How can I identify input variables for top modeling?
I am currently working on a machine learning project in which I need to analysis he customer reviews to identify key topics of interest. I am given a large dataset that contains thousands of reviews. The task is to extract keywords that would serve as input variables for further analysis. How can I identify and extract keywords to use them as input variables for topic 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)
Answers (2)
The yellow squares for letters in the incorrect location, dordle and green squares for letters in the right place make up the game's two Wordle grids that are side by side and function according to the usual format.
2 Days
To identify and extract keywords for topic modeling from customer reviews, you can follow these steps:
- Text Preprocessing: Clean the text data by removing punctuation, stop words, and performing lowercasing. Tokenize the text to split it into individual words or geometry dash phrases.
- Term Frequency-Inverse Document Frequency (TF-IDF): Use TF-IDF to transform the text data into a matrix of term frequencies. This method helps in identifying important words in the reviews by balancing the frequency of a word with its occurrence in different documents.
- N-grams: Extract n-grams (bigrams, trigrams) to capture more context around the keywords. For example, "customer service" as a bigram provides more meaningful information than individual words "customer" and "service".
- Named Entity Recognition (NER): Apply NER to identify specific entities such as product names, brands, or other significant terms in the reviews.
- Topic Modeling Algorithms: Use algorithms like Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF) to identify clusters of keywords that represent different topics.
- Key Phrase Extraction: Use techniques like RAKE (Rapid Automatic Keyword Extraction) or YAKE (Yet Another Keyword Extractor) to extract key phrases from the text.
- Word Embeddings: Utilize word embeddings like Word2Vec or GloVe to capture the semantic relationships between words, which can help in identifying relevant keywords for topic modeling.
4 Months
The yellow squares for letters in the incorrect location, dordle and green squares for letters in the right place make up the game's two Wordle grids that are side by side and function according to the usual format.
To identify and extract keywords for topic modeling from customer reviews, you can follow these steps:
- Text Preprocessing: Clean the text data by removing punctuation, stop words, and performing lowercasing. Tokenize the text to split it into individual words or geometry dash phrases.
- Term Frequency-Inverse Document Frequency (TF-IDF): Use TF-IDF to transform the text data into a matrix of term frequencies. This method helps in identifying important words in the reviews by balancing the frequency of a word with its occurrence in different documents.
- N-grams: Extract n-grams (bigrams, trigrams) to capture more context around the keywords. For example, "customer service" as a bigram provides more meaningful information than individual words "customer" and "service".
- Named Entity Recognition (NER): Apply NER to identify specific entities such as product names, brands, or other significant terms in the reviews.- Topic Modeling Algorithms: Use algorithms like Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF) to identify clusters of keywords that represent different topics.
- Key Phrase Extraction: Use techniques like RAKE (Rapid Automatic Keyword Extraction) or YAKE (Yet Another Keyword Extractor) to extract key phrases from the text.
- Word Embeddings: Utilize word embeddings like Word2Vec or GloVe to capture the semantic relationships between words, which can help in identifying relevant keywords for topic modeling.