Import the necessary libraries: I will need to import libraries such as pandas, numpy, nltk, and spaCy to perform various text analysis tasks.
Load the CSV file: I will use the pandas library to load the CSV file into a dataframe.
Preprocessing: I will preprocess the text data by removing punctuations, converting all text to lowercase, and removing stop words.
Tokenization: I will tokenize the text data into individual words or phrases.
Remove job titles and company names: I will remove job titles and company names from the text data to focus on the job descriptions and requirements.
Lemmatization: I will use the nltk library to lemmatize the words in the text data, which will help to reduce words to their base or dictionary form.
Vectorization: I will use the CountVectorizer or TfidfVectorizer from the scikit-learn library to convert the text data into numerical vectors.
Clustering: I will use clustering algorithms such as K-Means or DBSCAN to group similar job listings together based on their text content.
Visualization: I will use visualization tools such as matplotlib or seaborn to visualize the clusters of job listings and identify trends or patterns in the job market.
Summarization: I will use summarization techniques such as summarization2 to summarize the job listings in each cluster, highlighting the key job requirements and responsibilities.
