Sentiment analysis

What it is

Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text. This is a popular way for organizations to determine and categorize opinions about a product, service or idea.

Pros & Cons

Pros:

  • Provides user feedback at scale
  • Enables faster feedback collection
  • Helps make data-driven decisions
  • Can be used for competitive analysis

Cons:

  • Limited accuracy, especially with sarcasm or irony
  • Lack of context can lead to misinterpretation
  • Potential bias towards certain language or demographics
  • Limited to text-based feedback

How to use

Checklist:

  1. A machine learning algorithm as a sentiment analysis tool, like R studio or Python
  2. Script: qualitative data like interview transcripts

Steps:

  1. Collect and pre-process the data. Collect user feedback from sources such as customer reviews, social media, or feedback forms. Data is cleaned to remove irrelevant parts, like contractions, articles, and punctuation, and standardize the text.
  2. Select a sentiment analysis tool: Choose a sentiment analysis tool that best fits your needs and budget.
  3. Run sentiment analysis: Use the tool to analyse the data and categorize feedback into positive, negative, or neutral sentiment.
  4. Analyse the results: Review the sentiment analysis results to identify common themes and issues in user feedback.
  5. Act: Use the insights gained from the sentiment analysis to inform UX design decisions and prioritize areas for improvement. Communicate the findings to stakeholders and make changes as necessary.

Types of sentiment analysis

Sentiment analysis systems fall into several different categories:

  • Fine-grained sentiment analysis: Breaks down sentiment indicators into more precise categories, such as very positive and very negative. Effective for grading customer satisfaction surveys.
  • Emotion detection analysis: Identifies emotions rather than positivity and negativity. Examples include happiness, frustration, shock, anger, and sadness.
  • Intent-based analysis: Recognizes motivations behind a text in addition to opinion. Can help understand the reason behind the feedback.
  • Aspect-based analysis: Examines the specific component being positively or negatively mentioned. Helps identify which aspects of a product or service users like or dislike.

Recommended reading

Back to

Next Step