{"id":37331,"date":"2022-08-05T10:06:51","date_gmt":"2022-08-05T14:06:51","guid":{"rendered":"https:\/\/centricconsulting.com\/?p=37331"},"modified":"2023-05-16T13:11:33","modified_gmt":"2023-05-16T17:11:33","slug":"sentiment-analysis-way-beyond-polarity","status":"publish","type":"post","link":"https:\/\/centricconsulting.com\/blog\/sentiment-analysis-way-beyond-polarity\/","title":{"rendered":"Sentiment Analysis: Way Beyond Polarity"},"content":{"rendered":"

Sentiment analysis is a data analytics process you can use to improve the customer experience by tapping into the emotional aspects of consumer decision making.<\/h2>\n
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Are your net promoter scores (NPS) stagnating or declining? Are you losing more customers than you are gaining?\u00a0 When you are experiencing these or other customer behaviors that are causing a negative impact on your business, the go-to solution is sentiment analysis.<\/p>\n

Sentiment analysis is a cornerstone offering of artificial intelligence and machine learning (AIML) disciplines. Humans are highly emotional beings, and emotions impact everything we do and provide the basis for future actions. Sentiment analysis allows software companies and corporations to put their finger on the pulse of customers’ feelings based on past or current interactions, conversations and observable behaviors<\/a>. Companies are capitalizing on leveraging sentiment analysis tools to understand customer behavior and emotional states better.<\/strong> For example, sentiments are good indicators for future customer engagement, churn and share of a customer\u2019s wallet.<\/p>\n

Sentiment analysis typically leverages textual data, be it emails, chats, social media posts or survey responses. It uses natural language processing<\/a> (NLP), natural language understanding (NLU), computational linguistics and text analytics to infer positive or negative attitudes: Do consumers say good or bad things about your brand and your products or service?<\/strong><\/p>\n

\"Sentiment<\/a>Companies have successfully used sentiment analysis to manage brand and reputation, recommender systems, content-based filtering, semantic search and understating users\u2019 or consumers\u2019 opinions and needs to inform product design, triaging customer complaints and influencing customer buying behavior.<\/p>\n

Let\u2019s take a closer look at sentiment analysis and why you need to use it.<\/p>\n

What is Sentiment Analysis?<\/h2>\n

Typical sentiment analysis tools offer an aggregation of polarity scores across various parameters. For example, you can determine the total number of spoken sentences with positive, negative and neutral reception and descriptive analytics dashboards on top of these indicators based on geographies and product segments. Based on the sign of the polarity score, you can often infer overall sentiment as positive, neutral or negative.<\/p>\n

These polarity scores range from -1 to 1, where -1 indicates the sentiments of the speaker toward the subject is highly negative, to +1, which indicates the sentiments of the speaker toward the subject are highly positive. This binary scale used among many popular sentiment analysis tools leaves us with a desire for a more granular scale to capture customer sentiments.<\/strong><\/p>\n

Here is an example graph of how things could look.<\/p>\n

\"Sentiment<\/a><\/p>\n

Looking broadly at how companies currently perform sentiment analysis, the volume of data available and produced, along with the performance gaps associated with typical sentiment analysis tools, businesses need a more comprehensive approach and scales for effective analysis.<\/p>\n

Over the course of our careers, we\u2019ve explored many of the popular sentiment analysis tools and platforms to find the best fit solution. Our endeavors consistently landed us with a negative sentiment polarity \u2013 this gave us the insights we need to build a better sentiment analysis process.<\/p>\n

Sentiment Analysis in Practice<\/h2>\n

A normal call center interaction, for instance, is several minutes in length and normally involves a customer experiencing a problem. This means the customer is somewhere between frustrated, angry and tense at the start of the interaction. Let us take an example of a conversation between a customer and a telecom operator regarding a billing issue where the first part of the conversation happened between the customer and the telecom call center operator, and the customer\u2019s problem remained unsolved. The caller then got transferred to a second-level support agent who was also unable to resolve the issue. The customer disconnected the call, dropped the service and moved to another provider.<\/p>\n

The call center uses one of the many sentiment analysis tools and platforms available on the market. Sentiment analysis tools generally offer a polarity score for an individual sentence in a long paragraph which tells us that X percent of a total number of positive sentences were positive sentences, Y percent of the sentences were neutral, and Z percent were negative.<\/strong> They do not tell us things like how the conversation escalated and how the customer\u2019s level of sentiment and emotions changed during the call leading to a lost client.<\/p>\n

Current out-of-the-box tools were unable to provide the call center with steady changes in the sentiments and customer emotion levels. But, these tools do offer dashboards that do descriptive analytics on the results. One common dashboard analyses arousal and valence. Arousal (or intensity) is the level of autonomic activation that an event creates and ranges from calm (or low) to excited (or high). Valence, on the other hand, is the level of pleasantness that an event generates and is defined along a continuum from negative to positive.<\/p>\n

If we look into the arousal versus valence quadrant analysis chart below, we will see that this interaction likely ended in either quadrant II or III \u2013 neither of which are preferred outcomes:<\/p>\n

\"Sentiment<\/a><\/p>\n

Using Sentiment Analysis to Discover Pain Points<\/h2>\n

Let\u2019s set the above findings aside for the moment and try to answer a few basic questions:<\/p>\n