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.
This module contains various textcomparison algorithms designed to compare one statement to another.

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.

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.

class
chatterbot.comparisons.
SentimentComparison
[source]¶ Calculate the similarity of two statements based on the closeness of the sentiment value calculated for each statement.

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. Return type: float

initialize_nltk_punkt
()[source]¶ Download required NLTK corpora if they have not already been downloaded.

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
)