Understanding Sentiment Analysis

• Sentiment analysis is determining the emotional tone behind words to understand attitudes, opinions, and emotions in online mentions.

Importance of Sentiment Analysis

• Enables discovery of audience feelings on specific topics.
• Benefits include monitoring brand health, rapid problem identification, and improved customer service.

How Sentiment Analysis Works

• It employs Natural Language Processing (NLP), machine learning, and computational linguistics.
• Three types include rule-based, automated, and hybrid approaches.
• Rule-based analysis uses linguistic rules.
• Automated analysis employs machine learning algorithms for training sentiment analysis models.
• Hybrid analysis combines rule-based and automated approaches.

Accuracy of Sentiment Analysis

• Accuracy varies based on the approach and data quality.
• Rule-based is generally less accurate but easier to implement.
• Automated analysis can be highly accurate but needs lots of training data.

Getting Started with Sentiment Analysis

• Options include using commercial tools, building custom tools, or using sentiment analysis APIs.
• Commercial tools are user-friendly with many functions but can be pricey.
• Custom tools are tailored but can be complex to create and maintain.
• APIs offer an easy way to add sentiment analysis to applications but can be costly.

Sentiment Analysis Platforms

• Platforms to consider are MonkeyLearn, Brandwatch, Awario, Lexalytics, and MeaningCloud.

Conclusion

• Sentiment analysis helps in understanding customer opinion thus enabling informed business decisions by knowing customer sentiment.