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Python for conducting a literature review and Topic modelling

Python offers various libraries and tools that can be utilized for conducting a literature review and topic modeling, especially when working with datasets from sources like the Scopus database. Here's a step-by-step guide using Python and some popular libraries:

Data Preparation:

  • Load your dataset: Use a library like Pandas to read the .csv file and load the dataset into a DataFrame.
import pandas as pd
# Load dataset
df = pd.read_csv('your_dataset.csv')
  • Inspect the dataset: Understand the structure of your data, check for missing values, and explore the columns relevant to your literature review.

Text Preprocessing:

  • Tokenization: Split the text into individual words or tokens.
  • Stopword Removal: Remove common words (e.g., "the," "and," "is") that may not contribute much to the topic.
  • Lemmatization: Reduce words to their base or root form.
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Download NLTK resources (if not done before)
import nltk'punkt')'stopwords')'wordnet')

stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()

def preprocess_text(text):
    tokens = word_tokenize(text)
    tokens = [lemmatizer.lemmatize(token.lower()) for token in tokens if token.isalnum()]
    tokens = [token for token in tokens if token not in stop_words]
    return ' '.join(tokens)

# Apply preprocessing to your text column
df['preprocessed_text'] = df['your_text_column'].apply(preprocess_text)

Topic Modeling:

  • Choose a topic modeling algorithm. Latent Dirichlet Allocation (LDA) is a popular choice.
  • Use a library like Gensim or scikit-learn for topic modeling.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation

# Vectorize the preprocessed text
vectorizer = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')
dtm = vectorizer.fit_transform(df['preprocessed_text'])

# Apply LDA
lda_model = LatentDirichletAllocation(n_components=5, random_state=42)

# Display topics
for index, topic in enumerate(lda_model.components_):
    print(f"Top words for Topic #{index + 1}:")
    print([vectorizer.get_feature_names_out()[i] for i in topic.argsort()[-10:]])


  • Visualize the results using libraries like pyLDAvis or Matplotlib.
import pyLDAvis.sklearn
# Visualize the topics
panel = pyLDAvis.sklearn.prepare(lda_model, dtm, vectorizer, mds='tsne')

Iterate and Refine:

  • Adjust parameters such as the number of topics and experiment with different preprocessing steps to refine your topic modeling results.
Remember to install necessary libraries using tools like pip (pip install pandas nltk gensim scikit-learn pyLDAvis). Additionally, adapt the code according to your specific dataset and requirements.

Process Mining: Data science in Action

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