Survey text mining with IBM SPSS Text Analytics for Surveys, Part 1: Exploring sample survey data

Decipher a survey about touchscreen devices using default linguistic capabilities of IBM SPSS Text Analytics for Surveys

This two-part series of articles steps through the process of text mining by using IBM® SPSS® Text Analytics for Surveys, version 4.0.1. Part 1 describes the objectives of survey text mining and presents sample data of a survey for analysis. In a tour of survey analytics, explore the capabilities of SPSS Text Analytics for Surveys in a step-by-step manner. Every step shows you a bit of information about your sample data. Learn how to use SPSS Text Analytics for Surveys to completely decipher survey data.

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Bilal Siddiqui , Freelance consultant, XML4Java

Bilal Siddiqui is an electronics engineer, XML consultant, technology evangelist, and frequently published technical author. He is the founder of XML4Java.com, a company that is focused on simplifying e-business. After he graduated in 1995 from the University of Engineering and Technology, Lahore, Bilal began designing software solutions for industrial control systems. Later, he turned to XML and built web- and WAP-based XML processing tools, server-side parsing solutions, and service applications. Since 2006, he has focused exclusively on open source tools and solutions based on Java and XML technologies. A strong advocate of open source tools, he not only designs solutions that are based on them but also trains software and IT personnel at Lahore universities in using open source technologies. Bilal is the author of JasperReports 3.6 Development Cookbook (Packt Publishing, 2010).



03 June 2014

Also available in Russian

Objectives of text mining

Text mining is meant to extract meaningful information from large amounts of survey data. Text mining is especially useful when the survey data is in response to open-ended questions. In such surveys, responders give their comments or opinions in their own words. They can talk about whatever they feel, like, appreciate, disagree with, or complain about.

Text mining in survey analytics helps you extract meaning from what people say.

For example, consider an open-ended question that asks people for their comments about using touchscreen devices. Naturally, respondents can say whether they like or dislike touchscreen devices, they can mention something specific based on their experience, or perhaps they can suggest improvements.

This article steps through the process of using SPSS Text Analytics for Surveys to analyze and decipher survey data that includes answers to open-ended question about touchscreen devices. At the end of the analysis, you will know how many people like touchscreens and how many don't. You will also identify which features of touchscreen devices people are talking about, how many people like a particular feature, and how many dislike it.

Use software such as SPSS Text Analytics for Surveys to perform automated text mining and analytics when you must extract meaningful information from large amounts of data. The field of text analytics is an important aspect of big data applications.

Introducing sample survey data

The downloadable sample data is an Excel file that includes fictitious survey data. The Excel file has only one worksheet, in which survey data is organized in three columns. Figure 1 gives a view of the three-column headings and some rows of data.

Figure 1. Sample survey data
Screen capture shows three columns: respondent, answer, age group

The first column has a unique ID for each row of data. Each row represents one respondent who took part in the survey. The second column shows answers to the survey question and responses about touchscreen devices. Third column contains information about the age group of respondents who took part in the survey.

Loading survey data into SPSS Text Analytics for Surveys

As shown in Figure 2, when you run SPSS Text Analytics for Surveys you see a prompt that asks whether you want to start a new project or open an existing one.

Figure 2. The initial SPSS Text Analytics for Surveys prompt to start a new project or open an existing project
Screen capture of two icons: start a new project or type the file name for existing project

Click Start a New Project to select from various data sources. In this case, because an Excel file contains the survey data, select Excel from the list that is shown in Figure 3.

Figure 3. Selecting Excel from available data source options
Screen capture: Choose Excel from data source list

SPSS Text Analytics for Surveys presents a file browsing dialog where you enter the name of the Excel file and choose from available worksheets. Browse to the sample Excel file. Leave the Column names in first row option checked because the names of columns are included in the first row of the sample worksheet. Click Next.

Figure 4. Selecting the worksheet that carries sample survey data
Screen capture: Specify file name and file type

SPSS Text Analytics for Surveys now presents another dialog box, which asks some questions about columns in your Excel file.

Figure 5. SPSS Text Analytics for Surveys asks about columns of data in your Excel file
Screen capture: Select variables for survey analysis

In Figure 5, you can see that SPSS Text Analytics for Surveys loaded column headings Respondent ID, Q1: Comments about using touchscreen devices? and REF1: Age group. These column headings must be copied into three fields that are shown on the right in Figure 5Unique ID, Open Ended Text and Reference. Select the first column heading (Respondent ID) and click the right arrow next to the Unique ID field. The column name is copied into the variable. Similarly, copy the other two variables, as shown in Figure 6.

