Implementing feature selection methods on text classification

The size of variables or features is referred to as the dimensionality of a dataset. On text classification methods, the size of features could be enumerated a large number. In this post, we are going to implement tf-idf decomposition dimensionality reduction technique using Linear Discriminant Analysis-LDA.

Our pathway in this study:

1. Preparing Dataset

2. Transforming text to feature vectors

3. Applying filter methods

4. Applying Linear Discriminant Analysis

5. Building a Random Forest Classifier

6. Result comparison

All source codes and notebooks have been uploaded in this Github repository.

Problem Formulation

Enhancing the accuracy and reducing process time in text classification.

Data Exploration

We are using the “515K Hotel Reviews Data in Europe” from the Kaggle datasets. The data was scraped from All data in the file is publicly available to everyone already. Data is originally owned by and you can download it thought this profile on Kaggle. The dataset which we need contains 515,000 positive and negative reviews.

Import libraries

The most important libraries that we have used are Scikit-Learn and pandas.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import requests
import json
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split 
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import VarianceThreshold
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.preprocessing import StandardScaler
from nltk.stem.snowball import SnowballStemmer
from string import punctuation
from textblob import TextBlob
import re

Preparing Dataset

We are going to work just on two categories, positives and negatives. Therefore, we select 5,000 rows for each category and copy them into the Pandas Dataframe (5,000 for each part). We used Kaggle’s notebook for this project, therefore the dataset was loaded as a local file. If you are using another tool or running as a script you can download it. let’s take a glance at the dataset:

fields = ['Positive_Review', 'Negative_Review']
df = pd.read_csv(
    usecols= fields, nrows=5000)

Image for post

Stemming words using NLTK SnowballStemmer:

stemmer = SnowballStemmer('english')
df['Positive_Review'] = df['Positive_Review'].apply(
    lambda x:' '.join([stemmer.stem(y) for y in x.split()]))

df['Negative_Review'] = df['Negative_Review'].apply(
    lambda x: ' '.join([stemmer.stem(y) for y in x.split()]))

Removing Stop-Words:

Exclude stopwords with list comprehension and pandas.DataFrame.apply.

url = ""
response = pd.DataFrame(data = json.loads(requests.get(url).text))
SW = list(response['words'])

df['Positive_Review'] = df['Positive_Review'].apply(
    lambda x: ' '.join([word for word in x.split() if word not in (SW)]))
df['Negative_Review'] = df['Negative_Review'].apply(
    lambda x: ' '.join([word for word in x.split() if word not in (SW)]))

#feature-selection #lda #machine-learning #scikit-learn #text-classification #deep learning

Higher accuracy and less process time in text classification
1.45 GEEK