Ever wondered how these search engines like Google and Yahoo work. And ever thought about how can they scan all through the internet and return relevant results in just About 5,43,00,000 results (0.004seconds). Well, they work on the concept of Crawling and Indexing.
- Crawling: Automated bots looks for pages that are new or updated. And stores the key information like — URL, title, keywords, and so on from the pages to be used later.
- Indexing: Data captured from crawling is analyzed like — what the page is about. Key content, images, and video files on the page are used in the process. This information is indexed and stored to be returned later for a search query.
Hence, whenever we asked them to search anything for us they are not scanning through the length and breadth of the internet but just scanning through those indexed URLs in step 2.
Well, today we would work on how to develop a small prototype, very similar to the indexing functionality of any search engine. We would be using a tweets dataset on #COVID and try to index them based on our search term.
A. Importing packages
import pandas as pd from rank_bm25 import *
What is BM25?
BM25 is a simple Python package and can be used to index the data, tweets in our case, based on the search query. It works on the concept of TF/IDF i.e.
- TF or Term Frequency — Simply put, indicates the number of occurrences of the search term in our tweet
- IDF or Inverse Document Frequency — It measures how important your search term is. Since TF considers all terms equally important, thus, we can’t only use term frequencies to calculate the weight of a term in your text. We would need to weigh down the frequent terms while scaling up the rare terms showing their relevancy to the tweet.
Once you run the query, BM25 will show the relevancy of your search term with each of the tweets. You can sort it to index the most relevant ones.
B. Preparing your tweets
Since this is not a discussion on Twitter API, will start using an excel based feed. You can clean your text data on these key steps to make the search more robust.
Splitting the sentence into words. So that each word can be considered uniquely.
from nltk.tokenize import word_tokenize sentence = “Jack is a sharp minded fellow” words = word_tokenize(sentence) print(words)
Output: ['Jack', 'sharp', 'minded', 'fellow']
2. Removing special characters:
Removing the special characters from your tweets
def spl_chars_removal(lst): lst1=list() for element in lst: str=”” str = re.sub(“[⁰-9a-zA-Z]”,” “,element) lst1.append(str) return lst1
3. Removing stop words:
Stop words are commonly used words (is, for, the, etc.) in the tweets. These words do not signify any importance as they do not help in distinguishing two tweets. I used Gensim package to remove my stopwords, you can also try it using nltk, but I found Gensim much faster than others.
One can also easily add new words to the stop words list, in case your data is particularly surrounded with those words and is frequently occurring.
#adding words to stopwords from nltk.tokenize import word_tokenize from gensim.parsing.preprocessing import STOPWORDS
#adding custom words to the pre-defined stop words list all_stopwords_gensim = STOPWORDS.union(set([‘disease’]))
def stopwprds_removal_gensim_custom(lst): lst1=list() for str in lst: text_tokens = word_tokenize(str) tokens_without_sw = [word for word in text_tokens if not word in all_stopwords_gensim] str_t = “ “.join(tokens_without_sw) lst1.append(str_t) return lst1
Text normalization is the process of transforming a text into a canonical (standard) form. For example, the word “gooood” and “gud” can be transformed to “good”, its canonical form. Another example is mapping of near-identical words such as “stopwords”, “stop-words” and “stop words” to just “stopwords”.
This technique is important for noisy texts such as social media comments, text messages, and comments to blog posts where abbreviations, misspellings, and use of out-of-vocabulary words (oov) are prevalent. People tend to write comments in short-hand and hence this pre-processing becomes very important.
|tomo, 2moro, 2mrw, tmrw||tomorrow|
|brb||be right back|
Process of transforming the words to their root form. It’s the process of reducing inflection in words (e.g. troubled, troubles) to their root form (e.g. trouble). The “root” in this case may not be a real root word, but just a canonical form of the original word.
Stemming uses a heuristic process that chops off the ends of words in the hope of correctly transforming words into their root form. It needs to be reviewed as in the below example you can see “Machine” gets transformed to “Machin”, “e” is chopped off in the stemming process.
import nltk from nltk.stem import PorterStemmer ps = PorterStemmer() sentence = “Machine Learning is cool” for word in sentence.split(): print(ps.stem(word))
C. Tokenizing tweets and running BM25
This is the central piece where we run the query for search. We search the tweets based on the word “vaccine” user-based. One can enter a phrase too and it will fluently as we tokenize our search term in the 2nd line below.
You can check the association of each tweet with your search term using .get_scores function.
doc_scores = bm25.get_scores(tokenized_query) print(doc_scores)
As we enter n=5 in .get_top_n we would get five most associated tweets as our result. You can put the value of n according to your needs.
D. Top Five associated Tweets
|Top 5 Tweets||Tweeted By|
|@MikeCarlton01 Re #ABC funding, looked up Budget Papers. After massive prior cuts, it got extra $4.7M in funding (.00044% far less than inflation).
#Morrison wastes $Ms on over-priced & ineffective services eg useless #Covid app.; delivery vaccine #agedcare; consultancies vaccine roll-out..
|@TonyHWindsor @barriecassidy @4corners @abc730 For its invaluable work, #ABC got extra $4.7M in funding (.00044% far less than inflation).
While #Morrison Govt spends like drunken sailor on buying over-priced & ineffective services from mates (eg useless #Covid app.; delivery vaccine #agedcare; vaccine roll-out) #auspol
|It’s going to be a month after my #Covid recovery. Now I will go vaccine 😎😎😎😎||Simi Elizabeth😃|
|RT @pradeepkishan : What a despicable politician is #ArvindKejariwal ! The minute oxygen hoarding came to light his propaganda shifted to vaccine shortage. He is more dangerous than #COVID itself! @BJP4India @TajinderBagga||p.hariharan|
|RT @AlexBerenson : TL: DR – In the @pfizer teen #Covid vaccine trial, 4 or 5 (the exact figure is hidden) of 1,100 kids who got the vaccine had serious side effects, compared to 1 who got placebo.
@US_FDA did not disclose specifics, so we have no idea what they were or if they follow any pattern. https://t.co/n5igf2xXFN
E. Additional use cases of BM25
There can be many use cases where a search feature is required. One of the most relevant ones is around parsing the PDF and developing a search function over the PDF content.
This is one of the widely used cases for BM25. As the globe slowly shifts to better data strategy and efficient storage techniques, the old PDF documents can be retrieved efficiently using algorithms like BM25.
Hope you enjoyed reading this and find this helpful. Thank you, folks!
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