How can sentiment analysis help you identify and address negative reviews and feedback?

To effectively monitor your online reputation, it's important to use various text analysis tools. While sentiment analysis is useful, it shouldn't be used in isolation. Structural topic modelling is one such tool that can leverage metadata to cluster large amounts of textual data into identifiable categories. Combining sentiment analysis and structural topic modelling lets you quickly identify and respond to any negative feedback that requires attention.

Sentiment analyses can be carried out in more languages than one. In my experience tracking online sentiment in Pakistan, I needed the capability of a software to track sentiment across English, Urdu and Roman Urdu (Urdu typed out in English alphabets). This needed manual training for the monitors to understand and learn real-time what terms and sentences were to be considered negative, positive or neutral- although a gruelling process, reaped pretty good rewards for a powerful tool at the time. Nowadays we have local sentiment monitors that cater to local languages of all types through an automated machine-learning process with back-end support for any kinks and updates in the tool.

When responding to negative feedback, it's important to understand the emotions that are driving it. One effective tool for this is the NRC Emotion Lexicon, which can map reviews across eight emotions and positive or negative valence. With ChatGPT's help, you can visualize the top words and their counts to identify the triggering emotions. By considering these emotions, you can create a more effective resolution that addresses the customer's concerns and helps improve their overall experience.

You can also use sentiment analysis tools to detect spikes in your usual traffic of feedback- if you're seeing an unusual rise in negative mentions on social, it is likely connected to a product/service failure which needs to be addressed immediately. Some tools provide line graph visualization of which emotion is being detected in the spike, which keyterms are being used as a majority, which "influencers" have the most amount of reach for the topic and also which hashtags can be associated with it. This ensures efficient and timely crisis management

To improve your products, services, or content, you need to understand what your customers value most. By using sentiment analysis and structural topic modelling, you can analyze reviews from competitors to identify common themes and concepts. Using ChatGPT, you can then produce a list of value focus areas and map them to the NRC Emotion Lexicon. This will allow you to create actionable plans to address any value depletion or inversion and improve your competitive advantage.

Original Customer Insights is the holy grail for a solid sentiment analysis output. What are you doing if you're not actively listening to your customers and collecting authentic feedback? Venture need to utilize mechanisms and tools to collect, monitor, visualize, and analyze customer insights. A platform for customers to post reviews and tools like the Innovation Community Design and social media platforms can help. Having a customized space where you can identify real users is key. Successful ventures revolve around strong communication networks, and a presence on social media is crucial for building customer relationships.

When starting out with sentiment monitors, it is important to keep a close track of how well the tool is associating emotion to the context. Many times, mentions and comments are sarcastic or have multiple emotions going at once, which doesn't translate to the true "emotion". E.g. "I love this product but it's quality has dropped" - may be taken as a positive comment but in its essence is negative/constructive feedback. In that case, the sentiment of that particular comment can be manually changed, allowing the machine to optimize and learn real-time.