Figure 6. Specify to SPSS Text Analytics for Surveys the meaning of each of the columns
Screen capture: Specify unique ID, open-ended text, reference

Some column names (such as D, E, F) are still available on the left to be copied into variables that are shown on the right. But these columns are not available in the sample Excel file. SPSS Text Analytics for Surveys supports multiple questions and references in one analysis, so there is room for more columns. However, the sample Excel file contains only one question and one reference. Therefore, ignore the extra column names and click Next.

SPSS Text Analytics for Surveys asks whether you want to translate your sample data into English. Since the data is already in English, click Next. SPSS Text Analytics for Surveys presents a Category and Resources dialog.

Figure 7. The Category and Resources dialog
Screen capture shows where to copy resources from

SPSS Text Analytics for Surveys comes bundled with linguistic resources that you can use to start your analysis. You can build and reuse your own resources in the form of libraries.

Start analyzing the sample survey data by using the resources that come bundled with SPSS Text Analytics for Surveys. Part 2 of this article describes how to use this initial analysis to develop your own touchscreen-specific linguistic resources. For the moment, leave the Resource Template option selected in Figure 7. This option specifies that the default SPSS Text Analytics for Surveys resources (without any specialized libraries) are to be used for analysis. Click Finish. SPSS Text Analytics for Surveys starts analyzing sample data, a process called concept extraction.


Analyzing results of concept extraction

A concept in SPSS Text Analytics for Surveys is a word that SPSS Text Analytics for Surveys engine deems to be important in analysis, along with all synonyms of the word. SPSS Text Analytics for Surveys takes some time to extract concepts from your sample touchscreen survey data. When the extraction process is complete, you see a window as shown in Figure 8.

Figure 8. SPSS Text Analytics for Surveys brings initial results of extraction
Screen capture: List of extractions and responses that include them

The window of Figure 8 is horizontally divided into two; the right portion shows responses from the sample data in the Excel file. The left portion is vertically divided into two parts. The upper-left part is to show categories, which are described and built in Part 2 of this article. The lower-left part shows the results of extraction. This first article of the series focuses on the right (the response window) and lower-left (the results window) parts of Figure 8.

Look at the results window. This window shows the concepts that SPSS Text Analytics for Surveys extracted. Many concepts were extracted, for example, touch, touchscreens, like, greater, cool.

SPSS Text Analytics for Surveys engine produced this list of concepts by using its default linguistic resources. As you proceed in analyzing these initial results, you will find ways to enhance the linguistic capabilities of SPSS Text Analytics for Surveys to reach more meaningful analysis of the sample survey.

Exploring concepts that are generated by SPSS Text Analytics for Surveys

Each concept in Figure 8 is followed by a number in brackets (for example, "76" is in brackets after the first concept touch). The number indicates how many responses contain the touch concept. In this case, 76 responses contain word touch and its synonyms.

Select touch to see responses that contain the touch concept, as shown in Figure 9 in the right half of the window, which is the response window.

Figure 9. Responses that contain the touch concept
Screen capture with the word 'touch' highlighted

The response window shows individual responses that contain the concept you selected in the results window. The response window has four columns. The leftmost column counts the responses. The second column gives the ID of the response from the original Excel file. In this article, I use IDs to refer to individual responses. The third column contains actual responses. The rightmost column displays categories, which are described and built in Part 2 of this series. This column is therefore empty for the moment.

Notice from Figure 9 that SPSS Text Analytics for Surveys highlights all 76 occurrences of the touch concept, thus making it is easier to find the responses in Figure 9 that use the word "touch" and no synonym.

In addition to highlighting the particular concept that is displayed, SPSS Text Analytics for Surveys renders all concepts in color codes to make them identifiable in the response window. For example, in Figure 9 notice that bigger is displayed in purple in four places (response IDs 21, 46, 59, and 88). The word "bigger" is included in a concept named greater, which is also displayed in purple as the fourth concept from the top in the list of concepts that are shown in the results window.

Now click the second concept touchscreens, which is contained in 64 responses.

Figure 10. Responses that contain the touchscreens concept
Screen capture with the word 'touchscreen' highlighted

You can see from Figure 10 that the touchscreen concept uses two words touch screens and touchscreens as synonyms displayed highlighted in response window.

