Sentiment Scorer
Score the sentiment of text using a simple lexicon-based approach.
Given a sentiment lexicon (mapping words to scores) and a text, compute the average sentiment score of all lexicon words found in the text.
Input format:
- Line 1: Number of lexicon entries M
- Lines 2 to M+1: word followed by its score (float)
- Line M+2: Text to score (lowercase words)
Output: Average sentiment score of matched words, rounded to 4 decimal places. If no lexicon words are found, output 0.0.
Example:
5 good 1.0 great 1.5 bad -1.0 terrible -2.0 ok 0.0 the movie was great but the ending was bad
0.25
Lexicon words found in text:
- "great" -> 1.5
- "bad" -> -1.0
Words not in lexicon: the, movie, was, but, ending (skipped)
Average: (1.5 + (-1.0)) / 2 = 0.5 / 2 = 0.25
Constraints:
- Sentiment scores can be negative (negative sentiment) or positive
- Only average over words found in the lexicon
- If a word appears multiple times in text, count it each time
- If no lexicon words found, return 0.0
- Round to 4 decimal places
Background Knowledge
The Sentiment Scorer problem falls under the category of Text Classification, a fundamental task in Natural Language Processing (NLP). Text classification involves assigning a label or score to a piece of text based on its content. In this case, we're focusing on sentiment analysis, which aims to determine the emotional tone or attitude conveyed by the text. A simple approach to sentiment analysis is using a lexicon-based method, where a dictionary or lexicon maps words to their corresponding sentiment scores. This approach relies on the idea that the presence of certain words in a text can indicate its overall sentiment.
The given problem utilizes a sentiment lexicon, which is a pre-defined mapping of words to sentiment scores. This lexicon serves as the basis for scoring the sentiment of a given text. The scores are typically floating-point numbers, where positive values indicate positive sentiment, negative values indicate negative sentiment, and values close to zero indicate neutral sentiment. Understanding how to work with such a lexicon and how to compute the average sentiment score of words found in a text is crucial for solving this problem.
In the context of NLP, tokenization (the process of breaking down text into individual words or tokens) and lookup (finding the sentiment score of each word in the lexicon) are key operations. The problem also involves basic statistical computation, specifically calculating the average of the sentiment scores of matched words. Understanding these concepts and how they apply to text data will help in developing an effective solution to the Sentiment Scorer problem.
Algorithm/Approach
The general approach to solving this problem involves a straightforward lexicon-based sentiment analysis algorithm:
- Load the sentiment lexicon into a data structure that allows for efficient lookup.
- Tokenize the input text into individual words.
- For each word in the text, check if it exists in the lexicon and if so, retrieve its sentiment score.
- Calculate the average sentiment score of all words found in the lexicon. This approach is simple yet effective for demonstrating the basics of sentiment analysis using a pre-defined lexicon.
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