4.) a) Discuss the use of TF-IDF ?
b) Having 2000 articles, the word "Data" appears in 1500 articles, with frequency of 4000 times considering all articles. Find TF-IDF ?
a) TF-IDF (Term Frequency-Inverse Document Frequency) is a numerical statistic that reflects the importance of a term within a document or a collection of documents. It is commonly used in information retrieval and text mining to determine the relevance of a term in a specific document or a corpus.
TF (Term Frequency) measures the frequency of a term within a document. It is calculated by dividing the number of times a term appears in a document by the total number of terms in that document. TF gives higher weightage to more frequently occurring terms within a document.
IDF (Inverse Document Frequency) measures the significance of a term across a collection of documents. It is calculated by taking the logarithm of the ratio between the total number of documents in the collection and the number of documents that contain the term. IDF gives higher weightage to less frequently occurring terms across the collection.
TF-IDF is obtained by multiplying the TF and IDF values together for a specific term in a document. The resulting TF-IDF value increases as the term appears more frequently in the document, while also considering its rarity across the collection.
b) To find the TF-IDF for the word "Data" in the given scenario, we need the following information:
- Total number of articles: 2000
- Number of articles that contain the word "Data": 1500
- Total frequency of the word "Data" across all articles: 4000
First, we calculate the TF (Term Frequency):
TF = Frequency of the word "Data" in a specific article / Total number of terms in that article
Next, we calculate the IDF (Inverse Document Frequency):
IDF = log(Total number of articles / Number of articles that contain the word "Data")
Finally, we multiply the TF and IDF values to obtain the TF-IDF:
TF-IDF = TF * IDF
It is important to note that without further information on the term frequencies within each individual article and the total number of terms in each article, it is not possible to calculate the exact TF-IDF value for the word "Data" in this scenario.