# Comparisons¶

## Statement comparison¶

ChatterBot uses Statement objects to hold information about things that can be said. An important part of how a chat bot selects a response is based on its ability to compare two statements to each other. There are a number of ways to do this, and ChatterBot comes with a handful of methods built in for you to use.

class chatterbot.comparisons.JaccardSimilarity[source]

Calculates the similarity of two statements based on the Jaccard index.

The Jaccard index is composed of a numerator and denominator. In the numerator, we count the number of items that are shared between the sets. In the denominator, we count the total number of items across both sets. Let’s say we define sentences to be equivalent if 50% or more of their tokens are equivalent. Here are two sample sentences:

The young cat is hungry. The cat is very hungry.

When we parse these sentences to remove stopwords, we end up with the following two sets:

{young, cat, hungry} {cat, very, hungry}

In our example above, our intersection is {cat, hungry}, which has count of two. The union of the sets is {young, cat, very, hungry}, which has a count of four. Therefore, our Jaccard similarity index is two divided by four, or 50%. Given our similarity threshold above, we would consider this to be a match.

compare(statement, other_statement)[source]

Return the calculated similarity of two statements based on the Jaccard index.

initialize_nltk_wordnet()[source]

Download the NLTK wordnet corpora that is required for this algorithm to run only if the corpora has not already been downloaded.

class chatterbot.comparisons.LevenshteinDistance[source]

Compare two statements based on the Levenshtein distance of each statement’s text.

For example, there is a 65% similarity between the statements “where is the post office?” and “looking for the post office” based on the Levenshtein distance algorithm.

compare(statement, other_statement)[source]

Compare the two input statements.

Returns: The percent of similarity between the text of the statements. float
class chatterbot.comparisons.SentimentComparison[source]

Calculate the similarity of two statements based on the closeness of the sentiment value calculated for each statement.

compare(statement, other_statement)[source]

Return the similarity of two statements based on their calculated sentiment values.

Returns: The percent of similarity between the sentiment value. float
initialize_nltk_vader_lexicon()[source]

Download the NLTK vader lexicon for sentiment analysis that is required for this algorithm to run.

class chatterbot.comparisons.SynsetDistance[source]

Calculate the similarity of two statements. This is based on the total maximum synset similarity between each word in each sentence.

This algorithm uses the wordnet functionality of NLTK to determine the similarity of two statements based on the path similarity between each token of each statement. This is essentially an evaluation of the closeness of synonyms.

compare(statement, other_statement)[source]

Compare the two input statements.

Returns: The percent of similarity between the closest synset distance. float
initialize_nltk_punkt()[source]

initialize_nltk_stopwords()[source]

initialize_nltk_wordnet()[source]

### Use your own comparison function¶

You can create your own comparison function and use it as long as the function takes two statements as parameters and returns a numeric value between 0 and 1. A 0 should represent the lowest possible similarity and a 1 should represent the highest possible similarity.

def comparison_function(statement, other_statement):

# Your comparison logic

# Return your calculated value here
return 0.0


#### Setting the comparison method¶

To set the statement comparison method for your chat bot, you will need to pass the statement_comparison_function parameter to your chat bot when you initialize it. An example of this is shown below.

from chatterbot import ChatBot
from chatterbot.comparisons import levenshtein_distance

chatbot = ChatBot(
# ...
statement_comparison_function=levenshtein_distance
)