How to do sentiment analysis?

Sentiment analysis involves analyzing textual data to understand and categorize the emotions and opinions expressed. This technique is widely used in customer feedback, social media monitoring, and market research to gauge public sentiment towards a brand, product, or service. By implementing sentiment analysis, businesses can gain valuable insights into customer attitudes, identify trends, and make data-driven decisions to improve their offerings and customer experience.

Tips to do sentiment analysis

Here are some tips to consider when you’re trying to do sentiment analysis:

1. Choose the Right Tools: Select advanced sentiment analysis tools or platforms that utilize natural language processing (NLP) to accurately detect and interpret emotions in text.

2. Clean and Preprocess Data: Ensure your textual data is clean and preprocessed by removing noise such as stop words, punctuation, and irrelevant information to improve analysis accuracy.

3. Use Multiple Data Sources: Gather data from various sources like social media, customer reviews, surveys, and emails to get a comprehensive view of sentiment.

4. Monitor and Update Regularly: Continuously monitor sentiment analysis results and update your models and dictionaries to adapt to changing language and trends.

Use Narrative BI to do sentiment analysis

Narrative BI is a generative analytics platform that automatically turns your data into actionable data narratives. To do sentiment analysis with Narrative BI, follow the steps below:

  • Connect your data source directly to Narrative BI or upload a spreadsheet to the AI Data Analyst tool.
  • Explore the feed of automatically generated insights.
  • Ask questions to uncover strategies and actions to do sentiment analysis.
  • Get AI-generated answers, automated reports, and insights.
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Suggested questions to ask AI Data Analyst to do sentiment analysis

AI Data Analyst from Narrative BI is an advanced Generative Business Intelligence tool that leverages AI to provide actionable insights from your data. It allows you to upload spreadsheets or directly connect various data sources, ask questions using natural language queries, and get actionable answers. You can use the following AI Data Analyst prompts to do sentiment analysis:

What is the overall sentiment of our recent customer reviews and feedback?

How does sentiment vary across different social media platforms for our brand?

What are the most common positive and negative keywords associated with our product in customer feedback?

How has the sentiment towards our brand changed over the past six months?

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