If you don’t know how to analyze customer feedback, you’re wasting a crucial resource. The success of your product largely depends on whether you can get actionable feedback and implement it into your product roadmap. Tools like sentiment analysis let you tap into users’ minds, achieve product-market fit, and get ahead of your competitors.
In this article, we’ll explore different types of sentiment analysis, describe how to use it on product reviews, and explain how you can leverage sentiment analysis to gain a competitive advantage.
What Is Sentiment Analysis?
Sentiment analysis, also known as opinion mining, is the process of extracting subjective information from text and determining the sentiment expressed within it. It involves analyzing text data, such as customer reviews, social media posts, or survey responses, to identify whether the sentiment conveyed is positive, negative, or neutral.
Sentiment analysis tools use natural language processing (NLP) techniques, machine learning algorithms, or lexicon-based approaches to classify and quantify the sentiment expressed in the text. The goal is to understand the overall sentiment of a given piece of text and extract insights that can be valuable for businesses, market research, social media monitoring, or customer feedback analysis.
Rule-Based Vs. Automatic Sentiment Analysis
When it comes to sentiment analysis, you can take a rule-based, automatic, or hybrid approach.
Rule-based sentiment analysis
In rule-based sentiment analysis, the software uses predefined rules to identify the sentiment expressed in a text. The rules come in the form of a word-list, or lexicon, with a set of words that express negative, positive, and neutral emotions. Each word is assigned a score, which is then used to determine the overall sentiment of the text.
Rule-based algorithms are easy to use and implement, however, you need to constantly update and expand your lexicons to make sure the algorithms don’t overlook any sentiment or misinterpret more complex wording such as double negation, sarcasm, or idioms.
Automatic sentiment analysis
You can conduct various types of customer feedback analysis using machine learning, including sentiment analysis. The process typically involves using machine learning algorithms to train a model on labeled data. The model uses large sentiment analysis datasets as input and teaches itself to reach the right conclusion. After a system trains repeatedly on known data with diverse examples, it can accurately identify sentiment in a new dataset and work out the final sentiment score of the input text.
Types of Sentiment Analysis
Depending on your needs and objectives, you can conduct traditional sentiment analysis or choose a specific type of analysis.
Aspect-based sentiment analysis
This type of analysis allows you to focus on a certain aspect of qualitative feedback and find out how your customers feel about different features and attributes of your product, such as price, weight, longevity, etc. While general sentiment analysis determines the overall attitude towards the product, aspect-based analysis can help you identify what’s driving the sentiment.
Fine-grained sentiment analysis
Sometimes, understanding whether a piece of feedback is positive or negative is not enough. Fine-grained sentiment analysis goes beyond binary classification and lets you uncover more complex sentiments and intensity levels. Similar to five-star rating systems, fine-grained analysis uses five sentiment descriptors – very negative, negative, neutral, positive, and very positive.
Intent-based sentiment analysis
Aside from the overall mood and emotions, sentiment analysis can also determine the intent behind a piece of feedback. Intent-based analysis can identify if the input text is a complaint, question, or suggestion. It can also gauge the intensity of the sentiment, which helps in prioritizing more urgent feedback or spotting customers with higher purchase intent.
Emotion detection sentiment analysis
This method of sentiment analysis allows you to explore the emotional state of the writer. Understanding whether the customer is surprised, disappointed, or frustrated can help create a product that would meet both their physical and emotional needs.
Customer Sentiment Analysis Data Sources
Before you get started with sentiment analysis, you need to make sure you’re analyzing honest high-quality feedback. Here are three sources you should consider using.
1. Survey platforms
Surveys are one of the best ways to gather constructive feedback. A well-crafted survey can give you a deep understanding of the customer’s experience and help you identify the strengths and weaknesses of your product.
For sentiment analysis, you need to come up with open-ended questions that focus on people’s thoughts and emotions and don’t lead the respondent to a certain answer. After the responses are gathered, the software can quickly identify their sentiment and intent, giving you an opportunity to boost user satisfaction.
2. Product review platforms
Review sites are another key source of qualitative customer feedback. Feedback on platforms like Trustpilot, Trust Radius, or G2 is unsolicited, so you can expect more diverse opinions and uncover areas of improvement that were not included in your surveys. Once you conduct sentiment analysis on your product reviews, you can visualize your findings, target your problem areas and track the changes in customer sentiment.
3. Social media
Social media contains a plethora of data and user insights. Social media sentiment analysis tools scan social media platforms for brand mentions and can identify the sentiment behind them very quickly. While you may find a variety of opinions and sentiments in social media reviews, they can be hard to verify, therefore it is better to validate such feedback with customer research before acting on it.
How To Choose Sentiment Analysis API?
To conduct customer sentiment analysis, most businesses use Python or customer feedback analytics software.
Python sentiment analysis
Python is widely used for sentiment analysis. Thanks to many open-source libraries and tools, you can build your own Python-based sentiment analysis tool. One of the key libraries is NLTK (Natural Language Toolkit), which provides free pre-trained models and algorithms.
Python’s ease of use, community support, and open-source libraries make it a popular choice for sentiment analysis. However, sentiment analysis in Python still requires a significant amount of work and model training while also being dependent on external resources and having limitations when it comes to large-scale tasks.
Feedback analytics software
Building a custom tool for sentiment analysis is time-consuming and often unnecessary. With sentiment analysis software like Essense, there’s no need for coding or lengthy training processes. Essence can analyze customer feedback, perform accurate sentiment analysis, pinpoint important feedback points, and generate a list of actionable product insights in minutes.
Customer Sentiment Analysis for Product Managers
Sentiment analysis is a valuable tool that can help companies identify emerging trends, explore new markets, analyze competitors, and discover untapped opportunities. While manual feedback analysis can be inaccurate and time-consuming, AI sentiment analysis solutions can quickly analyze thousands of pieces of feedback and generate insights that will help you stay on top of your user’s expectations.