Integrating sentiment analysis into your application or software helps work with human emotions and filter feedback faster or generate meaningful and helpful responses. Understanding what exactly customers want and their overview of your business is crucial but going through massive piles of reviews manually to discover this is time-consuming and not practical.
Sentiment analysis utilizes Natural Language Process (NLP) to analyze statements and classify them based on the emotions they evoke as positive, negative, or neutral. Sentiment analysis can process reviews, tickets, feedback, survey responses, job applications, and other types of content.
What is Sentiment Analysis and Its Types?
Sentiment Analysis includes the process of recognizing a positive, negative or neutral sentiment in written form content. Businesses and organizations use it to gather customer data, identify customers’ main pain points, or measure the reputation of a brand.
Sentiment analysis takes into account not only the specific vocabulary used by customers but also emoticons and punctuation signs to identify their feelings, intentions, and urgency. When running sentiment analysis, you should keep in mind the interpretation of statements and your specific goals. There are four types of sentiment analysis you can perform:
Emotion Detection analyzes statements to discover more than the polarity of emotions. It focuses on finding what feelings the statements convey, such as anger, sadness, frustration or happiness. In these cases, systems could use advanced machine learning algorithms or lexicons (lists of words that characterize a particular emotion). The only issue that arises with lexicons is the fact that customers could use a negative word for a positive experience and vice-versa.
“You’ve killed it with this product!” vs. “This software is killing my site’s loading speed!!”
Aspect-based Sentiment Analysis
For businesses or organizations offering services or products, aspects-based analysis comes at help for understanding how customers feel for a specific aspect of your product or service. An aspects-based analysis helps you distinguish between positive and negative statements based on certain features that you offer. For example, if you’re selling computers when a customer says “the laptop is constantly freezing” you can classify that as a negative review based on aspects-based sentiment analysis.
Intent Sentiment Analysis
The intent analysis focuses on discovering the intent that lies behind the statements. Customers might be simply browsing around or searching for solutions. Classifying their feedback based on their intent is important when considering retargeting them through ads or responding to their queries accurately.
Running Sentiment Analysis using Python
Python is widely used for its practicality to perform accurate sentiment analysis on dbt models or other types of models. However, all sentiment workflows begin with data loading and then its subsequent processes such as tokenizing, removing stop words, and normalizing.
- Tokenizing takes entire reviews or long paragraphs and chunks them down into sentences and words
- Removing stopwords like “but”, “and” or “if” facilitates the understanding and processing of data
- Normalization includes the standardization of forms of words into one form (using lemmatization or stemming)
Here is an excellent practical example of using Python to run a sentiment analysis on dbt models. It involves the use of Hugging Face transformers and Zendesk tickets to analyze the experience of customers while booking a hotel room.
Being able to identify the sentiment of customer statements will help you classify reviews and feedback for further improvement of your products or services. At Blue Orange Digital, we have implemented sentiment analysis to remove biases from the hiring process when reviewing applications among other cases. Our data scientists can devise systems that will aid your organization in scaling up or in making an accurate evaluation of itself. Simply book a short (and free) 15-minute consultation call here and let’s talk more in-depth about it.