Generate Tag for Video Caption

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Strings

You are given a string caption representing the caption for a video.

The following actions must be performed in order to generate a valid tag for the video:

Combine all words in the string into a single camelCase string prefixed with '#'. A camelCase string is one where the first letter of all words except the first one is capitalized. All characters after the first character in each word must be lowercase.
Remove all characters that are not an English letter, except the first '#'.
Truncate the result to a maximum of 100 characters.

Return the tag after performing the actions on caption.

Example 1:

Input: caption = "Leetcode daily streak achieved"

Output: "#leetcodeDailyStreakAchieved"

Explanation:

The first letter for all words except "leetcode" should be capitalized.

Example 2:

Input: caption = "can I Go There"

Output: "#canIGoThere"

Explanation:

The first letter for all words except "can" should be capitalized.

Example 3:

Input: caption = "hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh"

Output: "#hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh"

Explanation:

Since the first word has length 101, we need to truncate the last two letters from the word.

Constraints:

1 <= caption.length <= 150
caption consists only of English letters and ' '.

Try coding it in LeetCode

Solution

Clarifying Questions

When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:

How should I handle various spacing patterns in the caption, such as multiple spaces between words or leading and trailing spaces?
What is the expected output if the input caption consists only of space characters, which would result in an empty list of words after splitting?
Rule #2 mentions removing non-letter characters, but the constraints state the input only contains letters and spaces. Should I write the code to handle other characters like digits or punctuation just in case?
To confirm the camelCase logic: should the first word be converted entirely to lowercase, and all subsequent words have only their first letter capitalized with the rest lowercased, regardless of the original casing?
When the problem refers to 'English letters', can I assume this is limited to the standard 'a-z' and 'A-Z' in the ASCII character set?

Brute Force Solution

Approach

The brute force strategy involves systematically checking every single tag from the provided list against the video caption. For each tag, we verify if its words appear within the caption in the correct sequence. After examining all possibilities, we select the longest tag that was a successful match.

Here's how the algorithm would work step-by-step:

Take the first tag from the list of possibilities.
Scan through the video caption from the beginning to find the first word of this tag.
If you find it, then continue scanning the rest of the caption, starting from where you left off, to find the second word of the tag.
Keep repeating this process, searching for each word of the tag in order within the caption.
If you are able to find all the words of the tag in the correct sequence, then this tag is a valid candidate. Keep it aside.
Now, grab the next tag from the list and repeat this entire checking procedure.
Continue this until you have checked every single tag against the caption.
Finally, look at all the valid candidates you've kept aside and pick the one that is the longest.

Code Implementation

def generate_tag_brute_force(caption_string, available_tags):
    caption_words = caption_string.split()
    number_of_words = len(caption_words)
    all_consecutive_phrases = []

    # To be exhaustive, we generate every possible phrase to ensure we don't miss any potential match.

    for start_index in range(number_of_words):
        for end_index in range(start_index, number_of_words):
            current_phrase = " ".join(caption_words[start_index : end_index + 1])
            all_consecutive_phrases.append(current_phrase)

    valid_phrases_from_caption = []

    # For efficient lookups, we convert the list of tags into a set to check for matches quickly.

    available_tags_set = set(available_tags)

    for phrase in all_consecutive_phrases:
        if phrase in available_tags_set:
            valid_phrases_from_caption.append(phrase)

    longest_matching_tag = ""

    # After collecting all valid tags found in the caption, we must find the longest one.

    for tag in valid_phrases_from_caption:
        if len(tag) > len(longest_matching_tag):
            longest_matching_tag = tag

    return longest_matching_tag

Big(O) Analysis

Time Complexity

O(T * L * C) – Let T be the number of tags, L be the maximum number of words in any tag, and C be the number of words in the caption. The primary cost driver is the nested checking process described by the strategy. The algorithm iterates through each of the T tags, and for each tag, it effectively scans the caption for each of the tag's L words. This results in a search operation that is repeated for every word of every tag. The total operations are therefore proportional to the product of these three factors, T * L * C, which simplifies to a time complexity of O(T * L * C).

Space Complexity

O(N) – The algorithm's space usage is determined by storing all valid tags found during the process. The description to 'Keep it aside' and later examine 'all the valid candidates' implies an auxiliary collection is created to hold every matching tag. In a worst-case scenario, all tags from the input list are valid matches and are added to this collection. Therefore, if N is the total size of all tags in the input list, the auxiliary space required is O(N).

Optimal Solution

Approach

The best way to find relevant tags is to first clean up the caption by removing punctuation and common filler words. After this preparation, we simply count the remaining, more meaningful words to see which ones appear most often, revealing the best tags.

Here's how the algorithm would work step-by-step:

First, prepare the text by making every letter lowercase and removing all punctuation like commas and periods. This ensures that words like 'Cat' and 'cat.' are treated as the same thing.
Next, break the clean text into a collection of individual words.
Go through this collection and filter out all the common, everyday words that don't describe the video's content, such as 'the', 'a', 'is', and 'for'.
Now, create a tally for every unique word that is left, counting each time it appears.
After counting, arrange the words in order from the most frequent to the least frequent.
Finally, choose the words from the very top of this ordered list to serve as the final generated tags.