Notice from the responses that are shown in Figure 9 and Figure 10 that respondents are using touch and touchscreens interchangeably with the same meaning. Although "touch" and "touchscreens" are not synonyms in normal English vocabulary, within the context of a discussion on touchscreen devices, the two words refer to the same concept. Therefore, it is better to combine touch and touchscreens into one concept. With SPSS Text Analytics for Surveys, you can configure your own linguistic resources, a function that is especially helpful in analyzing domain-specific terminology.

Part 2 defines a domain-specific library of linguistic resources and describes how to configure domain-specific synonyms.

The next concept in the list is like. Responses included in the like concept are shown in Figure 11.

Figure 11. Responses that contain the like concept
Screen capture with the word 'like' highlighted

The responses that are shown in Figure 11 include several words such as prefer, I love, and I like in the like concept. And responders like various things. Some like gestures and others like zooming, touch displays, and other features.

From a survey analytics viewpoint, the touch, touchscreens and like concepts do not reveal much unless you also know how many of the respondents like which features and how many like touchscreens in general. You can get more specific information by going through extraction results, step-by-step.

The data on the like concept indicates only that a noticeable number of survey responders are expressing positive sentiments about touchscreens and about features such as gestures and zooming.

The next concept, greater, contains 15 responses as shown in Figure 12. A quick view of the responses indicates that most people are mentioning size of touchscreen devices.

Figure 12. Responses included in the greater concept show that responders are mentioning size of touchscreen devices
Screen capture with the word 'bigger' highlighted

The next concept is cool with another 15 responses. Similar to the like concept, responders are expressing positively about various features such as zooming, the size of touchscreen display, and messaging.

Figure 13. Responses mentioning the cool concept show that responders are mentioning various features
Screen capture with the word 'cool' highlighted

The next concept is fingers. A quick glance at the responses shows that responders are mostly enjoying the use of fingers for zooming and typing on touchscreen devices.

Figure 14. Responses showing that most users like zooming and typing with fingers on touchscreen devices
Screen capture with the word 'fingers' highlighted

The next concept is easy to use, where responders appreciate the ease of using touchscreens. SPSS Text Analytics for Surveys considers all occurrences of three-word terms ease of use and easy to use as synonyms.

Figure 15. Responses that appreciate the ease of using touchscreen devices
Screen capture with the phrase 'ease of use' highlighted

Further down the list of concepts in Figure 8 you find two more concepts, easy (with eight responses) and user-friendly (with seven responses), which are mentioning the ease of using touchscreen devices. Therefore, within the context of this touchscreen survey, it is appropriate to combine easy and user-friendly with the easy to use concept.

Figure 16. Responses that use the concept easy
Screen capture with the word 'easy' highlighted
Figure 17. Responses that talk about user-friendliness
Screen capture with the phrase 'user friendly' highlighted

The next concept after easy to use is touch gestures with 14 responses (see Figure 8), which indicates that respondents like their experience of using hand gestures.

Figure 18. Responses mentioning gestures
Screen capture with the phrase 'touch gestures' highlighted

Within the context of this touchscreen survey, touch gestures is used interchangeably with the fingers concept that is described earlier in Figure 14. It is better to combine the two concepts together as a small step in compiling analytic results.

Gestures are a feature of touchscreen devices and SPSS Text Analytics for Surveys indicates that survey responders find this feature worth mentioning.

Browsing through concepts further down the list in a similar manner, you find that another pair of concepts, accurate and precise, are better combined into one concept. Both of these concepts mention the accuracy of touch devices (some criticizing and others appreciating), as shown in Figure 19 and Figure 20 below.

To meet survey objectives, you need to segregate how many respondents appreciate the accuracy and how many criticize.

Figure 19. Responses that use the concept accurate
Screen capture with the word 'accuracy' highlighted
Figure 20. Responses mentioning precision
Screen capture with the word 'precise' highlighted

Another pair of concepts, excellent (with 12 responses) and good (with nine responses), is also a candidate to be combined into one concept. Responses in these two concepts appreciate different aspects and features of touchscreens, similar to the like and cool concepts described earlier. You need to find out what is good and excellent in the eyes of respondents.

Figure 21. Responses mentioning excellent
Screen capture with the words 'great' and 'best' highlighted
Figure 22. Responses that find something is good
Screen capture with the words 'nice' and 'good' highlighted

You see another concept that is named displays, whose responses are shown in Figure 23. A quick glance tells that people are talking about the display of touchscreen devices, especially the display size.

