Main contribution" column shows in bold the kind of software support contribution according to our seven categories defined for each publication.Note that, in some cases, one publication could present several contributions for different RQ2 categories, although the common scenario is that one publication focuses on one kind of main software support contribution.Thus, considering only the main category associated with each publication (the first category reported in Appendix A), the distribution of software support in the works reviewed is as follows: With a total of 58 works reviewed, the majority category (23 publications) presents complete support in the form of a software tool, although the main objective and functionality of each tool may differ.Subsequently, the works present support in the form of InfoVis techniques (12 publications), application examples (seven publications), automatic solutions (seven publications), linguistic resources (four publications), qualitative studies (three publications) and finally the measurement of the basic discourse metrics (two publications).The large number of complete software tools reviewed offers us an idea of he current interest in software support in discursive and argumentation textual analysis.However, as we have already mentioned in the results of the SLR process, many of these tools present the weaknesses detected, and most of them are not even available for evaluation or use.

RQ3: Does the software support offered in these works present any weakness or deficiency reported in the study itself or detected as a result of the review?
Regarding RQ3, we found some weaknesses in the current software tools, especially related to availability, framework independence and a lack of information sharing and reuse software mechanisms.Based on these weaknesses, four software guidelines are defined and exemplified in implementation through Viscourse.(See Section 4).
RQ4: Is it possible to identify some software guidelines for improving the existing information visualization software solutions for supporting or assisting discourse and argumentation textual analysis tasks?
Answering RQ4, Viscourse tries to act as a complementary software tool, allowing for the generalization of the discursive or argumentation analyzes thanks to their flexibility and independence from the segmentation method (free definition of segments or multi-phrase groups) or of the theoretical framework (RST or IAT, among others), also with a user-customizable visualization criteria (screen position, color palette, color criteria, etc.).Typical users of Viscourse can be mainly researchers in humanistic disciplines, but also teachers or students of discourse or argumentation areas.
In addition, Viscourse focuses on the sharing and reuse of the analytical information generated, thanks to a black-box mechanism that allows for the encapsulation of import/export formats to researchers, without losing sharing and reused capacity.The next section details the guidelines extracted and the solutions proposed as part of Viscourse implementation for the guidelines' implementation.

Extracted Guidelines in Practice
Considering the SLR results, we have defined a non-exclusive set of software guidelines for improving the existing information visualization software assistance provided to discursive and argumentation textual analysis: Textual granularity: Visual mechanisms should be added to change the level of granularity of the textual analysis.This means that the user must be able to change the visual focus of the analysis, being able to focus on the specific text paragraphs, phrases or other textual segments, or to raise the level of abstraction, calculating general metrics for the full text analyzed.

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Linguistic framework flexibility: Software mechanisms should be developed to allow an independence between the visual mechanisms and the specific discursive or argumentation framework used for the textual analysis, allowing for the extension of the software tools to future discursive or argumentation frameworks.Some guidelines here include separate conceptual modelling strategies for the visual solution and each specific framework applied for each analysis performed.

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Sharing and reuse mechanisms: Software mechanisms should be developed for allowing the sharing and reuse of the resultant informational pieces for the textual analyses in a transparent way for end users.These mechanisms are particularly useful both in future analysis by the same users and in a collaborative or comparative analysis by other researchers.Some guidelines here include black-box and transparent export/import mechanisms for the informational pieces produced during the textual analyses.4.
Availability alternatives: The software assistance provided should offer some availability and maintenance solutions.This does not necessarily imply free or open models of all the software tools, but rather a prior planning of availability mechanisms so that the learning effort made by users is rewarded.
How these guidelines could be implemented in a real software solution for assisting discursive and argumentation textual analysis?Taking into account previous works on discourse and argumentation studies via software [26][27][28], Viscourse tool [29] is a web platform for discursive/argumentation textual analysis.We detail in the next sub-sections our approach for implementing the guidelines presented above in Viscourse.

Textual Granularity
As a tool for supporting discursive/argumentation textual analysis, Viscourse is initially conceived for an analysis attached to the text.This implies that the user details the literal text to be analyzed, segmenting it and performing the analysis at the discursive or argumentative level.Although this is the most common use, Viscourse includes some mechanisms related to guideline 1 for ensuring the software support at different degrees of textual granularity.Specifically, two mechanisms have been implemented related to this guideline: First, the user can vary the level of granularity of the text by grouping the textual segments into larger units, called groups.This allows one to associate discursive or argumentation characteristics to large sets of text, even entire paragraphs.
Secondly, it is possible to hide the literal text of the analysis performed, showing the option "Simplified mode".At that time, all segments and groups acquire the same size and alignment, hiding the literal text to analyze and keeping only the name of the segment or group.This simplified view allows the user to abstract from the literal text, focusing on the discursive or argumentation relationships detected and facilitating the comparison between several analyses on the same text or the calculation of simple metrics (number of discursive relationships of a specific type, etc.).In the future, we plan to offer this automatic metrics calculation and comparative functionality implemented as a module in Viscourse.Figures 2 and 3 show the same textual analysis ("Bouquets in the basket" text form RST corpus) with different levels of granularity.
hiding the literal text to analyze and keeping only the name of the segment or group.This simplified view allows the user to abstract from the literal text, focusing on the discursive or argumentation relationships detected and facilitating the comparison between several analyses on the same text or the calculation of simple metrics (number of discursive relationships of a specific type, etc.).In the future, we plan to offer this automatic metrics calculation and comparative functionality implemented as a module in Viscourse.Figures 2 and 3 show the same textual analysis ("Bouquets in the basket" text form RST corpus) with different levels of granularity.