Code Implementation

def generate_tag_for_video_caption(video_caption_text, list_of_forbidden_words):
    # Normalization ensures that different capitalizations or punctuations do not split a single word concept.
    normalized_caption_chars = []
    for character in video_caption_text:
        if character.isalnum():
            normalized_caption_chars.append(character.lower())
        else:
            normalized_caption_chars.append(' ')
    normalized_caption = "".join(normalized_caption_chars)

    # Using a set provides near-constant time lookups, which is far more efficient than searching a list.
    forbidden_words_lookup_set = set(list_of_forbidden_words)

    all_caption_words = normalized_caption.split()
    
    # This scoreboard tallies the appearances of each word that is not on the forbidden list.
    valid_word_frequencies = {}
    for current_word in all_caption_words:
        if current_word not in forbidden_words_lookup_set:
            valid_word_frequencies[current_word] = valid_word_frequencies.get(current_word, 0) + 1
    
    if not valid_word_frequencies:
        return ""

    best_tag_candidate = ""
    highest_frequency = -1

    # Review all valid words to find the one with the highest frequency, using alphabetical order for ties.
    for word, frequency in valid_word_frequencies.items():
        if frequency > highest_frequency:
            highest_frequency = frequency
            best_tag_candidate = word
        elif frequency == highest_frequency:
            if best_tag_candidate == "" or word < best_tag_candidate:
                best_tag_candidate = word

    return best_tag_candidate

Big(O) Analysis

Time Complexity

O(n log n) – Let 'n' be the number of words in the caption. The initial steps of cleaning the text, filtering stop words, and counting word frequencies using a hash map are all performed in linear time, proportional to 'n'. The most computationally expensive part of the process is sorting the unique words based on their frequency counts. This sorting step is the primary driver of the cost, requiring a number of operations that is proportional to n * log(n). Thus, the overall time complexity simplifies to O(n log n).

Space Complexity

O(N) – Let N be the number of words in the caption after cleaning. The algorithm's space usage is driven by storing intermediate collections of these words. First, a collection is created to hold all N words, requiring O(N) space. A tally, typically a hash map, is then used to store the frequency of unique words, which in the worst case could also approach N entries if all words are unique. Finally, sorting these frequencies usually involves creating another list of the unique words, further contributing to the O(N) space requirement. Thus, the total auxiliary space complexity is linear with respect to the number of words.

Edge Cases

Case	How to Handle
Caption contains only a single word	The solution must correctly lowercase the entire word, as there are no subsequent words to apply camel-casing to.
Caption with multiple spaces between words or leading/trailing spaces	The implementation must filter out any empty strings that result from splitting the caption to avoid incorrect camel-case construction.
Words in the caption have non-standard casing, such as all-caps or mixed-case	The solution must strictly enforce the casing rules by converting the first word to lowercase and title-casing all subsequent words.
A single long word causes the generated tag to exceed the 100-character limit	The solution must build the full tag string first and then correctly truncate the final result to the 100-character maximum length.
The generated tag, including the '#', has a length of exactly 100	The solution should not modify the string, as the truncation rule only applies to results longer than 100 characters.
Caption is the minimum possible length, consisting of a single letter	The solution correctly processes this into a two-character tag, such as '#a', demonstrating handling of minimal valid inputs.
Caption contains non-alphabetic characters, contrary to the stated constraints	A robust solution must follow the rules in order, performing camel-casing before removing any non-alphabetic characters from the result.
The input caption is an empty string	Although disallowed by constraints, a robust implementation should handle this by returning a default value like '#' to prevent errors.

Caption contains only a single word

How to Handle:

The solution must correctly lowercase the entire word, as there are no subsequent words to apply camel-casing to.

Caption with multiple spaces between words or leading/trailing spaces

How to Handle:

The implementation must filter out any empty strings that result from splitting the caption to avoid incorrect camel-case construction.

Words in the caption have non-standard casing, such as all-caps or mixed-case

How to Handle:

The solution must strictly enforce the casing rules by converting the first word to lowercase and title-casing all subsequent words.

A single long word causes the generated tag to exceed the 100-character limit

How to Handle:

The solution must build the full tag string first and then correctly truncate the final result to the 100-character maximum length.

The generated tag, including the '#', has a length of exactly 100

How to Handle:

The solution should not modify the string, as the truncation rule only applies to results longer than 100 characters.

Caption is the minimum possible length, consisting of a single letter

How to Handle:

The solution correctly processes this into a two-character tag, such as '#a', demonstrating handling of minimal valid inputs.

Caption contains non-alphabetic characters, contrary to the stated constraints

How to Handle:

A robust solution must follow the rules in order, performing camel-casing before removing any non-alphabetic characters from the result.

The input caption is an empty string

How to Handle:

Although disallowed by constraints, a robust implementation should handle this by returning a default value like '#' to prevent errors.