Case Study: Sentiment Text Analysis

The client

“TasNetworks is a Tasmanian state-owned corporation that supplies power from the generation source to homes and businesses through a network of transmission towers, substations and powerlines.”

The objective

To uncover the sentiment and topic clustering hidden within the verbatim responses collected by the monthly ‘Satisfaction’ and ‘Ease Of Doing Business’ survey over a twelve month period.

The customer verbatim sentiment analysis

The textual data for each response was analysed for sentiment using a sentiment model which identifies the positive/negative/neutral polarity in the textual communication.

The local polarity of the different sentences in the text is identified, and the relationship between them evaluated, resulting in a global polarity value for the whole text for a participant’s comment. This was done on a comment by comment level and scores were aggregated to provide a monthly or total score.

GroupQuality Text Sentiment Analysis Case Study

The customer verbatim comment topic clustering

We identified the text clusters by grouping a set of texts in such a way that comments in the same group (called a cluster) are more similar to each other than to those in other clusters.

The clustering text algorithm receives a set of comments and returns the list of detected clusters.

Each cluster is assigned a descriptive (topic name), a relevance value (indicating the relative importance of the cluster when compared to all other clusters), how often this descriptive topic is found, and the list of text elements that are included in the cluster.  Each comment may be assigned to one or several clusters.

We then identified the scale and size of the sentiment and clusters based on the frequency and score. This provided an indication of those subject areas which were statistically more relevant than other topics.

We then drilled down on the customer comments to identify the motivations which drive the topics of interest and reveal those customer verbatim which summarised the key findings.

Meaning behind the data

The customer sentiment and scoring is driven by the customers contact moment (last experience), and recall of previous events. The in-depth investigation of the verbatim comments identified those contact moments and touch points which have the greatest impact on the satisfaction and ease of doing business scores.

We presented the topic clusters, highlighting those topics from a customers’ viewpoint that had the greatest effect on customer sentiment.

We were able to provide context for the survey data and highlight the areas which need attention and their priority. Clearly there was a lot more going on than was being reflected in the overall satisfaction and ease of doing business scores.

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