Display size is another feature that SPSS Text Analytics for Surveys found in the survey data.

Figure 23. Responses that talk about displays
Screen capture with the word 'display' highlighted

In addition to the positively expressed concepts, you can also find negatively expressed concepts, such as not clear and wrong. Responses included in the not clear concept are describing precision of touchscreens, as shown in Figure 24. Although inaccurate and not clear don't appear to be synonyms, SPSS Text Analytics for Surveys combined some of the responses that express unhappiness about the precision of touchscreen devices.

Figure 24. Negative responses
Screen capture with 'not accurate' and 'not precise' highlighted

Most of the responses included in the concept wrong are referring to the inaccuracy of touchscreen devices. Therefore, not clear and wrong can be combined into one more meaningful concept to wrap all responses that complain about inaccuracy.

Figure 25. Negative responses included in the wrong concept
Screen capture with 'wrong' and 'inaccurate' highlighted

Similarly, the concept social media includes responses about staying connected by using touchscreen devices.

Staying connected is an application of touchscreen devices. SPSS Text Analytics for Surveys indicates that users appreciate staying connected by using touchscreen devices.

Figure 26. Responses included in the social media concept
Screen capture with 'social media' is highlighted

Up to this point, the concepts that are extracted by SPSS Text Analytics for Surveys rely on default functions. You can draw some basic conclusions from the results of this analysis of sample survey data.

  • The survey data includes responses that use concepts such as like, cool, easy to use, accurate, excellent, precise, and good. These concepts are positive expressions that show that someone likes or appreciates something.
  • Responses indicate that hand gestures and staying connected on social media are prominent applications of touchscreens.
  • The size of touchscreen devices is an important feature to respondents.
  • You need to know how many responses speak in favor of which features or applications. Similarly, you also need to know how many responses generally like touchscreen devices, without mentioning a particular feature or application.
  • You discovered negative comments that express dislike of touchscreen devices. You need to know how many responses mention dislike of particular features or aspects of touchscreen devices.

Positive expressions use the concepts cool, easy to use, and accurate. SPSS Text Analytics for Surveys provides a feature to combine concepts in the form of types of words. All concepts that have something in common with each other can form a type. For example, the type Positive can include all the positively expressing concepts.

Similarly, another type TouchFeaturesNApps can combine concepts such as touch gestures, displays and social media.

Building on this understanding of concepts, which are synonyms, turn your attention to types of words, which hold synonyms together.


Working with types of words

SPSS Text Analytics for Surveys combines concepts into types of words automatically during the extraction process by using its built-in linguistic resources. Explore the types that SPSS Text Analytics for Surveys generated from the sample survey data.

Use the drop-down list immediately above the list of concepts in the results window to switch the results window from showing concepts to showing types, as shown in Figure 27.

Figure 27. Switching from concept to type view
Screen capture shows how to select Type under Concept drop-down list

Click Type from the Concept drop-down list. SPSS Text Analytics for Surveys shows the list of types that it generated during the extraction process, as shown in Figure 28.

Figure 28. The list of types that SPSS Text Analytics for Surveys generated by using its built-in linguistic resources
Screen capture of list includes Unknown, Positive, Contextual, ...

Figure 28 shows that the first type is Unknown. This type contains all concepts that SPSS Text Analytics for Surveys cannot fit into any type. Set aside the Unknown type temporarily to focus on the known types.

The second type in Figure 28 is the Positive type. Expand it to view the list of concepts included.

Figure 29. Concepts included in the Positive type
Screen capture with 'nice,' 'accuracy,' and 'convenience' highlighted

All of the positively expressed concepts that were identified earlier (like, cool, easy to use, accurate) are included in the Positive type.

Similarly, notice in Figure 28 that a built-in type named Negative holds the negatively expressed concepts noted earlier (not clear and wrong). Concepts included in the Negative type are shown in Figure 30.

Figure 30. Concepts included in the Negative type
Screen capture with 'improvement,' 'mistake,' and 'not accurate' highlighted

Positive and Negative types are a bit more helpful than concepts because these types indicate the total of positive responses and the total of negative responses. Many survey analytic projects require counting and analyzing positive and negative expressions in a meaningful way. SPSS Text Analytics for Surveys includes the built-in types Positive and Negative to save time.

To determine which concepts SPSS Text Analytics for Surveys cannot fit into any type, look at the concepts included in the Unknown type, as shown in Figure 31.

