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The filtering part was fine, just check if the timestamp falls in the range and parse out tokens starting with '#'.
Clarify the problem constraints (array sizes, timestamp format, case sensitivity) and then propose a solution that filters tweets by the inclusive time window, tokenizes each tweet to extract hashtags, counts frequencies using a hash map, and finally sorts the hashtags by count descending and lexicographically ascending to return the top 3. Discuss time and space complexity, and consider edge cases like no hashtags or ties.
Pro tip: Demonstrate awareness of real-world data by mentioning that tweets may contain multiple hashtags, URLs, or punctuation, and that tokenization should handle these correctly; also note that sorting can be optimized with a heap if the number of unique hashtags is large.
Ask about input sizes, timestamp format (e.g., Unix epoch or string), whether the time window is inclusive, and if hashtags are case-sensitive. Confirm the output format (list of strings or with counts).
Iterate through the tweets and timestamps, selecting only those where the timestamp is within [current_time - window, current_time] (inclusive). If timestamps are not sorted, this is O(n).
For each selected tweet, tokenize the text (e.g., split by whitespace) and for each token starting with '#', increment its count in a hash map. Consider stripping punctuation and handling case sensitivity.
Sort the unique hashtags by count descending, and for ties, by lexicographic ascending order. Use a custom comparator or sort with a tuple key.
Return the first three hashtags (or fewer if not enough). Analyze time complexity: O(n + m log m) where n is number of tweets and m is number of unique hashtags; space O(m).
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