Should I Build It Should I Build It
07 Jun 2025
Marketing

A sentiment analysis tracker of financial news stored over time

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Run Away

Multiple attempts have failed with clear negative feedback. Continuing down this path would likely waste your time and resources when better opportunities exist elsewhere.

Should You Build It?

Don't build it.

Your are here

You're entering a crowded space with your sentiment analysis tracker for financial news. We found 18 similar products already launched, indicating high competition. While this validates interest in the problem, it also means you'll need a strong differentiator to stand out. The average engagement (6 comments) on similar products is medium, suggesting moderate interest. Unfortunately, we don't have enough data on net use or buy signals to reliably gauge market demand, highlighting a critical gap in understanding user needs. Given this category has been flagged as 'Run Away', it's critical to carefully consider if you have the edge to overcome these challenges. There's a risk you might waste resources on an idea with limited traction unless you can find a unique angle.

Recommendations

  1. Carefully analyze the criticisms of similar products. The feedback highlights concerns around sentiment accuracy, UI issues, unclear value propositions, and doubts about the necessity of complex machine learning. Pay close attention to the criticisms regarding products scraping WallStreetBets and those that summarized news using AI, as these are very similar to your idea.
  2. Focus on improving sentiment accuracy by incorporating real-world sentiment theory and addressing sarcasm detection. Many tools were criticized for misclassifying sentiments as neutral or missing negative sentiment. Users want to ensure that the tool has clear and effective methods to properly classify the data.
  3. Conduct thorough market research by talking to at least 3 people who've tried similar products to understand their actual needs and pain points. Focus on understanding specific features they use and which ones they wish they could have.
  4. Identify a clear differentiator. Given the competition, you need a unique selling proposition. Can you focus on a specific niche (e.g., crypto news), offer superior accuracy, or provide more actionable insights? One user suggested showing the news impact on price changes, that's a good starting point.
  5. Prioritize UI/UX improvements. Some competitors were criticized for low contrast, unclear clickability, and poor mobile styling. Consider how to make your tool as accessible and easy-to-use as possible, perhaps using a high contrast mode.
  6. Consider alternative approaches. Some users questioned the necessity of machine learning and suggested keyword matching. Evaluate if simpler methods can achieve similar results, especially if you're targeting a specific niche.
  7. Develop a monetization strategy. Since we lack buy signals, explore various options like freemium, subscriptions, or enterprise solutions. Ask yourself if it's more effective to provide value to customers for free or for a subscription.
  8. If you've already built something, assess if the technology can be repurposed for a related but different problem. Could the sentiment analysis engine be used for customer support or brand monitoring instead?

Questions

  1. Given the existing solutions, what specific unmet need will your sentiment analysis tracker address, and how will you validate that need before investing significant development effort?
  2. How will you ensure your sentiment analysis is accurate and reliable, especially in detecting sarcasm and nuance in financial news, given that competitors have struggled with this?
  3. What is your plan to overcome the 'Run Away' classification of this idea category, and what evidence will you use to determine if you're making progress or need to pivot to a different approach?

Your are here

You're entering a crowded space with your sentiment analysis tracker for financial news. We found 18 similar products already launched, indicating high competition. While this validates interest in the problem, it also means you'll need a strong differentiator to stand out. The average engagement (6 comments) on similar products is medium, suggesting moderate interest. Unfortunately, we don't have enough data on net use or buy signals to reliably gauge market demand, highlighting a critical gap in understanding user needs. Given this category has been flagged as 'Run Away', it's critical to carefully consider if you have the edge to overcome these challenges. There's a risk you might waste resources on an idea with limited traction unless you can find a unique angle.

Recommendations

  1. Carefully analyze the criticisms of similar products. The feedback highlights concerns around sentiment accuracy, UI issues, unclear value propositions, and doubts about the necessity of complex machine learning. Pay close attention to the criticisms regarding products scraping WallStreetBets and those that summarized news using AI, as these are very similar to your idea.
  2. Focus on improving sentiment accuracy by incorporating real-world sentiment theory and addressing sarcasm detection. Many tools were criticized for misclassifying sentiments as neutral or missing negative sentiment. Users want to ensure that the tool has clear and effective methods to properly classify the data.
  3. Conduct thorough market research by talking to at least 3 people who've tried similar products to understand their actual needs and pain points. Focus on understanding specific features they use and which ones they wish they could have.
  4. Identify a clear differentiator. Given the competition, you need a unique selling proposition. Can you focus on a specific niche (e.g., crypto news), offer superior accuracy, or provide more actionable insights? One user suggested showing the news impact on price changes, that's a good starting point.
  5. Prioritize UI/UX improvements. Some competitors were criticized for low contrast, unclear clickability, and poor mobile styling. Consider how to make your tool as accessible and easy-to-use as possible, perhaps using a high contrast mode.
  6. Consider alternative approaches. Some users questioned the necessity of machine learning and suggested keyword matching. Evaluate if simpler methods can achieve similar results, especially if you're targeting a specific niche.
  7. Develop a monetization strategy. Since we lack buy signals, explore various options like freemium, subscriptions, or enterprise solutions. Ask yourself if it's more effective to provide value to customers for free or for a subscription.
  8. If you've already built something, assess if the technology can be repurposed for a related but different problem. Could the sentiment analysis engine be used for customer support or brand monitoring instead?

Questions

  1. Given the existing solutions, what specific unmet need will your sentiment analysis tracker address, and how will you validate that need before investing significant development effort?
  2. How will you ensure your sentiment analysis is accurate and reliable, especially in detecting sarcasm and nuance in financial news, given that competitors have struggled with this?
  3. What is your plan to overcome the 'Run Away' classification of this idea category, and what evidence will you use to determine if you're making progress or need to pivot to a different approach?

  • Confidence: High
    • Number of similar products: 18
  • Engagement: Medium
    • Average number of comments: 6
  • Net use signal: -3.7%
    • Positive use signal: 6.9%
    • Negative use signal: 10.6%
  • Net buy signal: -8.2%
    • Positive buy signal: 0.6%
    • Negative buy signal: 8.9%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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