What is Sentiment Analysis in Python?

Sentiment analysis is a process for determining the human emotions behind text. By developing algorithms to extract the “sentiments” behind online reviews and responses, you can gain objective insights into your customer opinions and guide improvements as needed. When working with customers, understanding how they feel about your product helps ensure you’re listening to your audience and adapting to create the best possible experience. 

With Python, you can analyze and integrate insights from various channels, including customer reviews, chatbot text, and social media posts. Learn the basics of what sentiment analysis in Python is, different types, and examples of how you can apply this technology as you explore how these benefits could benefit your organization.

What is sentiment analysis in Python?

In Python, a sentiment analysis is a form of text analysis that you can use to identify and interpret the emotional tone (and subjective meaning) behind words. While basic text processing extracts factual information, like summarizing a report, sentiment analysis aims to understand how something is being said. Sentiment analysis focuses on the intent or attitude. With Python, you can use a range of libraries (like VADER and TextBlob) and pre-trained models to build sentiment analysis algorithms more easily. 

For example, if a customer wrote “Having this restaurant near me is dangerous,” a basic text analysis would likely interpret the information at face value (such as a customer finds restaurant proximity dangerous). In contrast, a sentiment analysis could interpret the context. A sentiment analysis would reveal a positive underlying emotion, as the customer is using humor to express “concern” over visiting too often due to enjoying the food. You can streamline this type of analysis in Python, where you can analyze large volumes of data to get a realistic interpretation of your customers’ feelings.

Types of sentiment analysis

Consider how granular you want the emotional classification of your text to be as you choose between the various types of sentiment analysis. You can also choose different methods depending on your overarching goals and what your company prioritizes at that time. Example options include:

Fine-grained sentiment analysis

With this method, you assign scores on a detailed scale that goes beyond just “positive” or “negative.” You might use a rating scale from 0 to 100, and use this rating to classify sentiments as “very positive,” “positive,” “neutral,” “negative,” and so on. For example, if a customer said, “The movie was fantastic, but the movie theater seats were a little bit uncomfortable,” the sentiment might earn an overall “moderately positive” rating by the algorithm.

You can use this type of sentiment analysis for overall product reviews or customer feedback. A classic example is the five-star rating system, which uses the average score of reviews to understand the customer experience. 

Aspect-based sentiment analysis

Aspect-based sentiment analysis focuses on customer sentiment toward a specific aspect or feature of your product rather than the whole text. In the previous example, the algorithm might detect a very positive sentiment toward the movie and a negative sentiment toward the movie theater seats. 

If you are concentrating on a particular product or service, this type of analysis can help you focus your efforts in a specific direction. 

Intent-based sentiment analysis

In this case, you’re looking at the emotional tone and the message's intent. It can help you determine the “point” of your customer’s message. If a review said, “You should increase the screen size,” the intent would be to provide a suggestion. This could help you categorize and focus on messages that provide actionable insights. 

Other categorizations you might find include questions, desires, complaints, and opinions. If you notice a particular segment of your audience has repeated desires or complaints, this can help you improve marketing efforts, make adjustments, and provide appropriate explanations.

Emotional detection

Emotional detection goes a step further and aims to define the underlying emotion, such as happiness, sadness, confusion, or frustration. This type of analysis can help you gain a more nuanced understanding of your audience's emotional state. 

If you launch a new product, understanding differences in audience feelings beyond “positive” and “negative” regarding a new feature can provide important insights. For example, knowing customers are excited rather than just satisfied can highlight strong engagement, while understanding customers are frustrated rather than disappointed may point to areas for usability improvement.

What can you use sentiment analysis in Python for?

You can use sentiment analysis in Python to improve your overall brand presence, audience engagement, and customer satisfaction. Popular use cases for this include:

• Market research: Use sentiment analysis to spot trends and find opportunities to meet customer needs.

• Build brand presence: Use it to understand how customers view your brand, what’s working, and areas for improvement in your company image. 