Linguistic Framework Flexibility
Our SLR showed that most of the current tools are particularly developed for supporting a specific framework of discursive or argumentation analysis, such as rstWeb in RST [10] or OVA in IAT [20].Despite the theoretical and linguistic approach differences between them, most of these frameworks base the discursive or argumentation analysis on the same needs in terms of software visualization structures: textual segmentation or grouping, and the definition of discursive or argumentation relationships between segments (or similar textual elements with greater granularity).

Linguistic Framework Flexibility
Our SLR showed that most of the current tools are particularly developed for supporting a specific framework of discursive or argumentation analysis, such as rstWeb in RST [10] or OVA in IAT [20].Despite the theoretical and linguistic approach differences between them, most of these frameworks base the discursive or argumentation analysis on the same needs in terms of software visualization structures: textual segmentation or grouping, and the definition of discursive or argumentation relationships between segments (or similar textual elements with greater granularity).Thus, guideline 2 defends that a software tool that supports this type of textual analysis should maintain a certain independence between the discursive/argumentation framework used during a certain textual analysis and the visual elements employed in the tool.This conceptual decoupling at the software tool level allows the future extension of support to perform analysis with future discursive or argumentation frameworks that may arise.
For implementing guideline 2, Viscourse initial version decouples visual mechanisms and discursive/argumentation information framework used.First, Viscourse allows for textual analysis based on the customized segmentation of the text by the user and its grouping into wider levels that also allow for analysis on a multi-phrase or paragraph level.Viscourse currently allows for the analysis of relationships between segments or multi-phrase groups, with highly customized features for the user, so any discursive and/or argumentation relationships framework can be used.
In addition, on a visualization level, the text follows existing approaches as discourse trees but adding color visualization for segments or multi-phrase groups.The color of the groups and the discursive or argumentation relationships during the analysis are also selectable by the user, which allows him to apply color criteria (it is common, for example, to use red for discursive and/or argumentation relationships with contrast or disagreement semantics).It is also possible to highlight only a specific type of relation or segment in the tool for visual clarification.Figure 2 shows an example of the well-known bouquets in a basket example from the RST corpus [10] performed using Viscourse.Viscourse natively implements the RST analysis [11,14] due to the applicability of the platform to ongoing projects.Besides, thanks to the customization possibilities in terms of segments, groups and relationships definition, it is possible to perform multiple analyses using different discursive or argumentation frameworks as a basis.

Sharing and Reuse Mechanisms
As our SLR showed, a few cases of the revised tools have automatic mechanisms for exporting the information generated during the analysis [19,21,[23][24][25], which allows some initial steps on sharing and reuse of this information.Most of these works use mechanisms that require knowledge of certain file exchange formats, in most cases requiring that the researcher must also edit the generated files.Viscourse also includes the classic import/export mechanisms through editable standard file formats from the web platform (Figure 4 shows the JSON editor included in Viscourse, with three options for file view: code, tree or a node view).This mechanism transforms Viscourse in a complete JSON editor for the discourse-based information produced during a textual analysis.Note that the tool allows the user to save multiple analyses in their user account, maintaining a visualization carrousel for each user.Each analysis performed is generating an underlined JSON file with information about the original textual sources, the segments, groups, relationships, etc., created by the user and color and position information that allow the software to replicate and export each analysis.
complete JSON editor for the discourse-based information produced during a textual analysis.Note that the tool allows the user to save multiple analyses in their user account, maintaining a visualization carrousel for each user.Each analysis performed is generating an underlined JSON file with information about the original textual sources, the segments, groups, relationships, etc., created by the user and color and position information that allow the software to replicate and export each analysis.However, in our experience, many of the analyses performed are reused by the researcher himself, or shared with colleagues for comparison or communication purposes, and many of these researchers do not need to edit the files generated by the applications for this purpose.For this reason, Viscourse also includes, following guideline 3, an import/export mechanism as a "black box" for the researcher, through its own Viscourse code mechanism.The implementation consists of encapsulating all the information produced during a textual analysis (information about the original text, the However, in our experience, many of the analyses performed are reused by the researcher himself, or shared with colleagues for comparison or communication purposes, and many of these researchers do not need to edit the files generated by the applications for this purpose.For this reason, Viscourse also includes, following guideline 3, an import/export mechanism as a "black box" for the researcher, through its own Viscourse code mechanism.The implementation consists of encapsulating all the information produced during a textual analysis (information about the original text, the discursive or argumentation information produced during the textual analysis and the visual decisions about colors, positions, etc., taken by the user during the analysis) in a black box piece associated with a unique code automatically generated.
The black-box import/export mechanism in Viscourse follows this workflow: • Once the textual analysis is finished, the user selects "share selected visualization" in the Options menu.An export message is shown.The user can generate an internal code for Viscourse that matches the JSON file created for the textual analysis with exactly their visualization and textual analysis parameters.

