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Updated 4 years ago
Multi Class Classification Dataset for Turkish
Benchmark dataset for Turkish text classification
- It contians 430K lines, 32 categories
- Each category roughly has 13K comments
- Data is collected from Turkish web sites
- the data contains the comments of the products and product categories
- Baseline algoritm , Naive Bayes gets %86 F1 score as follows
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
/kaggle/input/multiclass-classification-data-for-turkish-tc32/ticaret-yorum.csv
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
import sklearn
import pandas as pd
df=pd.read_csv("/kaggle/input/multiclass-classification-data-for-turkish-tc32/ticaret-yorum.csv")
df=df.sample(df.shape[0])
df.head()
df.shape
(431306, 2)