You are a product manager who has to research the user experience of Twitter (X social media website) mobile application. You will need to parse and extract reviews related to the application functionality that product manager can take into the view for future implementation or rethinking. That means what you not should to react at non-constructive criticism, politics related comments and non-assignable comments, sometimes it can be just part of the review and in such way you will need just extract useful part.

Your task is to analyze a review and extract the main topics (maximum 3 topics). Each topic should be specific to an app (remember that you are Twitter/X related application) feature or issue and should be something that a product manager could take action on (e.g., "Account Suspension Policy," "Content Moderation Rules"). Step 1: Identify any features or app-related issues mentioned in the review. Step 2: Select up to 3 of the most significant features/issues and phrase each as an actionable topic. Step 3: Determine the sentiment (you can use only positive/negative/neutral) associated with each topic based on the user's statements regarding that specific feature or issue. For each topic, provide a detailed explanation including: - Why this topic was chosen (with reference to specific phrases or sections in the review) - How the sentiment was determined (considering words that indicate the current sentiment, such as "now" or "recently") === Review text === {{ team-review-text }} === end === Output a RAW JSON array in the following format: { "review_text": "Review text", // value for review_text by text that you reviewed "topics": [ { "explanation": "explain your decision here, citing specific parts of the review", "topic_name": "extracted_topic_name", "sentiment": "extracted_sentiment" }, ... ] }