lexicon-overlap-score 0.0.1

Functions for calculation of the lexicon overlap score

Lexicon overlap score

The lexicon overlap score is a similarity measure for sentiment lexicons as they are used in Sentiment Classification.

Installation

Install via pip install lexicon_overlap_score

Usage

Import via import lexicon_overlap_score as los

Your lexicons should be a pandas dataframe with the columns word and so (semantic orientation). Then the following three functions can be used.

los.simple(lex1, lex2)
los.binary(lex1, lex2)
los.score(lex1, lex2)

simple and binary return a value between 0 and 1, while score returns values between -1 and 1. The functions expect positive words to have a positive value and negative words to have a negative value.

If your lexicon is a python dictionary mapping words to values, convert it to a pandas dataframe.

import pandas as pd
df_lex = pd.DataFrame().from_dict(dict_lex, orient="index").reset_index().rename(columns={"index": "word", 0: "so"})

Example

import pandas as pd
import lexicon_overlap_score as los
df1 = pd.DataFrame([("test",   1), ("free",    1), ("other", 1), ("check", 0.5)], columns=["word", "so"])
df2 = pd.DataFrame([("test", 0.5), ("free",    1)],                               columns=["word", "so"])
los.simple(df1, df2), los.binary(df1, df2), los.score(df1, df2)