How can I identify input variables for top modeling?

 In the context of data science, you can identify and extract keywords from customer reviews for using it as input variables in topic modeling by using the following steps:

Data preprocessing

First, try to split the text into individual worlds or even Tokena.

Now you can remove the common words that do not contribute to the keyword significance such as and, the etc.

Now you should try to reduce the words to their base or root form.

You can now filter out non-alphabetic tokens.

Import nltk

From nltk.tokenize import word_tokenize

From nltk.corpus import stopwords

From nltk.stem import PorterStemmer

# Ensure required NLTK resources are downloaded

Nltk.download(‘punkt’)

Nltk.download(‘stopwords’)

# Sample review text

Review_text = “This is an example review text! It includes various words and some non-alphabetic characters. Let’s clean it up!”

# Tokenization

Tokens = word_tokenize(review_text.lower())

# Stop words removal

Stop_words = set(stopwords.words(‘english’))

Tokens = [word for word in tokens if word not in stop_words]

# Stemming

Stemmer = PorterStemmer()

Tokens = [stemmer.stem(word) for word in tokens]

# Removing non-alphabetic characters

Tokens = [word for word in tokens if word.isalpha()]

# Output the processed tokens

Print(tokens)

Keyword extraction

You can calculate the TF-IDF scores to find the important words.

You can also select the most frequent terms which have been used.

From sklearn.feature_extraction.text import TfidfVectorizer

From collections import Counter

# Example list of review texts

Reviews = [

    “This product is excellent and very useful.”,

    “I found the product to be quite satisfactory and efficient.”,

    “Not satisfied with the product, it didn’t meet my expectations.”,

    “The product quality is amazing, highly recommended!”,

    “I have some issues with the product, needs improvement.”

]

# Initialize TF-IDF Vectorizer

Vectorizer = TfidfVectorizer(max_df=0.85, max_features=1000)

Tfidf_matrix = vectorizer.fit_transform(reviews)

Keywords = vectorizer.get_feature_names_out()

# Output the extracted keywords based on TF-IDF

Print(“Keywords from TF-IDF:”)

Print(keywords)

# Combine all reviews into a single string for frequency analysis

All_tokens = ‘ ‘.join(reviews).lower()

# Tokenize the combined text

All_tokens = word_tokenize(all_tokens)

# Remove stop words and non-alphabetic tokens for frequency analysis

All_tokens = [word for word in all_tokens if word not in stop_words and word.isalpha()]

# Stemming for frequency analysis

All_tokens = [stemmer.stem(word) for word in all_tokens]

# Frequency-based keyword selection

Word_freq = Counter(all_tokens)

Common_keywords = [word for word, freq in word_freq.most_common(100)]

# Output the most frequent keywords

Print(“

Most Frequent Keywords:”)

Print(common_keywords)

Validation and refinement

You should try to Verify that the selected keywords are contextually relevant to the themes. 

You can now further move to filter the keywords that are based on the knowledge of the domain.

You can also conduct a Manual review to ensure quality.

# Example criteria for contextual validation and domain filtering

Context_criteria = lambda word: len(word) > 3  # Example: keyword must be longer than 3 characters

Domain_criteria = lambda word: word not in [‘issue’, ‘found’]  # Example: exclude certain words

# Combine keywords from Step 2 for further validation and refinement

Extracted_keywords = set(keywords).union(set(common_keywords))

# Contextual Validation: filter keywords based on context criteria

Contextually_relevant_keywords = [word for word in extracted_keywords if context_criteria(word)]

# Domain-Specific Filtering: further filter keywords based on domain knowledge

Domain_specific_keywords = [word for word in contextually_relevant_keywords if domain_criteria(word)]

# Manual Review: Example of manual review by simply printing the keywords

# In practice, this step might involve more thorough examination by a domain expert

Final_keywords = domain_specific_keywords

Print(“

Keywords After Manual Review:”)

Print(final_keywords)

# Output the final refined keywords

Print(“

Final Refined Keywords:”)

Print(final_keywords)