Sentiment analysis

The aim of sentiment analysis is to identify, extract, quantify and understand the subjective attitudes and evaluations of people. By analyzing opinions and attitudes, companies can improve their products and services, increase customer satisfaction, and strengthen their brand reputation.

What methods are there for sentiment analysis?

There are two basic approaches to sentiment analysis:

Rule-based sentiment analysis methods

They use a dictionary (lexicon) with words and phrases that have a positive, negative or neutral meaning. The text is compared with these entries and the overall opinion is determined based on the frequency and strength of these entries.

Machine learning-based sentiment analysis methods

These train algorithms with large amounts of data of texts and their associated evaluations. In this way, the algorithms learn to independently recognize patterns in language that indicate positive, negative or neutral opinions.

areas of application

Sentiment analysis is used in a wide range of areas, including:

• Market research: Analyzing customer reviews, survey results, and social media posts to understand what customers think of products, services, or companies.
• Risk management: Identify potential problems or crises by monitoring online discussions and opinions.
• Product development: Tracking customer feedback and opinions about new products or features to improve them.
• Election research: Analyzing social media posts and online discussions to understand public opinion on political issues.
• Customer service: Automatic classification of customer inquiries and complaints according to their emotional tone to enable faster and more effective processing.

Benefits of sentiment analysis

• Extraction of insights from unstructured data: Sentiment analysis makes it possible to obtain valuable information from large amounts of data of texts used in conventional analyses would not be accessible.
• Improving decision making: By understanding the opinions and attitudes of customers, employees, or the public, companies can make more informed decisions.
• Increasing customer satisfaction: Sentiment analysis can help identify and address problems and potential crises early on before they affect customer satisfaction.
• Improving brand reputation: By understanding and responding to online opinions, you can improve and strengthen brand reputation.

Challenges of sentiment analysis

• Sarcasm and irony: It can be difficult to recognize sarcasm and irony in texts, which can lead to misinterpretations of sentiment analysis.
• Ambiguity of language: The meaning of words and phrases can vary depending on the context, which can make sentiment analysis difficult.
• Cultural differences: The way people express their opinions can differ across cultures, which can make sentiment analysis difficult in a global context.

Synonyms sentiment analysis

Common synonyms:

• Opinion analysis
• Sentiment analysis
• Emotion analysis
• Opinion extraction
• Finding an opinion
• Mood monitoring
• Reputation management

More specialized synonyms:

• Market sentiment analysis
• Customer opinion analysis
• Social media analysis
• Web opinion analysis
• Text analysis
• Natural language processing (NLP)

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.