Step 1: Keyword Input by Clients

• Clients specify a set number of keywords that are crucial for monitoring their brand's online presence. These keywords can include the brand name, key product names, high-profile employees, or any specific campaigns.

Step 2: Fetching Text and Images

• Using the specified keywords, your system performs searches across selected platforms (like Google, Bing, and Twitter). The search results will include both text (posts, comments, articles) and images related to the keywords.

Step 3: Initial Sentiment Analysis

• The text obtained from search results is then analyzed using AWS sentiment analysis tools (part of Amazon Comprehend) to determine the sentiment (positive, neutral, negative) of each piece of content.

Step 4: Filtering for Negative Sentiment

• Content flagged as having negative sentiment by the sentiment analysis step is filtered for further examination. This focuses your resources on content that could potentially harm your client's brand reputation.

Step 5: Detailed Analysis with Amazon Rekognition

• For the filtered content identified as negative, Amazon Rekognition is used to analyze images and videos for specific elements that might be malicious or damaging. This could include detecting inappropriate content, identifying fake images, or even recognizing if a brand's products are being shown in a negative context.

Benefits of This Approach:

• Efficiency: By only applying the more resource-intensive image and video analysis to content already flagged as potentially negative, you significantly reduce the volume of data needing detailed analysis, saving on processing time and costs.
• Focused Monitoring: This method ensures that monitoring efforts are concentrated on content most likely to impact the client's brand negatively, making the service more effective and valuable to clients.
• Scalability: The approach allows the service to scale more effectively, as initial sentiment analysis can filter a large volume of content quickly before applying the more detailed (and costlier) analysis.