Figure 31. Concepts included in the Unknown type
Screen capture with miscellaneous responses listed

Concepts specific to touchscreens (touch, touchscreens, fingers, touch gestures, displays, and others) are included in the Unknown type. SPSS Text Analytics for Surveys comes bundled with linguistic resources that are applicable to English dictionaries (and to dictionaries of other languages that SPSS Text Analytics for Surveys supports). Therefore, it can determine how to apply the types Positive and Negative to terms that are assumed to be positive or negative. However, SPSS Text Analytics for Surveys cannot build types that cover touchscreen-specific terminology.

Concepts such as fingers, touch gestures, displays, and social media are features and applications of touchscreen devices. Therefore, it is appropriate to make a new type TouchFeaturesNApps and assign all such concepts to it.

Part 2 covers how to configure the TouchFeaturesNApps type to combine into one category the features and applications of touchscreen devices that respondents found worth mentioning.

Although types are helpful because they bring together similar concepts, types do not go far enough to yield the needed insight. SPSS Text Analytics for Surveys must also indicate how many respondents like or dislike a particular feature. Combine SPSS Text Analytics for Surveys concepts and types to find more insightful answers to these questions.


Patterns of concepts

To find insightful answers, SPSS Text Analytics for Surveys gives a framework of linguistics that is called patterns of concepts or concept patterns.

Concept patterns are essentially a combination of one concept (touch gestures, for example) with a type (Positive, for example). If you can count responses that mention touch gestures and cool, you can determine how many responses appreciate touch gestures.

This technique of combining concepts and types gives more insight into the sentiment of people who take part in a survey. Therefore, the technique is part of the field of analytics referred to as sentiment analysis.

The next step is to dig deeper into sentiments that are expressed in the survey data. SPSS Text Analytics for Surveys automatically generates concept patterns during extraction by using its default function. It also enables users to configure their own concept patterns. First, look at the default function of SPSS Text Analytics for Surveys.

Click the same drop-down list Type that you used to display types earlier in Figure 27. This time, select Concept Pattern, as shown in Figure 32.

Figure 32. Displaying list of concept patterns
Screen capture of the All extractions tab > Type > Concept Pattern

Notice the concept patterns that SPSS Text Analytics for Surveys generates automatically by using its built-in linguistic resources, as shown in Figure 33.

Figure 33. Concept patterns that SPSS Text Analytics for Surveys generates
Screen capture of combinations of types and concepts

The first concept pattern in the list is touch + . (with 45 responses), which means 45 responses contain either just one concept (touch) or touch with other concepts that SPSS Text Analytics for Surveys does not think make a pattern with touch.

Similarly, the second concept pattern is touchscreens + . You can expect that after you combine the touch and touchscreens concepts into one, the two concept patterns automatically merge. However, start by looking at the responses included in touch + . patterns in Figure 34 and touchscreens + . patterns in Figure 35.

Figure 34. Responses included in the touch + . concept pattern
Touch is highlighted
Figure 35. Responses included in the touchscreens + . concept pattern
Screen capture with the phrase 'touch screens' highlighted

The touch + . and touchscreens + . patterns contain messages of mixed nature, including almost all features, along with positive and negative comments. These two patterns are not much help in the analysis.

The third pattern is touchscreens + <Positive> with 23 responses. A quick scan of the responses (Figure 36) shows that almost all responses say something positive and good about touchscreens.

Figure 36. Responses included in the touchscreens + <Positive> pattern
Screen capture of positive comments such as Touchscreens are nice, good, ...

This point is important in the analysis. It shows one clear opinion in the survey data along with the count of respondents who share the opinion.

Now expand the touchscreens + <Positive> pattern to find out how SPSS Text Analytics for Surveys formed this concept pattern.

Figure 37. Combinations included in the touchscreens + <Positive> pattern
Screen capture of responses with positive words about touch

SPSS Text Analytics for Surveys combined the concepts included in the Positive type (good, precise, user-friendly, and other types), one by one with touchscreens. For example, the first combination in Figure 37 is touchscreens + good, which includes responses that contain simple statements like touchscreens are nice or touchscreens are good.

Figure 38. Concepts included in the touchscreens + good combination
Screen capture with 'touchscreen' and positive words highlighted

This way the touchscreens + <Positive> pattern as a whole indicates the count of responses that appreciate touchscreens. Each individual combination within the touchscreens + <Positive> pattern indicates whether responders find touchscreens good or cool or user-friendly.

