“sentiment Analysis Tools For Australians: Gauging Market Mood For Profit” – Learn how text analytics software works and how you can use it to find breakthrough insights in unstructured data that take your customer, employee, brand and product experience programs to the next level. Written by Rohan Sinha, Chief CX Officer at .

Text feedback is the closest thing to a 1:1 conversation with every customer, every citizen and every employee. In free text, our customers can tell us what they really care about and why, without being limited by the questions we decide to ask them. It’s where customers can decide what’s most important.

“sentiment Analysis Tools For Australians: Gauging Market Mood For Profit”

However, internalizing ten thousand pieces of feedback is roughly equivalent to reading a novel and categorizing every sentence. It’s time-consuming, laborious, and hard to get the text to work. To effectively understand open-text feedback at scale, you need to either scale your team’s reading feedback or use a text analytics tool to uncover the most important parts and themes of the feedback. Let’s walk through the basics of text analysis together and give you some useful tools to consider.

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Text analysis is the process by which information is automatically extracted and classified from text data. In experience management, text can take the form of survey responses, emails, support tickets, call center notes, product reviews, social media posts, and any other feedback provided in free text as opposed to a multiple choice format options. Text analytics enables businesses to discover insights from this unstructured data format.

A powerful text analytics program can answer both of these questions—at scale—while keeping you connected to the voice of your customer and other actions that need to be taken.

The topics people talk about, but also whether they talk positively or negatively when they talk about such topics.

These are broad techniques that include all the other different ways of identifying emotion, intent, etc. It’s worth noting that some software claims to do emotion analysis from text – these tend to use a combination of words used in the text to arrive at the emotion.

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This can be somewhat misleading, as one could say “The flight was delayed” with anger, despair, joy (if they did something exciting at the airport), etc., but the text would never show the tonality or expression behind the sentence.

So using a combination of themes and sentiment from words is the only way to detect emotion rather than a catch-all algorithm.

When talking about text analysis, it is common for key terms such as text mining and text analysis to be used interchangeably – and confusion often occurs between the two.

There is much ambiguity in the differences between these two topics, so it may be easier to focus on the application of these topics than on their specific definitions.

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Text Mining is a technical concept that involves the use of statistical techniques to extract quantifiable data from unstructured text, which can then be used for other applications, such as MIS reporting, regulatory non-compliance, fraud detection or job application screening. Quantitative textual analysis is important, but it is unable to extract sentiment from customer feedback.

Text analytics, on the other hand, is a very business-focused concept that involves using similar techniques to text mining, but enhances them by identifying patterns, insights, sentiment, and trends for customer or employee programs. Text analytics focuses on discovering insights for action in specialized areas such as experience management.

Text analysis also includes natural language processing (NLP), also called natural language understanding. It’s a form of sentiment analysis that helps technology “read” or understand text from natural human language. Natural language processing algorithms can use machine learning to understand and evaluate valuable data, consistently and without any bias. It can be sophisticated enough to understand the context of textual data, even with complex concepts and ambiguities.

Therefore, it is very important to use specialized text analytics platforms for voice of customer or employee data, as opposed to the general text mining tools available.

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Text analytics has become an important part of many business intelligence processes, especially as part of experience management programs as they look for ways to improve customer, product, brand and employee experiences.

Before analyzing text, most businesses would have to rely on quantitative survey data to find areas where they can improve the experience.

While quantitative data is still essential to any program, it has its limitations in that it is limited to a predetermined set of responses.

For example, a telco might ask a typical customer satisfaction or CSAT question – “How satisfied were you with the service you received?” after a support call.

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A follow-up question from customer surveys might look to find out the reasons for customer satisfaction scores and could have options such as:

These options are limited and therefore limit the analysis that can be done for the score. For example, if the customer’s reason is not listed in these options, valuable information will not be captured.

It would be nearly impossible to list every possible reason in a customer survey, so including open-ended text feedback helps delve deeper into the experience.

Here, text analysis is key to identifying unknown unknown topics—topics that the business doesn’t know about but could be the cause of customer dissatisfaction.

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A better alternative is to ask an open-ended question about the reasons for the score – ‘Why did you give us this score?’

Applying survey text analysis techniques to this open-ended response then allows organizations to understand the themes that customers mention when they are dissatisfied, but also helps identify extremely negative versus less negative themes.

By being able to ask customers to say in their own words why they were or weren’t happy with the experience, you can better determine customer insights. Text analysis helps you be much more specific about the actions you need to take to improve their experience.

The ability to drive correlations between structured and unstructured data provides extremely powerful information for clear actionability.

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It is possible that there is a strong correlation between people who talk about employees providing clear explanations of next steps and high CSAT, or between those who talk about employees having good product knowledge and high CSAT.

And using text analytics techniques, this data can be easily organized and fed into your experience management program in the same way as quantitative data to gain deeper insight into what drives the customer, employee, brand or product experience.

By being able to see what people are talking about when they talk about the experience in their own words, and by being able to perform real-time sentiment and topic analysis, you can identify improvements that would otherwise go unnoticed using only qualitative data.

Text analytics is used in several different ways within Experience Management (XM) – if we break down XM into 4 pillars, we can see some of the most common use cases below:

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In textual analysis, one of the most common techniques for giving structure to such data is a process known as thematic modeling (sometimes referred to as categorization or taxonomy structures).

Here we explore what it is, how it works, and how to use it to analyze text responses in multiple languages.

”, although the words they use are different (“tariff” vs “price package”), they both refer to the same topic.

Topic modeling is a process that tries to merge different topics into a single understandable structure. It is possible to have a single-layer topic model where there are no groupings or hierarchical structures, but they usually tend to have multiple layers.

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This type of grouping of parent-child topics is usually referred to as a taxonomy, which involves grouping topics into broader concepts that make sense for a particular business.

A common example would be a parent topic such as “Staff Attributes” that contains various child topics (or sub-topics) such as “Staff Attitude”, “Staff Performance” and “Staff Knowledge”.

Taxonomy is essential in experience management as it can be used to report to relevant stakeholders and route feedback to the right teams and departments who can act on the insights gained.

For example, in a hotel business, the category ‘Personal Experience’ may be relevant to a Hotel Manager in terms of training, while Room Experience may be of specific interest to a Housekeeping Manager.

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Having a taxonomy is essential to getting the right information to the right people across the organization.

A topic model can have many levels or hierarchical levels. However, the best practice in experience management is to limit the model to two layers. Anything over two layers becomes extremely complex for a business user to understand and navigate, but more importantly, very tedious to build and maintain over time.

Another fundamental concept in topic modeling is the ability to have multiple topics for the same sentence or answer. This means that the topics must not be mutually exclusive. For example: “Losing my luggage caused extreme frustration.” can be split into two sentiment analysis topics at the same time – “Lost luggage” and “Emotion – Frustration”.

The theme model must be applicable to all languages ​​in which your company operates. I.e

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