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The code generated is shown to the user, with automatic copy options.Sharing the Viscourse code with any Viscourse user, it is possible to import the textual analysis in any web Viscourse session.

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For importing, the user selects the "Import from Code" option near their visualization carrousel in the main screen.Pasting the Viscourse code is enough to reproduce the textual analysis performed and all their visualization options in other Viscourse sessions or user accounts.
Figure 5 visually summarizes this workflow, showing the different actions in the screen performed by the user in an exporting/importing operation.
• For importing, the user selects the "Import from Code" option near their visualization carrousel in the main screen.Pasting the Viscourse code is enough to reproduce the textual analysis performed and all their visualization options in other Viscourse sessions or user accounts.Figure 5 visually summarizes this workflow, showing the different actions in the screen performed by the user in an exporting/importing operation.

Availability Alternatives
As previously detailed, one of the weaknesses that our SLR analysis points out is the absence of tools currently available to the end user.Although it is not the objective of the paper to go into the many reasons why this can happen, we think that, as guideline 4 details, the prior planning of the software maintenance model and availability of the different tools to support discursive and

Availability Alternatives
As previously detailed, one of the weaknesses that our SLR analysis points out is the absence of tools currently available to the end user.Although it is not the objective of the paper to go into the many reasons why this can happen, we think that, as guideline 4 details, the prior planning of the software maintenance model and availability of the different tools to support discursive and argumentation textual analysis is necessary.Currently, we are conducting this prior analysis to provide Viscourse with different availability and use alternatives: free use, research licenses, free trials, subscription payment, availability to users in code repositories, etc.There are several alternatives to ensure that the work done is effectively available to its end users.

Future Steps
As it has been shown throughout this paper, the development of software assistance for textual analysis through information visualization techniques presents great challenges.It is also an area of vital importance for humanistic and social disciplines, where researchers present special needs in terms of textual analysis assistance as compared to other disciplines.Thus, the systematic literature review carried out here is a valuable contribution to the field, including heterogeneity references in terms of source repositories, disciplines and approaches and presenting a broad panorama of the area.
While we believe that the steps taken so far are satisfactory, future steps are planned.Our immediate work includes a formal validation of the Viscourse tool and their guidelines on implementation.It is necessary to evaluate the Viscourse software tool on a wide set of case studies, by its target users.An evaluation working with researchers in various branches of the digital humanities is planned, in order to identify weaknesses and strengths of the tool, obtaining assessment valorization by end users.
Regarding the connection of the Viscourse tool with the current works revised here, future work includes exploring the possible connection between Viscourse visualization, sharing and reuse mechanisms and current natural language processing (NLP) algorithms and approaches to the automatic extraction of linguistic information on a pragmatic level.The flexibility of Viscourse in terms of the discursive or argumentation framework used and their black-box sharing and reuse capabilities allow us to explore the use of Viscourse as a complementary piece of software for visualizing and interacting with outputs from different NLP discursive parsers.
Besides, the following steps in Viscourse tool improvements will focus on the implementation of a comparative visualization solution, which allows for comparing several analyses of the same text from different frameworks, or performed by different users, in the same interface.Adding comparative features to Viscourse will further enhance Viscourse's reuse and sharing capabilities.

Figure 2 .
Figure 2. Viscourse textual analysis for the "Bouquets in a basket" Rhetorical Structure Theory (RST) corpus text.

Figure 3 .
Figure 3. Viscourse textual analysis for the bouquets in the basket RST corpus text.Simplified mode.

Figure 4 .
Figure 4. Viscourse classic import/export mechanisms through editable JavaScript Object Notation (JSON) files from the Viscourse web platform, with three options for JSON file view: code, tree or a node view.

Figure 4 .
Figure 4. Viscourse classic import/export mechanisms through editable JavaScript Object Notation (JSON) files from the Viscourse web platform, with three options for JSON file view: code, tree or a node view.

Figure 5 .
Figure 5. Viscourse code, a black-box mechanism for sharing and reusing textual analysis information between users.

Figure 5 .
Figure 5. Viscourse code, a black-box mechanism for sharing and reusing textual analysis information between users.

Table 1 .
SLR repositories, search queries and the number of resultant publications.

Table A1 .
Quality assessment SLR phase results, evaluating 59 publications (58 publications eliminating replications) from 7 different source repositories.Gray marked rows constitute publications included in the final SLR repository (17 publications)."Main contribution" column shows in bold the kind of software support contribution according to our seven categories (defined in RQ2) for each publication.