Notice the last combination in touchscreens + <Positive> pattern in Figure 37. The combination is touchscreens + reasonable in which one response from ID 89 says that touchscreens are reasonable in accuracy.... This combination is explained later in this article.

Figure 39. The last combination in touchscreens + <Positive> pattern with just one response
Screen capture with 'Touchscreens are reasonable' response

You can expect that after you combine concepts such as precise and accurate, corresponding combinations such as touchscreens + precise and touchscreens + accurate automatically merge.

Similarly, the touch + <Positive> and touchscreens + <Positive> patterns also merge when you combine the touch and touchscreens concepts.

To take the analysis further, the fifth concept pattern in Figure 33 is touch gestures + <Positive>, which includes 13 responses. You can easily see the advantage of concept patterns. You have an automatic count of positively expressed responses about touch gestures. You need this information: how many responses indicate positive feelings about a specific feature?

Check the responses by clicking the touch gestures + <Positive> pattern. You can see that SPSS Text Analytics for Surveys accurately chose responses that positively appreciate gestures, as shown in Figure 40.

Figure 40. Responses and combinations included in the touch gestures + <Positive> pattern
Screen capture of responses and combinations included in the touch gestures + Positive pattern

Similarly, check the responses in the next pattern touchscreens + <Negative> and you find that once again SPSS Text Analytics for Surveys correctly chose the responses that indicate negative feelings about touchscreen devices.

Figure 41. Responses and combinations included in the touchscreens + <Negative> pattern
Responses and combinations included in the touch gestures + Positive pattern

Readers can continue exploring other concept patterns of Figure 33. Consider more closely an example that shows how SPSS Text Analytics for Surveys is organizing responses in concept patterns. Figure 42 shows responses of the seventh pattern (easy to use + .) in the list.

Figure 42. Responses included in the easy to use + . pattern
Screen capture of responses included in the easy to use plus pattern.

You can see that most of the responses in the easy to use + . pattern are simple expressions such as easy to use and ease of use. It is simple to understand that such responses need to be included in the easy to use + . pattern because they don't say anything other than easy to use.

Now look at the complex response from ID 89 in the same Figure 42. It says that touchscreens are reasonable in accuracy and ease of use. Recall that the same response ID 89 was also included in touchscreens + <Positive> pattern (the only response in the touchscreens + reasonable combination that you saw earlier in Figure 39).

This example shows that SPSS Text Analytics for Surveys makes simple concept patterns. When it finds touchscreen is cool or touchscreens are easy to use, it assigns them to touchscreens + easy to use combination. Similarly, when it finds touchscreens are reasonable, it assigns the response to touchscreens + reasonable combination.

However, when you assign the touchscreens are reasonable response, SPSS Text Analytics for Surveys also considers that the response contains more text ... in accuracy and ease of use and the additional text contains two concepts accuracy and ease of use. Therefore, it also assigns the same response to accurate + . and ease of use + . patterns.

Other patterns are not covered in this article. Download the trial version of SPSS Text Analytics for Surveys and explore all the concepts, types, and patterns that are available.


Conclusions

SPSS Text Analytics for Surveys builds analytic features, one on top of the other. The most basic feature is a concept, which collects synonyms of a word. The next feature is a type of word, in which SPSS Text Analytics for Surveys collects concepts that have something in common.

Concepts and types give you an idea of how survey respondents feel about a topic. Concepts that are extracted by SPSS Text Analytics for Surveys provide a good point to start the analysis of your survey data.

SPSS Text Analytics for Surveys combines concepts and types into concept patterns, a powerful way to proceed in your analysis. Concept patterns provide insight into the sentiments of responders who take part in a survey. With concept patterns, you can learn how many respondents like or dislike something that is discussed in the survey.

SPSS Text Analytics for Surveys provides powerful analytic capability that is based on linguistic resources that come bundled with SPSS Text Analytics for Surveys. With SPSS Text Analytics for Surveys, you also can build your own domain-specific resources.

You can start your analysis with default resources of SPSS Text Analytics for Surveys. It is a good idea to make a note of domain-specific points while you explore results based on built-in functionality of SPSS Text Analytics for Surveys. Later you can use the points to configure your own domain-specific linguistics.

Part 2 explores more features of SPSS Text Analytics for Surveys, explains how to configure your own linguistic resources, and describes how far domain-specific resources go in fine-tuning the basic analytical conclusions that are made here.


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