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Aspect-Sentiment Analysis for Scalable User Research
- Aspect-Based Sentiment Analysis (ABSA) is a method used for generating sentiment scores over product features in context, allowing for insight generation over large datasets.
- It helps in identifying user sentiment towards specific product features, such as Search UI or Filters, enabling rapid understanding for Product teams and founders.
- The article discusses leveraging modern NLP methods and user data to extract valuable insights for Product teams, using G2 reviews as a data source.
- By structuring reviews from G2 using APIFY Actor, the article demonstrates extracting and organizing review data efficiently.
- A custom spaCy-based function is used for extracting product-relevant aspects from user reviews, filtering out irrelevant or generic phrases.
- The Aspect Extraction process involves a grammatical approach with SpaCy, followed by an LLM call to evaluate the relevance of aspects for targeted user research.
- A pre-trained transformer model is utilized for aspect-based sentiment analysis, providing structured sentiment data at scale.
- Heatmaps are used to visualize the distribution of sentiment across product aspects, aiding in identifying trends in user feedback quickly.
- Further pre-processing and analysis techniques can be employed to enhance the quality of aspects generated and provide more detailed insights.
- The article showcases a methodological approach to conducting scalable user research through aspect-sentiment analysis, offering valuable outcomes for enhancing product understanding.
- Overall, the article emphasizes the importance of leveraging ABSA and NLP techniques for extracting actionable insights from user reviews for informed decision-making.
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