• Customer support: Identify areas of customer frustration, including the level of urgency, to better support customer requests and address comments. 

• Customer segmentation: Find which customer segments have the strongest opinions and find opportunities to address the needs of specific target populations.

• Product improvement: Use customer recommendations and feedback to improve product features and add additional concepts to your designs.

• Social media monitoring: Track what users say about your products and organization on online platforms like Facebook, Twitter, and Instagram.

Sentiment analysis vs. NLP

Sentiment analysis is a subset of natural language processing (NLP). While NLP covers a wide range of language-related tasks, like translation and summarization, sentiment analysis focuses specifically on identifying the emotional tone of text. 

Python libraries for sentiment analysis

You can use several libraries in Python to streamline your sentiment analysis. Depending on the type of sentiment analysis you’re looking for, choose the library with the built-in functions best-suited to your needs. Popular options include:

• TextBlob: Simple and efficient, best for beginners, and offers pre-trained sentiment classifiers.

• VADER: Optimized for social media data, best for professionals using a rule-based approach for large-scale social media analysis.

• NLTK (Natural Language Toolkit): A comprehensive toolkit for NLP that is best for NLP professionals who need a wider range of NLP functions.

• spaCy: A fast NLP library that integrates well with other plugins, best for professionals looking for quick, large-scale sentiment analysis.

• Hugging Face Transformers: Provides powerful pre-trained models (like BERT), best for professionals who want to build deep learning architecture and fine-tune their own model.

Benefits of sentiment analysis in Python

Using sentiment analysis provides insight into how people feel about your products, services, or brand, including the intent behind the message. You can use these insights to benefit your brand as a whole, informing everything from your marketing strategies to product design. By detecting whether feedback is positive or negative and identifying specific emotions like joy or frustration, you can support more empathetic and effective decision-making within your company.

Deciding to use Python for your sentiment analysis also offers benefits, as Python provides a simple, flexible, and extensive library support. You can use tools like NLTK and Hugging Face Transformation to process text data, train models, and apply sentiment scoring to large amounts of data. Using built-in Python resources, you can more quickly and effectively turn your unstructured data into valuable insights.

Challenges of sentiment analysis in Python

Despite its many benefits, sentiment analysis is an imperfect science. Human language has nuances, and algorithms might have challenges detecting things like sarcasm, idioms, slang, negation, or multiple sentiments in one sentence. 

In addition, sentiment analysis relies heavily on context, meaning it may be difficult for algorithms to have accurate findings without proper context. To overcome or minimize these challenges in Python, you must pay careful attention to your preprocessing, model training, and fine-tuning. You’ll need to train your model specifically for your business area, meaning you won’t be able to use the same model for a different application. 

How to start learning sentiment analysis in Python

Sentiment analysis is a subset of NLP, which means that becoming familiar with NLP basics is a great first step toward creating your own analysis. Areas of NLP particularly relevant to sentiment analysis include tokenization, lemmatization, stop-word removal filters, and keyword analysis. 

Once you become familiar with these NLP concepts, you can explore different types of sentiment analysis approaches, including rule-based and machine learning approaches. While you can learn through online tutorials, taking a structured approach through online courses can help provide guidance through introductory (or advanced) concepts. For example, you might consider completing classes like IBM’s Data Analysis with Python on Coursera for a more didactic learning experience. 

Learn more about sentiment analysis in Python with Coursera 

Conducting sentiment analysis in Python allows you to use pre-built libraries to analyze text and determine the underlying human emotions. This type of analysis often uses data analysis and machine learning concepts, and building a foundation in these skills can help you excel toward more advanced applications. To begin, consider the Machine Learning Specialization by Stanford University and DeepLearning.AI, where you can learn to build ML models, build and train neural networks, and apply best practices for ML development. 

Want to learn more about Python? With the University of Michigan’s 

Python for Everybody Specialization, you’ll learn to develop programs to gather, clean, analyze, and visualize data with Python.