How NLP Conduct Sentiment Analysis Improves User Satisfaction
Customer experience directly affects a company’s success. However, it can be difficult to gauge customer’s feelings and understand their needs. NLP sentiment analysis tools bridge the gap by helping service providers appreciate end users perspectives and better cater to their needs.
In normal human conversation, the same set of words could have different meanings depending on the tone of the speaker and context of the conversation. Sentiment analysis applies natural language processing (NLP) to determine the emotion behind communication from customers. Positive or negative sentiments can be recognized and responded to appropriately- with to reinforce positive sentiment, or to empathically resolve negative ones.
Social media is an amazing platform for many reasons, not the least of which being that it’s allowed for monitoring of real time reactions from clients and customers. Sentiment towards a particular brand, product or service can change over time, due to trends and sometimes for reasons that have nothing to do with the company. Either way, sentiment analysis can help keep track of these changes and show when to deploy interventions where necessary.
Social media, again, can be a gold mine for observing customers' sentiment. Complaints especially can easily go viral, but you can quickly get on top of brewing bad press by responding directly to the client and righting the perceived wrong. The customer will appreciate it, and bad press can easily become good press.
Through sentiment analysis, it’s possible to learn how your customers rate your customer service. Immediate post encounter surveys are a crucial tool here, and feedback can range from negative due to long response times or positive due to perceived friendliness of responses or ease of solution.
There’s always room for improvement, and customer reviews can shed more light on the parts of your product that need to be worked on. Minor bugs, missing features or design fails could be the issues that need working on.
Sentiment analysis can also be used for competitors' products- analysing general response to new products and trends can inform your company’s strategy in deploying similar products or services. Customers can also be classified and targeted accordingly- for instance, different demographic groups might have different sentiments about a specific product and would need wildly different marketing approaches.
This is the most common type of sentiment analysis. It detects the overall polarity of a text, whether positive (eg, ‘I love this product!’), neutral (eg, ‘I bought this product yesterday’) and negative (eg, ‘I did not enjoy using this product’)
This gives a more granular level of customer feedback, classifying responses int 5 tiers ranging from 1 star (very negative) to 5 stars (very positive). 3 stars is classified as neutral.
With this analysis, the particular aspect or attribute of the product that the customer is referring to can be determined. The user may be happy about one component but be dissatisfied with others. Aspect based sentiment analysis will help to determine that.
In normal human conversation, negative sentiments may be conveyed using positive sounding words. For example, a customer might review a product saying, “I just love how fragile this product is.” The SA model might interpret this as a positive comment, when in actuality, the patient is complaining.
Context is essential to correct interpretation of statements with multipolarity- for example, in surveys, different questions can elicit the same or similar responses. Sentiment analysis models can be limited in understanding the difference in sentiment as a result.
Subjective assessments can be difficult to interpret in contrast to objective assessments. A customer reviewing a piece of furniture they liked but was too big for the space available, for example, might present a challenge to the SA model.
There are many open-source libraries available to build personalised models for sentiment analysis. Examples include SaaS software (for example, MonkeyLearn, IBM Watson etc), TensorFlow, NLTK and SpaCy.
Isahit provides a technologically agile platform of artificial intelligence that allows for meeting the needs of our clients and great flexibility in overcoming the challenges of NLP conduct sentiment analysis. With an in-house tool augmented by human intelligence, our clients' needs are met. To find out more, don’t hesitate to contact us
Ethically scale your digital annotation projects with our highly trained workforce. Access our On-Demand Workforce to get the best quality in your Dataset Labeling.
We have a wide range of solutions and tools that will help you train your algorithms. Click below to learn more!