An organization’s success depends on its ability to keep its clients satisfied, as this leads to more loyalty.
How, however, can we know exactly whether our customers were satisfied?
In this article, we’ll explore what sentiment analysis is and how it can help us better spot pain points in every customer journey.
Definition of Sentiment Analysis
Sentiment analysis is a method of determining the emotional state of a text. Sentiment analysis is part of Natural Language Processing (NLP). A text’s overall positive, negative, or neutral tone is represented by a text’s Sentiment Score.
The different methods to measure the sentiment of a text
Sentiment scoring can be done in several ways, some of which are lexicon-based, others are based on Machine Learning, and yet others are hybrid approaches.
Lexicon-based method
A pre-built dictionary of words and the emotions that are connected with those words is used in lexicon-based approaches. These techniques are used to assess the mood of a piece of text.
Machine learning method
Methods that are based on machine learning require training a model. You can use use a dataset containing text that has been labeled in some way, such as movie reviews.
Hybrid method
Lexicon-based techniques and those that are based on machine learning each have their own distinct benefits; hybrid approaches combine these benefits.
Currently, many companies use the Net Promoter Score (NPS) to assess customer satisfaction and feedback data. New techniques and metrics are emerging to provide more precise outcomes. The Net Sentiment Score (NSS) is a new measurement that assesses consumer satisfaction using both social and survey data.
How do I calculate the Net Sentiment Score?
The Net Sentiment is an aggregated customer satisfaction indicator that is essential in the pursuit of an authentic and comprehensive voice of the customer. The Net Sentiment is determined by gathering text feedback from end customers.
Each data point is given a score of 1, 0 or 1- depending on whether it is good (1) or bad (-1).
Then a score is made by subtracting the number of negative comments from the number of positive comments. The result of the Net Sentiment Score is therefore expressed as a number ranging from -100 to 100, just like the NPS.
Net Sentiment Score= %Positive feedback – %Negative feedback
The relation between Sentiment Analysis and Feedback Data
Sentiment analysis and feedback data are two concepts that are closely connected and that are utilized together to get a better understanding of the perspectives and feelings held by end customers or users.
When used in combination with one another, sentiment analysis and feedback data will give an in-depth comprehension of the perspectives and feelings held by end customers. While sentiment scores may be used to get a rapid sense of the general feeling of the feedback, this data can be used to get a better understanding of the exact factors that contribute to a given emotion.
For example, if some respondents provide a negative sentiment score, the feedback data may be evaluated to determine the particular causes of the bad sentiment, such as unsatisfactory customer service or a faulty product.
Centralization helps you get better Sentiment Analysis.
Centralize all of your feedback on one powerful platform to get the most out of your feedback data. This allows you to improve your business and stand out from the competition. This platform should also enable you to analyze all the different silos of data in one location. In addition, it should be easy to use and helpful to every department in your company.
Feedier lets you centralize all of your feedback data from any source or platform via one dashboard that is clear and easy to understand. It also provides detailed analysis, including a sentiment score, benchmarking reports, correlations, and real-time reporting.
To summarize
Sentiment scores and feedback data are concepts that are closely connected to one another. They are often utilized together to get a better understanding of the views and feelings of end customers. Sentiment scores provide a concise summary of the overall sentiment. On the other hand, feedback data offers specific insights into the factors that led to a certain sentiment. Feedier believes that this combination enables companies to discover areas for development and make choices based on data.
